Intelligent remote monitoring system for mass flow meter

Abstract

The invention discloses an intelligent remote monitoring system for a mass flow meter, which relates to the technical field of flow meter monitoring, and is characterized in that multiple data related to the monitoring precision of the flow meter are regularly acquired through the monitoring system, and after the multiple data are preprocessed, abnormal state factors are generated for the flow meter; the method comprises the following steps of: acquiring environmental data influencing the monitoring precision of the flowmeter in an environment, performing linear regression analysis on the environmental data through a regression analysis model, generating an environmental influence factor, substituting the environmental influence factor and an abnormal state factor into a fusion model for comprehensive evaluation, and judging whether the flow monitoring range of the flowmeter needs to be dynamically regulated or not. And a corresponding regulation and control strategy is generated based on a judgment result. According to the monitoring system, the mass flow meter is subjected to anomaly analysis, the environment condition of the mass flow meter is analyzed, the flow monitoring range of the mass flow meter is dynamically regulated and controlled in combination with the analysis result, and stable transportation of crude oil is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of flow meter monitoring, and in particular to an intelligent remote monitoring system for a mass flow meter. Background Art

[0002] In the field of crude oil pipeline transportation, mass flow meters, as an important metering equipment, are widely used in the measurement, verification and monitoring of oil products. Their main function is to accurately calculate the transmission volume of oil products by measuring the mass flow rate of the fluid flowing through the pipeline and combining parameters such as temperature and pressure. The remote monitoring system can realize real-time monitoring, data collection and analysis of the operating status of the mass flow meter, and support remote operation and fault warning, which can comprehensively improve the efficiency and safety of crude oil pipeline transportation, while meeting the high-precision requirements of oil product measurement and verification.

[0003] The prior art has the following defects: Since mass flow meters are mainly used for crude oil flow monitoring and early warning, the existing technology mainly sets a flow monitoring range for the mass flow meter, and alarms when the crude oil flow is not within the flow monitoring range. However, when the mass flow meter itself has an abnormality or is disturbed by the environment, the accuracy of crude oil flow monitoring may be reduced, and the monitoring system has no effective monitoring and control over such problems. If the crude oil flow deviates from the flow monitoring range all the time during transportation, excessive flow for a long time may cause the pipeline, valves and mass flow meters to be in a high-pressure, high-load state for a long time, increasing the risk of wear and fatigue damage, and even causing equipment damage or leakage accidents. If the flow is too small for a long time, the kinetic energy of fluid transportation is insufficient, oil deposition or pipeline blockage may occur, further affecting the transportation efficiency.

[0004] Based on this, the present invention proposes an intelligent remote monitoring system for a mass flow meter, which performs abnormal analysis on the mass flow meter and the environmental conditions of the mass flow meter, and dynamically adjusts the flow monitoring range of the mass flow meter based on the analysis results to ensure the stable transportation of crude oil. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent remote monitoring system for a mass flow meter to solve the deficiencies in the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent remote monitoring system for a mass flow meter, comprising an abnormal state analysis module, an environmental analysis module, a fusion evaluation module, and an interpolation estimation module; Abnormal state analysis module: obtains flow meter information from the management platform through the API interface. During the crude oil transportation process, it regularly obtains multiple data related to the flow meter monitoring accuracy. After pre-processing the multiple data, it generates an abnormal state factor for the flow meter. Environmental analysis module: obtains environmental data that affects the flow meter monitoring accuracy, performs linear regression analysis on the environmental data through a regression analysis model, and generates environmental impact factors; Fusion evaluation module: Substitute environmental impact factors and abnormal state factors into the fusion model for comprehensive evaluation to determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter, and generate corresponding control strategies based on the judgment results; Interpolation estimation module: When a flow meter issues a warning signal based on the flow monitoring range after dynamic regulation, the flow meter that issues the warning signal is interpolated and estimated in combination with the operating results of other flow meters in the crude oil pipeline, and the interpolation result is sent to the management platform.

