Volume correction system with magnetic attack response function

Abstract

The invention provides a volume correction system with a magnetic attack response function, which relates to the field of volume correction and comprises a volume correction instrument, an edge computing device and a cloud platform. The volume correction instrument comprises a metering unit, a communication unit, a working condition sensing unit, a controller and a valve control unit. The working condition sensing unit at least comprises a temperature sensor, a pressure sensor and a magnetic attack sensing assembly. The edge calculation device is used for judging whether magnetic attack occurs or not based on the gas flow, the environment temperature, the environment pressure and the environment magnetic field information, calculating a compression factor based on the gas flow, the environment temperature and the environment pressure when the magnetic attack does not occur, performing volume correction based on the compression factor and a gas state equation algorithm, and obtaining the corrected volume flow; when it is judged that the magnetic attack occurs, the corrected volume flow is predicted; and the edge computing device is also used for uploading the corrected volume flow to a cloud platform. The volume correction system has the advantage of improving the response performance of the volume correction system to magnetic attacks.

Description

Technical Field

[0001] The invention relates to the field of volume correction, and in particular to a volume correction system with a magnetic attack response function. Background Art

[0002] As the proportion of natural gas in primary energy continues to rise, natural gas measurement becomes increasingly important. The volume of natural gas is related to ambient temperature and pressure. The primary detection instrument can only detect the gas volume under working conditions. Therefore, the correction function of the intelligent volume corrector, a secondary instrument for natural gas flow measurement, is particularly important. The intelligent volume corrector is used in conjunction with a gas flow meter. It detects temperature and pressure signals online, corrects through compression factors, completes secondary measurement, and ensures measurement accuracy and reliability.

[0003] The detection circuit of the existing volume corrector is easily affected by the interference magnetic field, and some criminals often use various means to affect the normal operation of the volume corrector to steal gas. For example, strong magnets are used to affect the flow detection circuit so that it cannot provide normal flow detection signals.

[0004] Therefore, it is necessary to provide a volume correction system with a magnetic attack response function to improve the response performance of the volume correction system to magnetic attacks. Summary of the invention

[0005] The present invention provides a volume correction system with a magnetic attack response function, comprising a plurality of volume correctors, at least one edge computing device and a cloud platform; the volume corrector comprises a metering unit, a communication unit, a working condition sensing unit, a controller and a valve control unit, wherein the working condition sensing unit comprises at least a temperature sensor, a pressure sensor and a magnetic attack sensing component, the metering unit is used to obtain gas flow, the working condition sensing unit is used to obtain ambient temperature, ambient pressure and ambient magnetic field information, the controller is used to transmit the gas flow, ambient temperature, ambient pressure and ambient magnetic field information to the edge computing device through the communication unit; the edge computing device is used to transmit the gas flow, ambient temperature, ambient pressure and ambient magnetic field information to the edge computing device based on the gas flow, ambient temperature, ambient pressure and ambient magnetic field information. force and environmental magnetic field information, determine whether a magnetic attack occurs; when it is determined that no magnetic attack occurs, calculate the compression factor based on the gas flow rate, ambient temperature and ambient pressure, perform volume correction based on the compression factor and the gas equation algorithm, and obtain the corrected volume flow rate; when it is determined that a magnetic attack occurs, predict the corrected volume flow rate based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information; the edge computing device is also used to upload the corrected volume flow rate to the cloud platform; the cloud platform is also used to send parameter setting information; the controller is also used to receive the parameter setting information, and control the valve control unit, the metering unit and the working condition sensing unit to work based on the parameter setting information.

[0006] Furthermore, the edge computing device is also used to: obtain multiple groups of first test data, wherein each group of the first test data includes a test environment temperature and a test environment pressure collected by a temperature sensor of a model and a pressure sensor of a model at multiple test time points under multiple test environments; based on the multiple groups of first test data, establish a correlation coefficient surface corresponding to any model of temperature sensor and any model of pressure sensor, wherein the correlation coefficient surface is used to characterize the inductive correlation coefficient of the temperature sensor and the pressure sensor under multiple test environments; before using the volume corrector, determine a target correlation coefficient surface based on the device model information of the working condition perception unit of the volume corrector, calculate the perception abnormality parameter of the working condition perception unit of the volume corrector based on the target correlation surface and the ambient temperature and ambient pressure uploaded by the volume corrector, and when the perception abnormality parameter of the working condition perception unit of the volume corrector is greater than the perception abnormality parameter threshold, report the perception abnormality prompt information to the cloud platform.

