Oil and gas pipe network sensor fault intelligent identification method and device and storage equipment

Abstract

The invention provides an oil and gas pipe network sensor fault intelligent identification method and device and storage equipment, and the method comprises the steps: determining a safety production process data threshold interval of a target oil and gas medium based on the physical parameters of the target oil and gas medium, and obtaining the operation parameters, and determining a sudden change time point when the operation parameters are within a safety production process data threshold interval, determining an abnormal type of the sensor based on the sudden change time point, and distinguishing physical faults (such as natural aging and power depletion) of the sensor and network attacks suffered by the sensor. According to the method, whether the network attack occurs or not can be judged based on the abnormal disturbance time difference of the sensor output data after the control core analysis, the potential sensor anomaly type is identified, the method is suitable for numerous application scenes, the matching degree between the anomaly identification result and the application scenes is improved, and the user experience is improved. Therefore, the sensor network attack and the physical fault are distinguished.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, and storage device for intelligently identifying faults in oil and gas pipeline network sensors. Background Art

[0002] In gas transmission systems, such as oil and gas pipeline networks, sensors perform crucial functions such as data collection and information generation. Sensor anomalies or attacks can lead to risks such as data transmission anomalies and information leakage, significantly impacting the current production node and potentially causing anomalies at other production nodes.

[0003] Related sensor fault detection methods (such as distributed filters, improved fusion algorithms based on historical measurements, improved quasi-Newton sensor networks, etc.) have the characteristics of a single detection method and dependence on external conditions such as sensor signals. They are difficult to apply to actual application scenarios, resulting in a low matching degree of the detection results in the above methods. There is still a safety issue of accidents in the gas transmission system caused by sensor failure or attack. Summary of the Invention

[0004] The present application provides an intelligent identification method, device and storage device for oil and gas pipeline network sensor faults, which are used to solve the technical problem of low matching degree of detection results in related detection methods.

[0005] In a first aspect, the present application provides a method for intelligently identifying sensor faults in an oil and gas pipeline network, comprising:

[0006] Determining a threshold range of safe production process data of the target oil and gas medium based on physical parameters of the target oil and gas medium;

[0007] Acquiring an operating parameter, and when the operating parameter is within the threshold range of the safe production process data, determining a mutation time point based on the operating parameter;

[0008] Based on the mutation time point, the abnormality type is determined; wherein the abnormality type includes sensor failure or sensor being attacked by a network.

[0009] Optionally, the target oil and gas medium includes a first substance and a second substance; the physical parameters include a first physical parameter of the first substance and a second physical parameter of the second substance; and determining a threshold range of safe production process data of the target oil and gas medium based on the physical parameters of the target oil and gas medium includes:

[0010] Calculating a mixing parameter of the target oil-gas medium based on the first physical parameter and the second physical parameter;

[0011] determining a relationship ratio of temperature and pressure based on the mixing parameter;

[0012] When the relationship ratio of the temperature and pressure is less than a first threshold, a safety production process data threshold interval of the target oil and gas medium is determined; wherein the safety production process data threshold interval includes at least one of a target temperature interval and a target pressure interval.

[0013] Optionally, the acquiring of the operating parameters, and determining the mutation time point based on the operating parameters when the operating parameters are within the safety production process data threshold range, includes:

[0014] Get running parameters;

[0015] When the operating parameter is within the threshold range of the safe production process data, the cumulative sum of the log-likelihood ratios at each moment is calculated based on the operating parameters within a preset time period;

[0016] The mutation time point is determined based on the cumulative sum of the log-likelihood ratios at the various moments.

[0017] Optionally, the operating parameters include a first operating parameter of the first device and a second operating parameter of the second device; the mutation time point includes a first mutation time point of the first device and a second mutation time point of the second device;

[0018] The determining of the abnormality type based on the mutation time point includes:

[0019] When both the first mutation time point and the second mutation time point are not empty, calculating the absolute value of the time difference between the first mutation time point and the second mutation time point;

[0020] When the absolute value of the time difference is smaller than a second threshold, the abnormality type is determined to be a sensor failure.