[0007] Preferably, after the fusion evaluation module obtains the environmental impact factor and the abnormal state factor, the environmental impact factor and the abnormal state factor are substituted into the fusion model, and the fusion model outputs the overall performance coefficient of the flow meter. The fusion model expression is: , where is the environmental impact factor, is the abnormal status factor, is the overall performance coefficient; The obtained overall performance coefficient is compared with a preset performance threshold, and the performance threshold is used to determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter; If the overall performance coefficient is less than or equal to the performance threshold, it is determined that there is no need to dynamically adjust the flow monitoring range of the flow meter. If the overall performance coefficient is greater than the performance threshold, it is determined that there is a need to dynamically adjust the flow monitoring range of the flow meter.

[0008] Preferably, the environmental analysis module periodically obtains multiple data related to the flow meter monitoring accuracy, including electromagnetic interference, electrostatic aggregation amplitude, and pipeline vibration amplitude, and substitutes the electromagnetic interference, electrostatic aggregation amplitude, and pipeline vibration amplitude into a regression analysis model to generate an environmental impact factor for the flow meter. The regression analysis model expression is: , where is the environmental impact factor, They are electromagnetic interference, electrostatic aggregation amplitude and pipeline vibration amplitude. is the regression coefficient, and the regression coefficient is greater than 0.

[0009] Preferably, the abnormal state analysis module periodically obtains multiple data related to the flow meter monitoring accuracy, the multiple data including the sensitivity decay rate and the error accumulation rate, normalizes the sensitivity decay rate and the error accumulation rate, maps the value range of the sensitivity decay rate and the error accumulation rate to [0,1], obtains the normalized value of the sensitivity decay rate and the normalized value of the error accumulation rate, and sums the normalized value of the sensitivity decay rate and the normalized value of the error accumulation rate to obtain the abnormal state factor.

[0010] Preferably, when it is determined that the flow monitoring range of the flow meter needs to be dynamically adjusted, the fusion evaluation module dynamically adjusts the flow monitoring range based on the overall performance coefficient, and the adjustment algorithm is: , where is the initial flow monitoring range of the flow meter, is the flow monitoring range after dynamic regulation. It is the overall performance coefficient, that is, the flow monitoring range of the flow meter is narrowed by the overall performance coefficient, thereby improving the monitoring intensity.

[0011] Preferably, the flow meter monitors the crude oil flow in the pipeline in real time. When the actual crude oil flow is not within the flow monitoring range after dynamic regulation, a warning signal is sent to the management platform. The interpolation estimation module marks the flow meter that sends the warning signal as a warning flow meter, and obtains information of all flow meters on the current pipeline; Calculate the weight of each flow meter in the pipeline relative to the warning flow meter. The calculation logic is: obtain the spatial distance between the flow meter and the warning flow meter, as well as the historical flow similarity between the flow meter and the warning flow meter, normalize the spatial distance and the historical flow similarity, sum the normalized spatial distance and the historical flow similarity to obtain the flow meter impact value, take the inverse of the impact value as the importance assignment, sum the importance assignments of all flow meters to obtain the assignment sum, and divide the importance assignment by the assignment sum to obtain the weight of each flow meter relative to the warning flow meter; The actual flow rate monitored by each flow meter is obtained, and the actual flow rates of all flow meters are weighted to obtain the estimated flow rate of the warning flow meter. The expression is: , where To warn the flow meter flow estimation, is the number of flow meters, For the The weight of each flow meter, For the The actual flow of each flow meter is detected, and the estimated flow of the warning flow meter is sent to the management platform.

[0012] Preferably, the calculation expression of the sensitivity attenuation rate is: , where is the sensitivity attenuation rate, is the actual sensitivity of the flow meter probe at time instant, is the standard sensitivity of the flow meter probe at time instant, To monitor the time period; The calculation expression of the error accumulation rate is: , where is the error accumulation rate, is the flow meter usage time, For the estimated service life of the flow meter, is the adjustment coefficient, the value is 1.236, is the ambient temperature change, is the nominal temperature range.