[0007] Furthermore, the edge computing device calculates the perception abnormality parameters of the working condition perception unit of the volume corrector based on the target related surface and the ambient temperature and ambient pressure uploaded by the volume corrector, including: calculating the environmental change parameter based on the ambient temperature and ambient pressure of multiple consecutive time points uploaded by the volume corrector; when the environmental change parameter is greater than the environmental change parameter threshold, dividing the multiple consecutive time points into multiple time periods, and for each of the time periods, calculating the average ambient temperature and the average ambient pressure corresponding to the time period, determining the target correlation coefficient corresponding to the time period from the target related surface based on the average ambient temperature and the average ambient pressure corresponding to the time period, and calculating the real correlation coefficient corresponding to the time period based on the ambient temperature and the ambient pressure of the multiple consecutive time points included in the time period. coefficient, based on the real correlation coefficient and the target correlation coefficient corresponding to the time period, calculate the perception abnormality parameter corresponding to the time period, and based on the perception abnormality parameter corresponding to each of the time periods, calculate the perception abnormality parameter of the working condition perception unit of the volume corrector; when the environmental change parameter is less than or equal to the environmental change parameter threshold, based on the environmental temperature and environmental pressure at multiple consecutive time points uploaded by the volume corrector, calculate the average environmental temperature and the average environmental pressure, based on the average environmental temperature and the average environmental pressure, determine the target correlation coefficient from the target correlation surface, calculate the real correlation coefficient based on the environmental temperature and environmental pressure at multiple consecutive time points uploaded by the volume corrector, and calculate the perception abnormality parameter of the working condition perception unit of the volume corrector based on the real correlation coefficient and the target correlation coefficient.

[0008] Furthermore, the cloud platform is also used to: obtain multiple groups of second test data, wherein the second test data includes environmental magnetic field information obtained by working condition sensing units of multiple volume correctors under multiple magnetic attack environments; based on the multiple groups of second test data, calculate the magnetic attack associated parameters of any two volume correctors, and determine the magnetic attack associated volume corrector of each volume corrector; the edge computing device determines whether a magnetic attack occurs based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information, including: determining whether a magnetic attack occurs based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information and the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information collected by the magnetic attack associated volume corrector of the volume corrector.

[0009] Furthermore, the edge computing device determines whether a magnetic attack occurs based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information and the magnetic attack-related volume corrector of the volume corrector. The method comprises: calculating a first magnetic attack risk value of the volume corrector based on the gas flow rate, ambient temperature and ambient pressure; calculating a second magnetic attack risk value of the volume corrector based on the ambient magnetic field information; calculating a magnetic attack risk value of the volume corrector based on the first magnetic attack risk value and the second magnetic attack risk value of the volume corrector; and calculating a magnetic attack risk value of the volume corrector based on the magnetic attack-related volume corrector of the volume corrector. The first magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector is calculated based on the gas flow rate and ambient temperature collected by the positive instrument; the second magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector is calculated based on the ambient magnetic field information collected by the volume corrector; the magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector is calculated based on the first magnetic attack risk value and the second magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector; based on the magnetic attack risk value of the volume corrector and the magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector, it is determined whether a magnetic attack occurs.

[0010] Furthermore, the edge computing device calculates the first magnetic attack risk value of the volume corrector based on the gas flow, ambient temperature and ambient pressure, including: performing variational modal decomposition on the gas flow at multiple consecutive time points uploaded by the volume corrector to obtain multiple gas flow modal components and gas flow residuals; performing variational modal decomposition on the ambient temperature at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient temperature modal components and ambient temperature residuals; performing variational modal decomposition on the ambient temperature at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient pressure modal components and ambient temperature residuals; calculating the first magnetic attack risk value based on the multiple gas flow modal components and gas flow residuals, multiple ambient temperature modal components and ambient temperature residuals, and multiple ambient pressure modal components and ambient temperature residuals through a first risk prediction model.

[0011] Furthermore, the edge computing device calculates a second magnetic attack risk value of the volume corrector based on the environmental magnetic field information, including: performing variational modal decomposition on the environmental magnetic field information of multiple consecutive time points uploaded by the volume corrector to obtain multiple environmental magnetic field modal components and environmental magnetic field residuals; and calculating the second magnetic attack risk value based on the multiple environmental magnetic field modal components and environmental magnetic field residuals through a second risk prediction model.

[0012] Furthermore, the edge computing device calculates the compression factor based on the gas flow, ambient temperature and ambient pressure, including: obtaining composition information of natural gas; determining a target compression factor correction algorithm based on the composition information of natural gas, gas flow, ambient temperature and ambient pressure; and using the target compression factor correction algorithm to calculate the compression factor based on the gas flow, ambient temperature and ambient pressure.

[0013] Furthermore, the edge computing device predicts a corrected volume flow rate based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information, including: judging a correction method based on the multiple gas flow rate modal components and gas flow rate residuals, multiple ambient temperature modal components and ambient temperature residuals, multiple ambient pressure modal components and ambient temperature residuals, and multiple ambient magnetic field modal components and ambient magnetic field residuals, wherein the correction method is one of a real-time correction method and a historical data correction method; and predicting a corrected volume flow rate according to the correction method.