[0021] or,

[0022] When the absolute value of the time difference is greater than or equal to the second threshold, it is determined that the abnormality type is that the sensor is under a network attack.

[0023] Optionally, determining the abnormality type based on the mutation time point further includes:

[0024] When both the first mutation time point and the second mutation time point are empty, it is determined that the sensor operates normally.

[0025] Optionally, the safety production process data threshold interval includes an upper boundary and a lower boundary; after obtaining the operating parameters, the following steps are included:

[0026] When the operating parameter is less than the lower limit or greater than the upper limit, an alarm message is generated; wherein the alarm message is used to indicate that the sensor is operating abnormally.

[0027] In a second aspect, the present application provides an intelligent device for identifying faults in oil and gas pipeline network sensors, comprising:

[0028] A first determination module is configured to determine a threshold range of safety production process data of the target oil and gas medium based on physical parameters of the target oil and gas medium;

[0029] A second determination module is configured to obtain an operating parameter, and when the operating parameter is within the threshold range of the safe production process data, determine a mutation time point based on the operating parameter;

[0030] The third determination module is configured to determine an abnormality type based on the mutation time point; wherein the abnormality type includes a sensor failure or a sensor being attacked by a network.

[0031] In a third aspect, an embodiment of the present application provides a storage device comprising: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, so that the processor executes the intelligent identification method for oil and gas pipeline sensor faults as described in the first aspect and various possible designs of the first aspect.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the intelligent identification method for oil and gas pipeline sensor faults as described in the first aspect and various possible designs of the first aspect is implemented.

[0033] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the intelligent identification method for oil and gas pipeline sensor faults as described in the first aspect and various possible designs of the first aspect.

[0034] The present application provides an intelligent identification method, device and storage device for oil and gas pipeline sensor faults, which can determine the threshold range of the safe production process data of the target oil and gas medium based on the physical parameters of the target oil and gas medium, obtain the operating parameters, and determine the mutation time point when the operating parameters are within the threshold range of the safe production process data. The abnormality type of the sensor is determined based on the mutation time point, such as sensor failure or sensor being attacked by a network, that is, it can judge whether a network attack has occurred based on the size of the abnormal disturbance time difference after the control core analysis of the sensor output data, identify potential sensor abnormality types, and is suitable for many application scenarios. It improves the matching degree between the abnormality identification results and the application scenarios, realizes the distinction between sensor network attacks and physical failures, and thus improves the accuracy of the abnormality identification results. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0036] Figure 1 A schematic diagram of the architecture of an intelligent identification system for oil and gas pipeline network sensor faults provided in an embodiment of the present application;

[0037] Figure 2 This is a flow chart of a method for intelligently identifying faults in oil and gas pipeline network sensors provided in an embodiment of the present application;

[0038] Figure 3 This is a second flow chart of the intelligent identification method for oil and gas pipeline network sensor faults provided in an embodiment of the present application;

[0039] Figure 4 A schematic diagram of a sensor data transmission application provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of monitoring operating parameters provided in an embodiment of the present application;

[0041] Figure 6 The third flow chart of the intelligent identification method for oil and gas pipeline network sensor faults provided in the embodiment of the present application;

[0042] Figure 7 This is one of the application scenario diagrams of the oil and gas pipeline network sensor fault intelligent identification method provided in the embodiment of the present application;

[0043] Figure 8 Figure 2 of the application scenario of the intelligent identification method for oil and gas pipeline network sensor faults provided in an embodiment of the present application;

[0044] Figure 9 A schematic diagram of the structure of an intelligent device for identifying faults in oil and gas pipeline network sensors provided in an embodiment of the present application;

[0045] Figure 10 A schematic diagram of the structure of a storage device provided in an embodiment of the present application.