[0013] Preferably, the pipeline vibration amplitude is obtained online by a vibration sensor arranged on the pipeline, and the calculation expression of the electromagnetic interference degree is: , where is the measured noise signal voltage, As the reference signal, the calculation expression of the electrostatic aggregation amplitude is: , where is the electrostatic surface charge density, is the surface area of ​​the flow meter monitoring area, is the distance of the electrostatic field.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention obtains multiple data related to the flow meter monitoring accuracy through the monitoring system at regular intervals, generates abnormal state factors for the flow meter after preprocessing the multiple data, and obtains environmental data in the environment that affects the flow meter monitoring accuracy. After performing linear regression analysis on the environmental data through the regression analysis model, an environmental impact factor is generated, and the environmental impact factor and the abnormal state factor are substituted into the fusion model for comprehensive evaluation to determine whether it is necessary to dynamically control the flow monitoring range of the flow meter, and generate a corresponding control strategy based on the judgment result. The monitoring system performs abnormal analysis on the mass flow meter and analyzes the environmental conditions of the mass flow meter, and dynamically controls the flow monitoring range of the mass flow meter in combination with the analysis results to ensure the stable transportation of crude oil.

[0015] 2. In the present invention, when a certain flow meter sends out a warning signal based on the flow monitoring range after dynamic regulation, the monitoring system interpolates and estimates the flow meter that sends out the warning signal in combination with the operating results of other flow meters in the crude oil pipeline, and sends the interpolation result to the management platform, thereby being able to estimate the flow of the flow meter with the abnormality, making it easier for the management platform to understand the actual pipeline flow situation, which is conducive to improving management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 As shown, the intelligent remote monitoring system for a mass flow meter described in this embodiment includes an abnormal state analysis module, an environmental analysis module, a fusion evaluation module, and an interpolation estimation module; Abnormal state analysis module: obtains flow meter information from the management platform through the API interface. During the crude oil transportation process, it regularly obtains multiple data related to the flow meter monitoring accuracy. After pre-processing the multiple data, it generates an abnormal state factor for the flow meter, which is sent to the fusion evaluation module. Environmental analysis module: obtains environmental data that affects the flow meter monitoring accuracy in the environment, performs linear regression analysis on the environmental data through a regression analysis model, generates environmental impact factors, and sends the environmental impact factors to the fusion evaluation module; Fusion evaluation module: Substitute environmental impact factors and abnormal state factors into the fusion model for comprehensive evaluation, determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter, and generate corresponding control strategies based on the judgment results. The dynamic control results of the flow monitoring range of the flow meter are sent to the interpolation estimation module; Interpolation estimation module: When a flow meter issues a warning signal based on the flow monitoring range after dynamic regulation, the flow meter that issues the warning signal is interpolated and estimated in combination with the operating results of other flow meters in the crude oil pipeline, and the interpolation result is sent to the management platform.

[0020] This application uses a monitoring system to periodically obtain multiple data related to the flow meter monitoring accuracy, and after preprocessing the multiple data, generates an abnormal state factor for the flow meter, and obtains environmental data in the environment that affects the flow meter monitoring accuracy. After performing linear regression analysis on the environmental data through a regression analysis model, an environmental impact factor is generated, and the environmental impact factor and the abnormal state factor are substituted into the fusion model for comprehensive evaluation to determine whether it is necessary to dynamically control the flow monitoring range of the flow meter, and generate a corresponding control strategy based on the judgment result. The monitoring system performs abnormal analysis on the mass flow meter and analyzes the environmental conditions of the mass flow meter, and combines the analysis results to dynamically control the flow monitoring range of the mass flow meter to ensure the stable transportation of crude oil.

[0021] In the present application, when a certain flow meter issues a warning signal based on the flow monitoring range after dynamic regulation, the monitoring system interpolates and estimates the flow meter that issues the warning signal in combination with the operating results of other flow meters in the crude oil pipeline, and sends the interpolation result to the management platform, thereby being able to estimate the flow of the flow meter with the abnormality, making it easier for the management platform to understand the actual pipeline flow situation, which is conducive to improving management efficiency.