[0014] Furthermore, the edge computing device predicts a corrected volume flow rate according to a correction method, including: when the correction method is a real-time correction method, generating a corrected gas flow rate, ambient temperature and ambient pressure based on a plurality of gas flow modal components and gas flow residuals, a plurality of ambient temperature modal components and ambient temperature residuals, a plurality of ambient pressure modal components and ambient temperature residuals, and a plurality of ambient magnetic field modal components and ambient magnetic field residuals through a parameter correction model; calculating a compression factor based on the corrected gas flow rate, ambient temperature and ambient pressure; performing volume correction based on the compression factor and a gas equation algorithm to obtain a corrected volume flow rate; when the correction method is a historical data correction method, predicting the corrected volume flow rate based on the historical corrected volume flow rate of the volume corrector.

[0015] Compared with the prior art, the volume correction system with magnetic attack response function provided by the present invention has at least the following beneficial effects: 1. The system's built-in magnetic attack sensing component can monitor environmental magnetic field information in real time. Once an abnormal magnetic field change is detected, it can be determined as a magnetic attack behavior. This real-time monitoring mechanism greatly improves the security and reliability of the system and prevents the influence of external malicious interference on the measurement results. When the edge computing device determines that a magnetic attack has occurred, it will immediately predict the corrected volume flow rate based on the current gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information. This rapid response mechanism ensures that the system can provide relatively accurate flow data even under magnetic attacks. When the edge computing device does not detect a magnetic attack, it will use the compression factor and gas state equation algorithm to perform volume correction based on accurate gas flow rate, ambient temperature and ambient pressure data to obtain a high-precision corrected volume flow rate. This intelligent correction algorithm greatly improves the accuracy and reliability of measurement. By integrating multiple sensor data (such as temperature, pressure, magnetic field, etc.), the system can more comprehensively reflect the actual state of the gas, thereby improving the accuracy and stability of the correction. The cloud platform is not only used to receive and store the corrected volume flow data from the edge computing device, but also to send parameter setting information to each volume corrector. This remote monitoring and management function makes the system more flexible and convenient, and can realize centralized control and management of multiple sites or devices. The cloud platform can also conduct in-depth analysis of the collected data to provide data support for system optimization and improvement. For example, by analyzing historical data, potential magnetic attack patterns or traffic anomalies can be discovered, so that preventive measures can be taken in advance.

[0016] 2. The system not only relies on the data of a single volume corrector, but also takes into account the data of multiple volume correctors associated with it. This multi-dimensional data fusion method can more comprehensively reflect the characteristics of magnetic attacks, thereby improving the accuracy of detection. By calculating the first magnetic attack risk value (based on gas flow, ambient temperature, ambient pressure) and the second magnetic attack risk value (based on ambient magnetic field information), and combining the two to obtain the final magnetic attack risk value, the possibility of magnetic attack can be more accurately assessed. When the data of a single volume corrector is abnormal, the system can compare and verify with the data of other associated devices to reduce the possibility of false alarms or missed alarms and enhance the stability and reliability of the system.

[0017] 3. The system provides two methods: real-time correction and historical data correction, which can be flexibly selected according to actual conditions. Real-time correction is suitable for scenarios that require instant and accurate data, while historical data correction can provide reliable predictions when data stability requirements are high or real-time data is insufficient. Through real-time correction, the system can instantly consider the current gas flow, ambient temperature, ambient pressure, and changes in the ambient magnetic field, so as to more accurately calculate and correct the volume flow. This dynamic adjustment ensures the accuracy and reliability of the measurement results. Taking into account the ambient magnetic field information, the system can identify and respond to potential magnetic attacks, such as tampering with the flow meter readings through magnetic field interference. This design improves the security and stability of the system and prevents illegal operations and fraud. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0019] Figure 1 is a schematic diagram of a module of a volume correction system with a magnetic attack response function according to some embodiments of this specification; Figure 2 It is a schematic diagram of a flow chart for determining whether a magnetic attack has occurred according to some embodiments of this specification. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0021] Figure 1 is a schematic diagram of a module of a volume correction system with a magnetic attack response function according to some embodiments of this specification, such as Figure 1 As shown, the volume correction system with magnetic attack response function may include multiple volume correctors, at least one edge computing device and a cloud platform.

[0022] The volume corrector includes a metering unit, a communication unit, a working condition sensing unit, a controller and a valve control unit, wherein the working condition sensing unit includes at least a temperature sensor, a pressure sensor and a magnetic attack sensing component, the metering unit is used to obtain the gas flow, the working condition sensing unit is used to obtain the ambient temperature, ambient pressure and ambient magnetic field information, and the controller is used to transmit the gas flow, ambient temperature, ambient pressure and ambient magnetic field information to the edge computing device through the communication unit.

[0023] The edge computing device is used to determine whether a magnetic attack has occurred based on gas flow, ambient temperature, ambient pressure and ambient magnetic field information.