[0046] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0047] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0048] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0049] In gas transmission systems (such as oil and gas pipeline networks), sensors perform critical functions such as data collection and information generation. Sensor anomalies or attacks (such as those involving forged, altered, or replayed routing information, Sybil attacks, cesspool attacks, Hello attacks, Denial of Service (DoS) attacks, and Distributed Denial of Service (DDoS) attacks) can lead to data transmission anomalies and information leakage in gas transmission systems, significantly impacting current production nodes and potentially causing anomalies in other production nodes.

[0050] Related sensor fault detection methods (such as distributed filters, improved fusion algorithms based on historical measurements, and improved quasi-Newtonian sensor networks) typically utilize simulated sensor signals and employ filters, state detectors, and other devices to detect sensor attacks. These methods are characterized by a single detection method and reliance on external conditions such as sensor signals and operation logs. Operation logs typically consist of alarm records and status parameter records, containing only discrete data. Therefore, these logs are difficult to apply to real-world scenarios, resulting in poor matchability between the detection results of these methods and the potential for accidents in gas transmission systems caused by sensor failures or attacks.

[0051] In response to the above-mentioned problems, the present application proposes a method, device, and storage device for intelligently identifying sensor faults in oil and gas pipeline networks. The method includes: determining a threshold interval of safe production process data of the target oil and gas medium based on the physical parameters of the target oil and gas medium, obtaining operating parameters, and determining a mutation time point when the operating parameters are within the threshold interval of the safe production process data, and determining the type of sensor anomaly based on the mutation time point, such as a physical failure of the sensor or a network attack on the sensor. In this way, it is possible to determine whether a problem occurs with the sensor based on the threshold interval of the safe production process data, and identify potential sensor anomaly types when the operating parameters are within the threshold interval of the safe production process data. This method is applicable to many application scenarios, improves the matching degree between the anomaly identification results and the scenarios, and realizes the distinction between sensor network attacks and physical failures, thereby improving the accuracy of the anomaly identification results.

[0052] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0053] Figure 1 A schematic diagram of the architecture of an intelligent identification system for oil and gas pipeline network sensor faults provided in an embodiment of the present application.

[0054] Take the oil and gas pipeline network sensor fault intelligent identification system as an oil and gas pipeline system and the sensor as a pressure sensor as an example, see Figure 1In 101, the intelligent identification system for oil and gas pipeline network sensor failures includes a server and a control unit. The control unit includes an electric valve, a pressure sensor, a data collection device, a first device, a second device, and a PI controller. Among them, the pressure sensor is used to collect pressure data of the target oil and gas medium passing through the filter separator, and transmit it to the first device and the second device through the data collection device. During the operation of the oil and gas pipeline network system, the first device serves as an operation control device and inputs the processed data into the PI controller. The PI controller is used to generate feedback control instructions (such as feedback adjustment increase instructions, feedback adjustment decrease instructions or no adjustment, etc.) based on the pressure data transmitted by the first device, so as to control the electric valve to control the transmission of the target oil and gas medium and adjust the pressure of the target oil and gas medium in the oil and gas pipeline network system. The second device is used to control redundancy and monitor abnormal conditions.

[0055] The server is communicatively connected with the first device and the second device in the control unit, and is used to obtain operating parameters (such as pressure data or temperature data) in the first device and the second device, and determine whether there is a system abnormality based on the comparison result between the above operating parameters and the threshold interval of the safe production process data. When there is an operating parameter that is not within the threshold interval of the safe production process data, an alarm message is generated. When the operating parameters in the first device and the second device are within the threshold interval of the safe production process data, the data mutation time point in the first device and the second device is determined, and then the abnormality type of the sensor is determined.

[0056] The control principle of the PI controller is shown in 102. The control unit includes multiple electric valves ( Figure 1 Only two are shown, including Figure 1 PIC-100 is a pressure sensor controller (sensing, control and feedback are combined and can be referred to as a controller), and LIC-100 is a level sensor controller (sensing, control and feedback are combined and can be referred to as a controller).