[0022] The specific workflow of the monitoring system is as follows: The monitoring system obtains flow meter information from the management platform through the API interface. During the crude oil transportation process, the monitoring system regularly obtains multiple data related to the flow meter monitoring accuracy. After preprocessing the multiple data, it generates an abnormal state factor for the flow meter and obtains environmental data in the environment that affects the flow meter monitoring accuracy. After performing linear regression analysis on the environmental data through the regression analysis model, an environmental impact factor is generated. The environmental impact factor and the abnormal state factor are substituted into the fusion model for comprehensive evaluation to determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter, and generate a corresponding control strategy based on the judgment result. When a flow meter issues a warning signal based on the flow monitoring range after dynamic adjustment, the monitoring system combines the operating results of other flow meters in the crude oil pipeline to interpolate and estimate the flow meter that issued the warning signal, and sends the interpolation result to the management platform.

[0023] Embodiment 2: The abnormal state analysis module obtains flow meter information from the management platform through the API interface, and during the crude oil transportation process, regularly obtains multiple data related to the flow meter monitoring accuracy, and generates an abnormal state factor for the flow meter after preprocessing the multiple data; The abnormal status analysis module initiates a connection request to the management platform through the API interface to check network connectivity and interface availability. Use pre-configured API keys, tokens, or OAuth authentication mechanisms to ensure the security and legitimacy of interface calls. Set API parameters according to the call requirements, such as the flow meter's device ID, data type, time range, etc.

[0024] Obtain a list of all or a specified range of flow meter information on the management platform through the API, including basic information such as device ID, name, and location. Request the management platform to provide real-time monitoring data of the flow meter, including flow value, temperature, pressure, operating status, etc. Obtain historical operating data of the flow meter according to the specified time range to provide more background information for abnormal analysis.

[0025] Verify the integrity of the data obtained from the API, check whether key fields (such as timestamps and traffic values) are missing, and preliminarily remove erroneous data that is obviously illogical or illogical to avoid affecting subsequent analysis. Unify the obtained data into standardized formats and units to facilitate subsequent algorithm processing and analysis.

[0026] The abnormal state analysis module obtains flow meter information from the management platform through the API interface. During the crude oil transportation process, it regularly obtains multiple data related to the flow meter monitoring accuracy. After pre-processing the multiple data, it generates an abnormal state factor for the flow meter.

[0027] The abnormal state analysis module periodically obtains multiple data related to the flow meter monitoring accuracy, including the sensitivity decay rate and the error accumulation rate, normalizes the sensitivity decay rate and the error accumulation rate, maps the value range of the sensitivity decay rate and the error accumulation rate to [0,1], obtains the normalized value of the sensitivity decay rate and the normalized value of the error accumulation rate, and sums the normalized value of the sensitivity decay rate and the normalized value of the error accumulation rate to obtain the abnormal state factor.

[0028] The larger the abnormal state factor is, the greater the probability that the monitoring accuracy of the flow meter will decrease.

[0029] The calculation expression of sensitivity attenuation rate is: , where is the sensitivity attenuation rate, is the actual sensitivity of the flow meter probe at time instant, is the standard sensitivity of the flow meter probe at time instant, For the monitoring time period, the greater the sensitivity decay rate, the greater the decrease in the monitoring accuracy of the probe.

[0030] The calculation expression of error accumulation rate is: , where is the error accumulation rate, is the flow meter usage time, For the estimated service life of the flow meter, is the adjustment coefficient, the value is 1.236, is the ambient temperature change, For the nominal temperature range, the larger the error accumulation rate, the more serious the aging of the flow meter, and the greater the reduction in monitoring accuracy.

[0031] The environmental analysis module obtains environmental data that affects the flow meter monitoring accuracy in the environment, and generates environmental impact factors after performing linear regression analysis on the environmental data through a regression analysis model; The environmental analysis module obtains multiple data related to the flow meter monitoring accuracy at regular intervals, including electromagnetic interference, electrostatic aggregation amplitude, and pipeline vibration amplitude. The electromagnetic interference, electrostatic aggregation amplitude, and pipeline vibration amplitude are substituted into the regression analysis model to generate environmental impact factors for the flow meter. The regression analysis model expression is: , where is the environmental impact factor, They are electromagnetic interference, electrostatic aggregation amplitude and pipeline vibration amplitude. is the regression coefficient, and the regression coefficient is greater than 0; The pipeline vibration amplitude is obtained online through the vibration sensor installed on the pipeline, and the calculation expression of electromagnetic interference degree is: , where is the measured noise signal voltage, As the reference signal, the sensor and signal processing unit of the mass flow meter may be interfered by nearby power equipment or high-frequency signals, affecting the stability of the signal, thereby reducing the monitoring accuracy of the mass flow meter. The calculation expression of the electrostatic aggregation amplitude is: , where is the electrostatic surface charge density, is the surface area of ​​the flow meter monitoring area, The distance of the electrostatic field. The movement of pipeline fluid may cause static electricity accumulation. The larger the amplitude of static electricity aggregation, the easier it is to interfere with the electrical signal processing of the flow meter, thereby reducing the monitoring accuracy of the flow meter. In the calculation related to the electrostatic field, the distance of the electrostatic field usually refers to the distance between two points. In this application, it refers to the distance between the flow meter probe and the detected electrostatic field. The smaller the distance, the closer the flow meter probe is to the electrostatic field, and the greater the impact.