[0024] In some embodiments, the edge computing device is further configured to: Acquire multiple groups of first test data, wherein each group of first test data includes test environment temperature and test environment pressure collected by a temperature sensor of one model and a pressure sensor of one model at multiple test time points under multiple test environments; Based on multiple groups of first test data, a correlation coefficient surface corresponding to any type of temperature sensor and any type of pressure sensor is established, wherein the correlation coefficient surface is used to characterize the inductive correlation coefficients of the temperature sensor and the pressure sensor under multiple test environments; Before using the volume corrector, determine the target correlation coefficient surface based on the device model information of the working condition perception unit of the volume corrector, calculate the perception abnormality parameters of the working condition perception unit of the volume corrector based on the target correlation surface and the ambient temperature and ambient pressure uploaded by the volume corrector, and report the perception abnormality prompt information to the cloud platform when the perception abnormality parameters of the working condition perception unit of the volume corrector are greater than the perception abnormality parameter threshold.

[0025] Specifically, the correlation coefficient surface may be a surface in a three-dimensional coordinate axis, where an x-axis, a y-axis, and a z-axis of the three-dimensional coordinate axis represent the ambient temperature, the ambient pressure, and the induction correlation coefficient, respectively.

[0026] For any type of temperature sensor and any type of pressure sensor, a multivariate function can be established, wherein the independent variable of the multivariate function is the inductive correlation coefficient of the temperature sensor and the pressure sensor under the test environment, and the dependent variable of the multivariate function includes the ambient temperature and the ambient pressure. The multivariate function is solved by the first test data corresponding to the temperature sensor and the pressure sensor of this type, and a correlation coefficient surface is generated according to the solved multivariate function.

[0027] Among them, the induction correlation coefficient of the temperature sensor of this type and the pressure sensor of this type under a certain test environment can be calculated based on the following formula: in, is the inductive correlation coefficient of the i-th type of temperature sensor and the j-th type of pressure sensor under the k-th test environment, is the ambient temperature sensed by the temperature sensor of the ith type at the tth test time point under the kth test environment, is the total number of sampled time points, is the environmental pressure sensed by the temperature sensor of the i-th model at the t-th test time point under the k-th test environment.

[0028] The device model information of the working condition sensing unit of the volume corrector may include the model of the temperature sensor and the model of the pressure sensor, and the correlation coefficient surface corresponding to the model of the temperature sensor and the model of the pressure sensor is used as the target correlation coefficient surface.

[0029] In some embodiments, the edge computing device calculates the abnormal perception parameters of the working condition perception unit of the volume corrector based on the target-related surface and the ambient temperature and ambient pressure uploaded by the volume corrector, including: Calculate environmental change parameters based on the ambient temperature and ambient pressure at multiple consecutive time points uploaded by the volume corrector; When the environmental change parameter is greater than the environmental change parameter threshold, multiple continuous time points are divided into multiple time periods, and for each time period, the average environmental temperature and the average environmental pressure corresponding to the time period are calculated, and based on the average environmental temperature and the average environmental pressure corresponding to the time period, the target correlation coefficient corresponding to the time period is determined from the target correlation surface, and based on the environmental temperature and the environmental pressure of multiple continuous time points included in the time period, the real correlation coefficient corresponding to the time period is calculated, and based on the real correlation coefficient and the target correlation coefficient corresponding to the time period, the perception abnormality parameter corresponding to the time period is calculated, and based on the perception abnormality parameter corresponding to each time period, the perception abnormality parameter of the working condition perception unit of the volume corrector is calculated; When the environmental change parameter is less than or equal to the environmental change parameter threshold, the average ambient temperature and the average ambient pressure are calculated based on the ambient temperature and the ambient pressure at multiple consecutive time points uploaded by the volume corrector, and the target correlation coefficient is determined from the target correlation surface based on the ambient temperature and the ambient pressure at multiple consecutive time points uploaded by the volume corrector. The true correlation coefficient is calculated based on the ambient temperature and the ambient pressure at multiple consecutive time points uploaded by the volume corrector, and the perception abnormality parameter of the working condition perception unit of the volume corrector is calculated based on the true correlation coefficient and the target correlation coefficient.

[0030] Specifically, the environmental change parameter can be calculated based on the following formula: in, is the environmental variation parameter, is the ambient temperature at the tth time point among the multiple consecutive time points uploaded by the volume corrector, is the total number of time points, It is the ambient pressure at the tth time point among multiple consecutive time points uploaded by the volume corrector.

[0031] For each time period, the corresponding induction correlation coefficient can be read from the target correlation coefficient surface as the target correlation coefficient based on the average value of the ambient temperature and the average value of the ambient pressure corresponding to the time period.

[0032] The calculation method of the true correlation coefficient is similar to that of the induced correlation coefficient and will not be repeated here.

[0033] The perception anomaly parameter corresponding to the time period can be calculated based on the following formula: in, is the perception abnormality parameter corresponding to the nth time period, is the preset parameter, greater than 0, is the true correlation coefficient corresponding to the nth time period, is the target correlation coefficient corresponding to the nth time period.