[0057] PI controller includes a separator (such as Figure 1 As separator V-100 separates the target oil and gas media, the pressure sensor controller PIC-100 (sensor-control feedback integrated, referred to as the pressure sensor controller) detects that the pressure data is too high, and then controls the pressure via control valve VLV-104 to reduce it. If the pressure data is too low, then controls the pressure via control valve VLV-103 to increase it. Similarly, if the liquid level sensor controller LIC-100 detects that the liquid level data is too high, it controls the liquid level via control valve VLV-103 to reduce it. If the liquid level data is too low, then controls the liquid level via control valve VLV-103 to increase it.

[0058] Take pressure data as an example, Figure 1 As shown in 103: When the control valve VLV-104 receives the PV process value (also the pressure data after separation by the separator V-100), it compares it with the PID set value (the target value that the PID regulator expects to be adjusted) SP, generates a corresponding OP output value (including but not limited to an increase or decrease in value) based on the comparison result, and adjusts the pressure value based on the OP output value to increase or decrease it.

[0059] Based on this, the external transmission process of the intelligent identification system for oil and gas pipeline sensor faults is as follows: Figure 1 As shown in 104 , the intelligent identification system for oil and gas pipeline network sensor faults includes a mixer MIX-100, a separator V-100, control valves VLV-100, control valves VLV-101, control valves VLV-102, control valves VLV-103, control valves VLV-104, a compressor K-100, etc.

[0060] The target oil and gas medium is input into the mixer MIX-100 through the control valve VLV-100, and then input into the separator V-100 through the control valve VLV-104. After separation in the separator V-100, the temperature, pressure and other data of the target oil and gas medium are adjusted through the control valves VLV-102, VLV-103 and VLV-104.

[0061] Optionally, when the target oil and gas medium is pressure-regulated, this can be achieved through the compressor K-100, and Q-100 represents the heat energy required by the compressor.

[0062] Figure 2 This is a flow chart of an intelligent identification method for oil and gas pipeline network sensor faults provided in an embodiment of the present application. Figure 2 , the method comprises the following steps:

[0063] S101 : Determine a threshold range of safe production process data of a target oil and gas medium based on physical parameters of the target oil and gas medium.

[0064] Optionally, the oil and gas pipeline network sensor fault intelligent identification method provided in the embodiment of the present application can be applied to a storage device (for example, Figure 1 The server shown in the figure, etc.) is not limited in the embodiment of the present application.

[0065] Specifically, the physical parameters of the target oil and gas medium are obtained, wherein the target oil and gas medium is used to indicate the oil and gas medium transmitted in the gas transmission system (such as the oil and gas pipeline system). The target oil and gas medium can be an oil and gas medium composed of a single substance (or component) or a mixed oil and gas medium composed of two or more substances.

[0066] Based on the physical parameters of the target oil and gas medium, a threshold range for the safe production process data of the target oil and gas medium is determined. It is understood that the threshold range for the safe production process data of a single substance oil and gas medium is directly determined by the physical parameters of the single substance, while the threshold range for the safe production process data of a mixed oil and gas medium is determined by integrating the physical parameters of each substance in the mixed oil and gas medium.

[0067] S102: Acquire operating parameters, and when the operating parameters are within the safety production process data threshold range, determine a mutation time point based on the operating parameters.

[0068] Specifically, the operating parameters of the target oil and gas medium are obtained and compared with a threshold range of the target oil and gas medium's safe production process data. If the operating parameters are less than or equal to the upper boundary of the safe production process data threshold range and greater than or equal to the lower boundary of the safe production process data threshold range, it is determined that the operating parameters are within the safe production process data threshold range. This indicates that the sensor may not be abnormal, or that the sensor may be abnormal but the abnormality does not affect the normal operation of the gas transmission system. In this case, the time point of the data mutation can be determined based on the operating parameters of the target oil and gas medium.