[0032] The logical factors of the environmental impact factor when used in the present invention are as follows: taking the influence of environmental impact data on the monitoring accuracy of the flow meter as an example, the first is the indicator, that is, the factor that causes the change in the monitoring accuracy of the flow meter (the present invention refers to the influence of environmental impact data on the monitoring accuracy of the flow meter); the second is the weight of these indicators, that is, the proportion of each major influencing data when it is generated; the third is the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the environmental impact factor is obtained by calculating the indicators with their respective weights through the operation equation.

[0033] The main influencing data obtained from the sample were transformed and processed into a data language recognized by computer software; secondly, these evaluation factors were analyzed by Logistic regression using SPSS software to screen out factors and their weights that are significantly correlated with the results; thirdly, the evaluation factors and weights were substituted into the Logistic regression equation for calculation to obtain the results, which are as follows: First, ensure the integrity of the main effect data, handle missing values ​​and outliers, and convert the data into a format that SPSS software can recognize. Usually, the data is stored in csv, xlsx and other formats, and then imported into SPSS. Open SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (outcome) and the independent variable (main effect data) to the corresponding boxes. SPSS will fit the Logistic regression based on the selected variables. Model, in the output results, you will see the coefficients, standard errors, p-values ​​and other information of the model. Check the coefficients and p-values ​​in the output results to determine which variables have a significant correlation with the results. Usually, a p-value less than 0.05 is considered significant. While fitting the model, use variable selection methods, such as stepwise regression, to help screen the most relevant factors. According to the coefficients of the Logistic regression model, the size of the coefficient reflects the degree of influence of each factor on the result, and the positive and negative signs of the coefficients indicate the direction of the influence. After obtaining the significant factors and their coefficients, the Logistic regression equation is obtained, which is used to calculate the probability of each sample and then predict the results.

[0034] The fusion evaluation module substitutes the environmental impact factors and abnormal state factors into the fusion model for comprehensive evaluation, determines whether it is necessary to dynamically adjust the flow monitoring range of the flow meter, and generates corresponding control strategies based on the judgment results; After the fusion evaluation module obtains the environmental impact factors and abnormal state factors, it substitutes the environmental impact factors and abnormal state factors into the fusion model. The fusion model outputs the overall performance coefficient of the flow meter. The fusion model expression is: , where is the environmental impact factor, is the abnormal status factor, is the overall performance coefficient; The larger the overall performance coefficient, the more serious the decline in the overall monitoring accuracy of the flow meter, and the more the flow monitoring range of the flow meter should be dynamically adjusted. The obtained overall performance coefficient is compared with the preset performance threshold. The performance threshold is used to determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter. If the overall performance coefficient is less than or equal to the performance threshold, it is determined that there is no need to dynamically adjust the flow monitoring range of the flow meter. If the overall performance coefficient is greater than the performance threshold, it is determined that the flow monitoring range of the flow meter needs to be dynamically adjusted.

[0035] When it is determined that the flow monitoring range of the flow meter needs to be dynamically adjusted, the fusion evaluation module dynamically adjusts the flow monitoring range based on the overall performance coefficient, and the adjustment algorithm is: , where is the initial flow monitoring range of the flow meter, is the flow monitoring range after dynamic regulation. It is the overall performance coefficient, that is, the flow monitoring range of the flow meter is narrowed by the overall performance coefficient, thereby improving the monitoring intensity.