[0034] The perception abnormality parameter of the working condition perception unit of the volume corrector can be calculated according to the perception abnormality parameter corresponding to each time period based on the following formula: in, is the abnormal perception parameter of the working condition perception unit of the volume corrector, is the total number of time periods.

[0035] The perception abnormality parameter of the working condition perception unit of the volume corrector can be calculated according to the true correlation coefficient and the target correlation coefficient based on the following formula: in, is the abnormal perception parameter of the working condition perception unit of the volume corrector, is the true correlation coefficient, is the target correlation coefficient, is the preset parameter, Greater than 0.

[0036] In some embodiments, the cloud platform is also used to: Acquire multiple sets of second test data, wherein the second test data includes environmental magnetic field information acquired by working condition sensing units of multiple volume correctors under multiple magnetic attack environments; Based on the multiple sets of second test data, the magnetic attack correlation parameters of any two volume correctors are calculated, and the magnetic attack correlation volume corrector of each volume corrector is determined.

[0037] Specifically, the method of calculating the magnetic attack correlation parameter of any two volume correctors is similar to the method of calculating the induction correlation coefficient, which will not be repeated here.

[0038] When the magnetic attack associated parameters of the two volume correctors are greater than the magnetic attack associated parameter threshold, it is determined that the two volume correctors are magnetic attack associated volume correctors for each other.

[0039] In some embodiments, the edge computing device determines whether a magnetic attack occurs based on gas flow, ambient temperature, ambient pressure, and ambient magnetic field information, including: Based on the gas flow, ambient temperature, ambient pressure and ambient magnetic field information and the magnetic attack correlation volume corrector, the gas flow, ambient temperature, ambient pressure and ambient magnetic field information collected by the volume corrector are judged whether a magnetic attack occurs.

[0040] Figure 2 is a flow chart of determining whether a magnetic attack has occurred according to some embodiments of this specification, such as Figure 2 As shown, the edge computing device determines whether a magnetic attack occurs based on the gas flow rate, ambient temperature, ambient pressure, and ambient magnetic field information and the magnetic attack correlation volume corrector collected by the volume corrector, including: Calculate the first magnetic attack risk value of the volume corrector based on the gas flow rate, ambient temperature and ambient pressure; Based on the environmental magnetic field information, a second magnetic attack risk value of the volume corrector is calculated; Calculating a magnetic attack risk value of the volume corrector based on a first magnetic attack risk value and a second magnetic attack risk value of the volume corrector; Calculate a first magnetic attack risk value of the volume corrector associated with the magnetic attack based on the gas flow and the ambient temperature collected by the volume corrector associated with the magnetic attack; Calculate a second magnetic attack risk value of the volume corrector associated with the magnetic attack based on the environmental magnetic field information collected by the volume corrector associated with the magnetic attack; Calculating the magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector based on the first magnetic attack risk value and the second magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector; Based on the magnetic attack risk value of the volume corrector and the magnetic attack correlation of the volume corrector and the magnetic attack risk value of the volume corrector, it is determined whether a magnetic attack occurs.

[0041] Specifically, the overall magnetic attack risk value can be calculated based on the magnetic attack risk value of the volume corrector and the magnetic attack risk value of the volume corrector associated with the magnetic attack of the volume corrector according to the following formula. When the overall magnetic attack risk value is greater than the overall magnetic attack risk value threshold, it is determined that a magnetic attack has occurred: in, is the overall magnetic attack risk value, , , and is the preset weight, and , , and are greater than 0, , , is the magnetic attack risk value of the volume corrector, is the magnetic attack correlation parameter of the volume corrector and the mth magnetic attack correlation volume corrector, is the magnetic attack risk value of the volume corrector associated with the mth magnetic attack of the volume corrector, The total number of volume modifiers associated with the magnetic attack of the volume modifier.

[0042] In some embodiments, the edge computing device calculates a first magnetic attack risk value of the volume corrector based on the gas flow rate, the ambient temperature, and the ambient pressure, including: Perform variational modal decomposition on the gas flow at multiple consecutive time points uploaded by the volume corrector to obtain multiple gas flow modal components and gas flow residuals; Perform variational modal decomposition on the ambient temperature at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient temperature modal components and ambient temperature residuals; Perform variational modal decomposition on the ambient temperature at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient pressure modal components and ambient temperature residuals; A first magnetic attack risk value is calculated by a first risk prediction model based on multiple gas flow modal components and gas flow residuals, multiple ambient temperature modal components and ambient temperature residuals, and multiple ambient pressure modal components and ambient temperature residuals, wherein the first risk prediction model may be a convolutional neural network (CNN) model.

[0043] In some embodiments, the edge computing device calculates a second magnetic attack risk value of the volume corrector based on the environmental magnetic field information, including: Perform variational modal decomposition on the ambient magnetic field information at multiple continuous time points uploaded by the volume corrector to obtain multiple ambient magnetic field modal components and ambient magnetic field residuals; A second magnetic attack risk value is calculated based on a plurality of ambient magnetic field modal components and ambient magnetic field residuals through a second risk prediction model, wherein the second risk prediction model may be a convolutional neural network (CNN) model.