[0069] The mutation time point is used to indicate the moment when the data in the operating parameters suddenly changes (such as suddenly increases and / or suddenly decreases).

[0070] S103. Determine an abnormality type based on the mutation time point; wherein the abnormality type includes sensor failure or sensor being attacked by a network attack.

[0071] Specifically, based on the data mutation time point in the operating parameters, the abnormality type of the sensor is determined, where the abnormality type includes but is not limited to sensor failure (or sensor physical failure) or the sensor being attacked by a network.

[0072] Understandably, cyberattacks on sensors represent a more widespread, potentially more damaging, and more difficult anomaly to repair. Physical vulnerabilities and failures require certain conditions and time to develop from a security breach to an incident. Once a sensor is successfully attacked or compromised by an external attacker, the failure / anomaly propagates to the physical core devices in the system. Therefore, when operating parameters fall within the threshold range for safe production process data, potential sensor failures or sensor cyberattack anomalies can be efficiently and quickly identified, thereby improving system security.

[0073] In some embodiments, the target oil and gas medium includes a first substance and a second substance; the physical parameters include a first physical parameter of the first substance and a second physical parameter of the second substance; and determining the safety production process data threshold interval of the target oil and gas medium based on the physical parameters of the target oil and gas medium includes:

[0074] Calculating a mixing parameter of the target oil-gas medium based on the first physical parameter and the second physical parameter;

[0075] determining a relationship ratio of temperature and pressure based on the mixing parameter;

[0076] When the relationship ratio of the temperature and pressure is less than a first threshold, a safety production process data threshold interval of the target oil and gas medium is determined; wherein the safety production process data threshold interval includes at least one of a target temperature interval and a target pressure interval.

[0077] Optionally, when the target oil and gas medium is a mixed oil and gas medium, the threshold interval of the safe production process data of the target oil and gas medium is determined by the physical parameters of each substance in the mixed oil and gas medium. In the embodiment of the present application, the method of determining the threshold interval of the safe production process data is mainly explained with the target oil and gas medium being a mixed oil and gas medium composed of two substances (such as a first substance and a second substance).

[0078] Specifically, when the target oil and gas medium includes a first substance and a second substance, the physical parameters of the target oil and gas medium include a first physical parameter of the first substance and a second physical parameter of the second substance. The physical parameters include, but are not limited to, at least one of the critical temperature, critical pressure, and relative humidity of the substance.

[0079] Specifically, based on the first physical parameter and the second physical parameter, the mixing parameter of the target oil and gas medium is calculated, and based on the mixing parameter of the target oil and gas medium, the relationship ratio of temperature and pressure in the target oil and gas medium is determined. When the relationship ratio of temperature and pressure is less than a first threshold, the algorithm convergence is determined, and based on the equipment parameters in the gas transmission system and the above-mentioned relationship ratio of temperature and pressure, the threshold interval of the safe production process data of the target oil and gas medium is determined.

[0080] The safety production process data threshold interval includes at least one of a target temperature interval and a target pressure interval.

[0081] Optionally, the safety production process data threshold interval includes an upper boundary and a lower boundary.

[0082] Exemplarily, the target temperature range is: [target temperature lower boundary, target temperature upper boundary], and the target pressure range is: [target pressure lower boundary, target pressure upper boundary].

[0083] As Figure 1 The oil and gas pipeline network sensor fault intelligent identification system shown in the figure takes the target oil and gas medium as a mixed oil and gas medium composed of two substances i and j as an example, and uses the Peng-Robinson equation to determine the boundary value of the pressure data, as shown in formula (1):

[0084]

[0085] Where R represents the universal gas constant (8.314 J / (mol·K)), T represents the temperature of the target oil and gas medium, a represents the attraction constant of the target oil and gas medium, and b represents the volume constant of the target oil and gas medium. α represents the temperature-dependent factor used to adjust the attraction constant a. The attraction constant a and volume constant b for any substance (e.g., i or j) in the target oil and gas medium are calculated as follows:

[0086]

[0087] Among them, T c represents the critical temperature, P C represents the critical pressure, T r It indicates the relative temperature of the target oil and gas medium, which is the ratio of the actual temperature to the critical temperature.