[0036] When a flow meter issues a warning signal based on the flow monitoring range after dynamic regulation, the interpolation estimation module interpolates and estimates the flow meter that issues the warning signal based on the operation results of other flow meters in the crude oil pipeline, and sends the interpolation result to the management platform; The flow meter monitors the crude oil flow in the pipeline in real time. When the actual crude oil flow is not within the flow monitoring range after dynamic regulation, a warning signal is sent to the management platform. The interpolation estimation module marks the flow meter that sends the warning signal as a warning flow meter and obtains information on all flow meters on the current pipeline. Calculate the weight of each flow meter in the pipeline relative to the warning flow meter. The calculation logic is: obtain the spatial distance between the flow meter and the warning flow meter, as well as the historical flow similarity between the flow meter and the warning flow meter, normalize the spatial distance and the historical flow similarity, sum the normalized spatial distance and the historical flow similarity to obtain the flow meter impact value, take the inverse of the impact value as the importance assignment, sum the importance assignments of all flow meters to obtain the assignment sum, and divide the importance assignment by the assignment sum to obtain the weight of each flow meter relative to the warning flow meter; The actual flow rate monitored by each flow meter is obtained, and the actual flow rates of all flow meters are weighted to obtain the estimated flow rate of the warning flow meter. The expression is: , where To warn the flow meter flow estimation, is the number of flow meters, For the The weight of each flow meter, For the The actual flow of each flow meter is detected, and the estimated flow of the warning flow meter is sent to the management platform.

[0037] The spatial distance is obtained by general Euclidean distance calculation, that is, the spatial coordinates of the warning flow meter and the spatial coordinates of each flow meter are obtained and then calculated. This calculation method belongs to the prior art and will not be repeated in this application. The smaller the spatial distance, the closer the distance between the flow meter and the warning flow meter, that is, the greater the weight of the flow meter.

[0038] The calculation logic of historical flow similarity is: obtain multiple flow values ​​obtained by the warning flow meter and multiple flow values ​​obtained by other flow meters at multiple historical time points, subtract the flow value of the warning flow meter at each time point from the flow value of other flow meters to obtain the flow difference, and sum the flow differences at multiple time points to obtain the historical flow similarity. The smaller the historical flow similarity, the more similar the historical flow of the flow meter is to the warning flow meter, that is, the greater the weight of the flow meter.

[0039] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0040] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0041] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent remote monitoring system for mass flowmeter, characterized in that: It includes abnormal state analysis module, environmental analysis module, fusion evaluation module and interpolation estimation module; Abnormal state analysis module: obtains flow meter information from the management platform through the API interface. During the crude oil transportation process, it regularly obtains multiple data related to the flow meter monitoring accuracy. After pre-processing the multiple data, it generates an abnormal state factor for the flow meter. Environmental analysis module: obtains environmental data that affects the flow meter monitoring accuracy, performs linear regression analysis on the environmental data through a regression analysis model, and generates environmental impact factors; Fusion evaluation module: Substitute environmental impact factors and abnormal state factors into the fusion model for comprehensive evaluation to determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter, and generate corresponding control strategies based on the judgment results; Interpolation estimation module: When a flow meter issues a warning signal based on the flow monitoring range after dynamic regulation, the flow meter that issues the warning signal is interpolated and estimated in combination with the operating results of other flow meters in the crude oil pipeline, and the interpolation result is sent to the management platform.

2. The intelligent remote monitoring system for mass flowmeter according to claim 1, characterized in that: After the fusion evaluation module obtains the environmental impact factor and the abnormal state factor, the environmental impact factor and the abnormal state factor are substituted into the fusion model. The fusion model outputs the overall performance coefficient of the flow meter. The fusion model expression is: , where is the environmental impact factor, is the abnormal status factor, is the overall performance coefficient; The obtained overall performance coefficient is compared with a preset performance threshold, and the performance threshold is used to determine whether it is necessary to dynamically adjust the flow monitoring range of the flow meter; If the overall performance coefficient is less than or equal to the performance threshold, it is determined that there is no need to dynamically adjust the flow monitoring range of the flow meter. If the overall performance coefficient is greater than the performance threshold, it is determined that there is a need to dynamically adjust the flow monitoring range of the flow meter.