[0044] When it is determined that no magnetic attack has occurred, the compression factor is calculated based on the gas flow rate, ambient temperature and ambient pressure, and the volume correction is performed based on the compression factor and the gas equation algorithm to obtain the corrected volume flow rate.

[0045] In some embodiments, the edge computing device calculates the compression factor based on the gas flow rate, ambient temperature, and ambient pressure, including: Obtain information on the composition of natural gas; Determine the target compression factor correction algorithm based on the composition information of natural gas, gas flow, ambient temperature and ambient pressure; The target compressibility factor correction algorithm is used to calculate the compressibility factor based on the gas flow rate, ambient temperature and ambient pressure.

[0046] The target compression factor correction algorithm may be an existing algorithm for calculating the compression factor, such as the NX-19 algorithm, the SGERG-88 algorithm, and the AGA8-92DC algorithm. The AGA8-92DC algorithm can calculate a relatively accurate natural gas compression factor based on parameters such as the molar composition, absolute pressure, and thermodynamic temperature of natural gas.

[0047] A knowledge graph can be established in advance to determine the natural gas composition information, gas flow, ambient temperature and ambient pressure that are suitable for each compression factor correction algorithm, and the target compression factor correction algorithm that is suitable for the current natural gas composition information, gas flow, ambient temperature and ambient pressure can be found based on the knowledge graph.

[0048] When it is determined that a magnetic attack has occurred, the corrected volume flow rate is predicted based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information.

[0049] Specifically include: Based on multiple gas flow modal components and gas flow residuals, multiple ambient temperature modal components and ambient temperature residuals, multiple ambient pressure modal components and ambient temperature residuals, and multiple ambient magnetic field modal components and ambient magnetic field residuals, a correction method is determined, wherein the correction method is one of a real-time correction method and a historical data correction method; According to the correction method, the corrected volume flow rate is predicted.

[0050] Specifically, the correction method can be determined by a correction determination model based on multiple gas flow modal components and gas flow residuals, multiple ambient temperature modal components and ambient temperature residuals, multiple ambient pressure modal components and ambient temperature residuals, and multiple ambient magnetic field modal components and ambient magnetic field residuals, wherein the correction determination model can be a convolutional neural network (CNN) model.

[0051] In some embodiments, the edge computing device predicts the corrected volume flow rate according to the correction method, including: When the correction mode is a real-time correction mode, a corrected gas flow, ambient temperature and ambient pressure are generated based on a plurality of gas flow modal components and gas flow residuals, a plurality of ambient temperature modal components and ambient temperature residuals, a plurality of ambient pressure modal components and ambient temperature residuals, and a plurality of ambient magnetic field modal components and ambient magnetic field residuals through a parameter correction model; a compression factor is calculated based on the corrected gas flow, ambient temperature and ambient pressure; a volume correction is performed based on the compression factor and a gas state equation algorithm to obtain a corrected volume flow; wherein the parameter correction model may be a convolutional neural network (CNN) model; When the correction method is the historical data correction method, the corrected volume flow rate is predicted based on the historical corrected volume flow rate of the volume corrector.

[0052] Specifically, a historical database may be established, wherein the historical database is used to record the historically corrected volume flow, ambient temperature, and ambient pressure at multiple historical time points in multiple historical time periods.

[0053] Obtain the historical corrected volume flow, ambient temperature and ambient pressure of multiple time points in the current time period when no magnetic attack occurs, calculate similarity parameters of each historical time period based on the historical corrected volume flow, ambient temperature and ambient pressure of multiple time points in the current time period when no magnetic attack occurs and the historical corrected volume flow, ambient temperature and ambient pressure of multiple historical time points in multiple historical time periods, take the historical time period with similarity parameters greater than the similarity parameter threshold as the target time period, and predict the corrected volume flow through a flow prediction model based on the historical corrected volume flow, ambient temperature and ambient pressure of multiple historical time points in the target historical time period and the historical corrected volume flow, ambient temperature and ambient pressure of multiple time points in the current time period when no magnetic attack occurs.

[0054] For example, similar parameters for historical time periods can be calculated based on the following formula: in, is the similarity parameter of the g-th historical period, is the preset parameter, greater than 0, is the mean ambient temperature in the g-th historical period, is the average of the ambient temperature at multiple time points in the current time period when no magnetic attack occurs, is the mean environmental pressure in the g-th historical period, is the average value of the environmental pressure at multiple time points in the current time period when no magnetic attack occurred, is the corrected mean volume flow rate of the g-th historical time period, It is the corrected mean volume flow rate at multiple time points in the current time period when no magnetic attack occurs.