[0088] α represents the temperature dependence factor, which is used to adjust the attraction constant a, see the following formula:

[0089]

[0090] Based on this, the attraction constant a of the target oil and gas medium mix , volume constant b mix The calculation method is as follows:

[0091]

[0092] Among them, x i represents the mole fraction of the target oil and gas medium substance i in the mixture, a i represents the attraction constant of the i-th substance in the target oil and gas medium, b i represents the volume constant of the target oil and gas medium i-th substance, k ij represents the quadratic interaction factor between substance i and substance j.

[0093] It can be understood that formula (1) can also be called the relationship ratio of the temperature and pressure of the target oil and gas medium. The above formulas (1)-(6) are the criteria for determining whether the simulation process conforms to the actual production. When the difference between the temperature and pressure relationship ratio calculation results is less than the first threshold, the simulation process converges. Based on the equipment parameters in the gas transmission system and the above temperature and pressure relationship ratio, the threshold range of the safe production process data of the target oil and gas medium is determined, the dynamic conditions of the valve are defined, and the controllers and control expressions such as liquid level and pressure are designed (such as Figure 1 The first threshold value can be set according to actual conditions. For example, the first threshold value is set to 0.01 or 0.001.

[0094] Optionally, the safety production process data threshold range of the target oil and gas medium also includes upper and lower liquid level thresholds.

[0095] For example, the target oil and gas medium safety production process data threshold intervals are primarily used to indicate the upper and lower safety production thresholds at the filter separator inlet and outlet. For example, the target pressure interval at the filter separator inlet is [7, 8.95], the target pressure interval at the filter separator outlet is [7, 11.8], the target temperature interval at the filter separator inlet is [11, 12], and the target temperature interval at the filter separator outlet is [11, 50].

[0096] In some embodiments, after obtaining the operating parameters, the method includes:

[0097] When the operating parameter is less than the lower limit or greater than the upper limit, an alarm message is generated; wherein the alarm message is used to indicate that the sensor is operating abnormally.

[0098] Specifically, when the operating parameters of the target oil and gas medium are less than the lower boundary of the safe production process data threshold interval, or when the operating parameters of the target oil and gas medium are greater than the upper boundary of the safe production process data threshold interval, it is determined that the sensor is faulty or attacked, and the normal operation of the oil and gas monitoring system is affected, and an alarm message is generated based on the operating parameters of the target oil and gas medium; wherein, the alarm message is used to indicate abnormal sensor operation.

[0099] Optionally, the alarm information includes but is not limited to at least one of the type of operating parameters in the target oil and gas medium, the value of the operating parameters, etc.

[0100] Figure 3 This is another flow chart of a method for intelligently identifying faults in oil and gas pipeline network sensors provided in an embodiment of the present application. Figure 3 The method step S102 includes the following steps:

[0101] S1021. Obtain operating parameters.

[0102] S1022. When the operating parameter is within the safety production process data threshold range, calculate the cumulative sum of log-likelihood ratios at each moment according to each operating parameter within a preset time period.

[0103] Specifically, the operating parameters of the target oil and gas medium are obtained and compared with the upper and lower bounds of the safety production process data threshold range. If the target oil and gas medium operating parameters are within the upper and lower bounds of the safety production process data threshold range, it is determined that the sensor is not abnormal or that the sensor is abnormal but the abnormality does not affect the normal operation of the gas transmission system. Based on the operating parameters within a preset time period, the cumulative sum of the log-likelihood ratios at each time point is calculated.

[0104] Optionally, the operating parameters include but are not limited to at least one of temperature and air pressure.