3. The intelligent remote monitoring system for mass flowmeter according to claim 2 is characterized in that: The environmental analysis module obtains multiple data related to the flow meter monitoring accuracy at regular intervals, including electromagnetic interference, electrostatic aggregation amplitude, and pipeline vibration amplitude, and substitutes the electromagnetic interference, electrostatic aggregation amplitude, and pipeline vibration amplitude into the regression analysis model to generate an environmental impact factor for the flow meter. The regression analysis model expression is: , where is the environmental impact factor, They are electromagnetic interference, electrostatic aggregation amplitude and pipeline vibration amplitude. is the regression coefficient, and the regression coefficient is greater than 0.

4. The intelligent remote monitoring system for mass flowmeter according to claim 3 is characterized in that: The abnormal state analysis module periodically obtains multiple data related to the flow meter monitoring accuracy, including the sensitivity decay rate and the error accumulation rate, normalizes the sensitivity decay rate and the error accumulation rate, maps the value range of the sensitivity decay rate and the error accumulation rate to [0,1], obtains the normalized value of the sensitivity decay rate and the normalized value of the error accumulation rate, and sums the normalized value of the sensitivity decay rate and the normalized value of the error accumulation rate to obtain the abnormal state factor.

5. The intelligent remote monitoring system for mass flowmeter according to claim 2 is characterized in that: When it is determined that the flow monitoring range of the flow meter needs to be dynamically adjusted, the fusion evaluation module dynamically adjusts the flow monitoring range based on the overall performance coefficient, and the adjustment algorithm is: , where is the initial flow monitoring range of the flow meter, is the flow monitoring range after dynamic regulation. It is the overall performance coefficient, that is, the flow monitoring range of the flow meter is narrowed by the overall performance coefficient, thereby improving the monitoring intensity.

6. The intelligent remote monitoring system for mass flowmeter according to claim 5, characterized in that: The flow meter monitors the crude oil flow in the pipeline in real time. When the actual crude oil flow is not within the flow monitoring range after dynamic regulation, a warning signal is sent to the management platform. The interpolation estimation module marks the flow meter that sends the warning signal as a warning flow meter and obtains information of all flow meters on the current pipeline. Calculate the weight of each flow meter in the pipeline relative to the warning flow meter. The calculation logic is: obtain the spatial distance between the flow meter and the warning flow meter, as well as the historical flow similarity between the flow meter and the warning flow meter, normalize the spatial distance and the historical flow similarity, sum the normalized spatial distance and the historical flow similarity to obtain the flow meter impact value, take the inverse of the impact value as the importance assignment, sum the importance assignments of all flow meters to obtain the assignment sum, and divide the importance assignment by the assignment sum to obtain the weight of each flow meter relative to the warning flow meter; The actual flow rate monitored by each flow meter is obtained, and the actual flow rates of all flow meters are weighted to obtain the estimated flow rate of the warning flow meter. The expression is: , where To warn the flow meter flow estimation, is the number of flow meters, For the The weight of each flow meter, For the The actual flow of each flow meter is detected, and the estimated flow of the warning flow meter is sent to the management platform.

7. The intelligent remote monitoring system for mass flowmeter according to claim 4 is characterized in that: The calculation expression of the sensitivity attenuation rate is: , where is the sensitivity attenuation rate, is the actual sensitivity of the flow meter probe at time instant, is the standard sensitivity of the flow meter probe at time instant, To monitor the time period; The calculation expression of the error accumulation rate is: , where is the error accumulation rate, is the flow meter usage time, For the estimated service life of the flow meter, is the adjustment coefficient, the value is 1.236, is the ambient temperature change, is the nominal temperature range.

8. The intelligent remote monitoring system for mass flowmeter according to claim 7 is characterized in that: The pipeline vibration amplitude is obtained online through a vibration sensor installed on the pipeline, and the calculation expression of the electromagnetic interference degree is: , where is the measured noise signal voltage, As the reference signal, the calculation expression of the electrostatic aggregation amplitude is: , where is the electrostatic surface charge density, is the surface area of ​​the flow meter monitoring area, is the distance of the electrostatic field.

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