[0055] The edge computing device is also used to upload the corrected volume flow rate to the cloud platform; The cloud platform is also used to send down parameter setting information (e.g., valve opening parameters, data collection frequency of temperature sensors, data collection frequency of pressure sensors); The controller is also used to receive parameter setting information, and control the operation of the valve control unit, the metering unit and the working condition sensing unit based on the parameter setting information.

[0056] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A volume correction system with a magnetic attack response function, characterized in that: It includes a plurality of volume correctors, at least one edge computing device and a cloud platform; The volume corrector includes a metering unit, a communication unit, a working condition sensing unit, a controller and a valve control unit, wherein the working condition sensing unit includes at least a temperature sensor, a pressure sensor and a magnetic attack sensing component, the metering unit is used to obtain the gas flow, the working condition sensing unit is used to obtain the ambient temperature, ambient pressure and ambient magnetic field information, and the controller is used to transmit the gas flow, ambient temperature, ambient pressure and ambient magnetic field information to the edge computing device through the communication unit; The edge computing device is used to determine whether a magnetic attack occurs based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information; when it is determined that no magnetic attack occurs, the edge computing device calculates a compression factor based on the gas flow rate, ambient temperature and ambient pressure; based on the compression factor and a gas state equation algorithm, performs volume correction to obtain a corrected volume flow rate; when it is determined that a magnetic attack occurs, the edge computing device predicts a corrected volume flow rate based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information; The edge computing device is also used to upload the corrected volume flow to the cloud platform; The cloud platform is also used to send parameter setting information; The controller is also used to receive the parameter setting information, and control the valve control unit, the metering unit and the working condition sensing unit to operate based on the parameter setting information.

2. The volume correction system with magnetic attack countermeasure function according to claim 1, characterized in that: The edge computing device is also used for: Acquire multiple groups of first test data, wherein each group of the first test data includes test environment temperature and test environment pressure collected by a temperature sensor of one model and a pressure sensor of one model at multiple test time points under multiple test environments; Based on the multiple groups of first test data, a correlation coefficient surface corresponding to any type of temperature sensor and any type of pressure sensor is established, wherein the correlation coefficient surface is used to characterize the inductive correlation coefficients of the temperature sensor and the pressure sensor under multiple test environments; Before using the volume corrector, a target correlation coefficient surface is determined based on the device model information of the working condition perception unit of the volume corrector, and a perception abnormality parameter of the working condition perception unit of the volume corrector is calculated based on the target correlation surface and the ambient temperature and ambient pressure uploaded by the volume corrector. When the perception abnormality parameter of the working condition perception unit of the volume corrector is greater than a perception abnormality parameter threshold, perception abnormality prompt information is reported to the cloud platform.

3. The volume correction system with magnetic attack countermeasure function according to claim 2, characterized in that: The edge computing device calculates the perception abnormality parameters of the working condition perception unit of the volume corrector based on the target related surface and the ambient temperature and ambient pressure uploaded by the volume corrector, including: Calculating environmental change parameters based on the environmental temperatures and environmental pressures at multiple consecutive time points uploaded by the volume corrector; When the environmental change parameter is greater than the environmental change parameter threshold, the multiple continuous time points are divided into multiple time periods, and for each of the time periods, the average environmental temperature and the average environmental pressure corresponding to the time period are calculated, and based on the average environmental temperature and the average environmental pressure corresponding to the time period, the target correlation coefficient corresponding to the time period is determined from the target correlation surface, and based on the environmental temperature and the environmental pressure of the multiple continuous time points included in the time period, the real correlation coefficient corresponding to the time period is calculated, and based on the real correlation coefficient and the target correlation coefficient corresponding to the time period, the perception abnormality parameter corresponding to the time period is calculated, and based on the perception abnormality parameter corresponding to each of the time periods, the perception abnormality parameter of the working condition perception unit of the volume corrector is calculated; When the environmental change parameter is less than or equal to the environmental change parameter threshold, the average environmental temperature and the average environmental pressure are calculated based on the environmental temperature and the environmental pressure at multiple consecutive time points uploaded by the volume corrector, and the target correlation coefficient is determined from the target correlation surface based on the average environmental temperature and the average environmental pressure. The real correlation coefficient is calculated based on the environmental temperature and the environmental pressure at multiple consecutive time points uploaded by the volume corrector, and the perception abnormality parameter of the working condition perception unit of the volume corrector is calculated based on the real correlation coefficient and the target correlation coefficient.

4. The volume correction system with magnetic attack countermeasure function according to claim 2, characterized in that: The cloud platform is also used for: Acquire multiple sets of second test data, wherein the second test data includes environmental magnetic field information acquired by working condition sensing units of multiple volume correctors under multiple magnetic attack environments; Based on the multiple sets of second test data, calculating the magnetic attack associated parameters of any two of the volume correctors, and determining the magnetic attack associated volume corrector of each of the volume correctors; The edge computing device determines whether a magnetic attack occurs based on the gas flow, ambient temperature, ambient pressure, and ambient magnetic field information, including: Based on the gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information and the magnetic attack correlation volume corrector's gas flow rate, ambient temperature, ambient pressure and ambient magnetic field information collected by the volume corrector, it is determined whether a magnetic attack occurs.