[0105] It is understood that when the target oil and gas medium operating parameters include multiple types, if all operating parameters of each type are within the upper and lower boundaries of the safe production process data threshold range, the sensor is determined to have no abnormality, or if the sensor has an abnormality but the abnormality does not affect the normal operation of the gas transmission system. If one or more of the operating parameters of each type are not within the corresponding safe production process data threshold range, the sensor is determined to have an abnormality and a corresponding alarm is generated.

[0106] Exemplarily, the operating parameters of the target oil and gas medium include operating temperature and operating pressure. When the operating temperature is within the upper and lower bounds of the target temperature range, and the operating pressure is within the upper and lower bounds of the target pressure range, the sensor is determined to be abnormal, or to be abnormal but not affecting the normal operation of the gas transmission system. If any of the following conditions exist: the operating temperature is greater than the upper bound of the target temperature range, the operating temperature is less than the lower bound of the target temperature range, the operating pressure is greater than the upper bound of the target pressure range, or the operating pressure is less than the lower bound of the target pressure range, the sensor is determined to be abnormal, and a corresponding alarm is generated.

[0107] As an example but not a limitation, the number of operating parameters within a unit time may include one or more, and the corresponding preset time period may be specifically set according to the number of operating parameters within the unit time.

[0108] For example, there are two operating parameters for 1 second, and the preset time period is set to 5 seconds. Correspondingly, based on 15 groups of operating parameters within the preset time period, the cumulative sum of the log-likelihood ratios of the operating parameters every 0.5 seconds is calculated.

[0109] As another example, there is one operating parameter for 1s, and the preset time period is set to 10s. Correspondingly, based on 10 groups of operating parameters within the preset time period, the cumulative sum of the log-likelihood ratios of the operating parameters for every 1 second is calculated.

[0110] S1023. Determine the mutation time point based on the cumulative sum of the log-likelihood ratios at each time moment.

[0111] Specifically, the data mutation time point is determined based on the change in the cumulative sum of the log-likelihood ratio at each time moment.

[0112] Optionally, the mutation time point is determined based on the difference between the cumulative sum of the log-likelihood ratios at each moment and the cumulative sum of the log-likelihood ratios at the previous moment.

[0113] For example, the cumulative sum of log-likelihood ratios at time e is S e , the cumulative sum of log-likelihood ratios at time e-1 is the cumulative sum of log-likelihood ratios S e-1 , calculate S e With S e-1 When the difference is greater than or equal to the second threshold, the moment e is determined to be the data mutation time point.

[0114] The embodiments of the present application can determine the association rules between abnormal data and abnormal types by pre-determining the threshold interval of the safe production process data of the target oil and gas medium, based on the timing characteristics of different safety level controllers of the system (such as the application of the first device and the redundancy of the second device) under different causes of abnormal data, monitor the operating parameters of the equipment in real time, use the non-parametric cumulative sum to detect the deviation of sensor data, identify the mutation data that exceeds the threshold interval of the safe production process data of the target oil and gas medium as abnormal data, and based on the mutation time point of the data and the association rules as the criterion, efficiently, quickly and accurately identify the abnormal type of the sensor.

[0115] In some embodiments, the operating parameters include a first operating parameter of the first device and a second operating parameter of the second device; the mutation time point includes a first mutation time point of the first device and a second mutation time point of the second device.

[0116] Specifically, a sensor may be faulty or under attack from a network, and the data collected by the sensor may be inaccurate or erroneous. Therefore, the sensor can be detected for abnormalities based on the operating parameters of the first and second devices. The operating parameters include a first operating parameter of the first device and a second operating parameter of the second device. The corresponding mutation time points include a first mutation time point at which a data mutation occurs on the first device and a second mutation time point at which a data mutation occurs on the second device.

[0117] by Figure 1Take the oil and gas pipeline network sensor fault intelligent identification system shown in Figure 101 as an example. Figure 4 A schematic diagram of a sensor data transmission application provided in an embodiment of the present application.