5. The volume correction system with magnetic attack countermeasure function according to claim 4, characterized in that: The edge computing device determines whether a magnetic attack occurs based on the gas flow rate, ambient temperature, ambient pressure, and ambient magnetic field information and the gas flow rate, ambient temperature, ambient pressure, and ambient magnetic field information collected by the magnetic attack correlation volume corrector, including: Calculating a first magnetic attack risk value of the volume corrector based on the gas flow, ambient temperature and ambient pressure; Based on the environmental magnetic field information, calculating a second magnetic attack risk value of the volume corrector; Calculating a magnetic attack risk value of the volume corrector based on a first magnetic attack risk value and a second magnetic attack risk value of the volume corrector; Calculating a first magnetic attack risk value of the magnetic attack associated volume corrector of the volume corrector based on the gas flow and ambient temperature collected by the magnetic attack associated volume corrector of the volume corrector; Calculating a second magnetic attack risk value of the magnetic attack associated volume corrector of the volume corrector based on the environmental magnetic field information collected by the magnetic attack associated volume corrector of the volume corrector; Calculating the magnetic attack risk value of the magnetic attack associated volume corrector of the volume corrector based on the first magnetic attack risk value and the second magnetic attack risk value of the magnetic attack associated volume corrector of the volume corrector; Based on the magnetic attack risk value of the volume corrector and the magnetic attack risk value of the magnetic attack-associated volume corrector of the volume corrector, it is determined whether a magnetic attack occurs.

6. The volume correction system with magnetic attack countermeasure function according to claim 5, characterized in that: The edge computing device calculates a first magnetic attack risk value of the volume corrector based on the gas flow, ambient temperature, and ambient pressure, including: Performing variational modal decomposition on the gas flow at multiple consecutive time points uploaded by the volume corrector to obtain multiple gas flow modal components and gas flow residuals; Performing variational modal decomposition on the ambient temperatures at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient temperature modal components and ambient temperature residuals; Performing variational modal decomposition on the ambient temperatures at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient pressure modal components and ambient temperature residuals; The first magnetic attack risk value is calculated by a first risk prediction model based on the multiple gas flow modal components and gas flow residuals, the multiple ambient temperature modal components and ambient temperature residuals, and the multiple ambient pressure modal components and ambient temperature residuals.

7. The volume correction system with magnetic attack countermeasure function according to claim 6, characterized in that: The edge computing device calculates a second magnetic attack risk value of the volume corrector based on the environmental magnetic field information, including: Performing variational modal decomposition on the ambient magnetic field information at multiple consecutive time points uploaded by the volume corrector to obtain multiple ambient magnetic field modal components and ambient magnetic field residuals; The second magnetic attack risk value is calculated based on the multiple ambient magnetic field modal components and the ambient magnetic field residuals through a second risk prediction model.

8. The volume correction system with magnetic attack response function according to any one of claims 1 to 7, characterized in that: The edge computing device calculates the compression factor based on the gas flow, ambient temperature and ambient pressure, including: Obtain information on the composition of natural gas; Determining a target compression factor correction algorithm based on the composition information of the natural gas, gas flow rate, ambient temperature and ambient pressure; The target compressibility factor correction algorithm is used to calculate the compressibility factor based on the gas flow rate, ambient temperature and ambient pressure.

9. The volume correction system with magnetic attack countermeasure function according to claim 7, characterized in that: The edge computing device predicts a corrected volume flow rate based on the gas flow rate, ambient temperature, ambient pressure, and ambient magnetic field information, including: Based on the multiple gas flow modal components and gas flow residuals, multiple ambient temperature modal components and ambient temperature residuals, multiple ambient pressure modal components and ambient temperature residuals, and multiple ambient magnetic field modal components and ambient magnetic field residuals, a correction method is determined, wherein the correction method is one of a real-time correction method and a historical data correction method; According to the correction method, the corrected volume flow rate is predicted.

10. The volume correction system with magnetic attack countermeasure function according to claim 9, characterized in that: The edge computing device predicts the corrected volume flow rate according to the correction method, including: When the correction method is a real-time correction method, a parameter correction model is used to generate a corrected gas flow rate, ambient temperature and ambient pressure based on multiple gas flow rate modal components and gas flow rate residuals, multiple ambient temperature modal components and ambient temperature residuals, multiple ambient pressure modal components and ambient temperature residuals, and multiple ambient magnetic field modal components and ambient magnetic field residuals; a compression factor is calculated based on the corrected gas flow rate, ambient temperature and ambient pressure; a volume correction is performed based on the compression factor and a gas equation algorithm to obtain a corrected volume flow rate; When the correction method is a historical data correction method, the corrected volume flow rate is predicted based on the historical corrected volume flow rate of the volume corrector.

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