[0118] like Figure 4 As shown, during the process of transmitting data from a sensor to a first device, the sensor was attacked by a network attack and injected with false data, causing the first device to receive abnormal data and causing the PI controller to issue an incorrect feedback control command. During operation, the data collected by the second device is normal data.

[0119] Based on this, the actual pressure data transmitted by the filter separator is P real (t), the pressure data collected by the sensor is P sensor (t), the pressure data obtained by the first device is P feed (t), the pressure data obtained by the second device is P monitor (t), respectively calculate the first mutation time point at which the data in the first device mutates, and the second mutation time point at which the data in the second device mutates.

[0120] Optionally, a change point statistics (NP-CUSUM) algorithm is used to construct a real-time monitoring model for sensor abnormal data to calculate the cumulative sum of log-likelihood ratios at each moment within a preset time period and identify the first mutation time point and the second mutation time point.

[0121] The operating parameter sequence Y collected in the preset time period e y1, y2...y t , where y e represents the operating parameters at time e. For each time point e, the upper and lower cumulative sums are calculated to detect the fluctuation of the data transmission process within the preset time period. The upper and lower cumulative sums are calculated as shown in the following formula.

[0122]

[0123] in, represents the upper cumulative sum, represents the cumulative sum, h represents the decision value, which is used to determine the mutation time point and can be set according to the actual situation. μ0 represents the expected value (baseline value) of the data transmission process and can be set according to the actual situation. If the offset is large, it is determined that the data has changed. max(0,·) takes the larger value of zero and the current value to ensure that the calculation result is not negative.

[0124] Optionally, the log-likelihood ratio is used to detect the mutation time point. Assume H0, indicating that the data does not change during the data transmission process and the data conforms to the normal distribution; Assume H1, indicating that the data changes during the data transmission process and the data distribution is shifted. The log-likelihood ratio is calculated as follows:

[0125]

[0126] Among them, Λ(y e ) represents the log-likelihood ratio of the e-th data, Λ(y e ) is large, indicating that the data is more consistent with hypothesis H1, that is, the data has shifted significantly. e ) means that under hypothesis H1, data y e Likelihood function, L(H0|y e ) indicates that under the hypothesis H0, the data y e The likelihood function of the normal distribution is as follows:

[0127]

[0128] Among them, μ represents the sample mean of the operating parameter sequence collected in the preset time period, σ 2 represents the variance of the operating parameter sequence collected during the preset time period, and Y represents the operating parameter sequence collected during the preset time period.

[0129] Calculate the log-likelihood ratio of the data at each moment and calculate the cumulative sum S for each data point e , as shown below:

[0130] S e =max(0,S e-1 +Λ(y e )-h) (11);

[0131] Thus, the difference between the cumulative sum of log-likelihood ratios at time t0 and the minimum value of the cumulative sum of log-likelihood ratios before time t0 is It can be used to describe the change process of the operating parameter sequence, that is, It is the basis for judging whether the data change exceeds the threshold, as shown in the following formula:

[0132]

[0133] in, Represents the cumulative sum of log-likelihood ratios at time t0.

[0134] It is understandable that in When it is greater than the decision value h, it indicates that the data at time t0 in the operating parameter sequence collected during the preset time period has changed. When it is less than or equal to h, it indicates that there is no change in the data in the operating parameter sequence collected during the preset time period.

[0135] By starting from the perspective of sensor abnormal data, while adapting to on-site discrete process data, removing the data fitting function processing steps, and adopting a data-driven sensor abnormality recognition method, the causes of sensor abnormal data and the corresponding abnormality types can be identified efficiently and timely, thereby improving the accuracy of sensor abnormality recognition and facilitating equipment maintenance.

[0136] Figure 5 A schematic diagram of monitoring operating parameters provided in an embodiment of the present application.

[0137] like Figure 5 As shown in the figure, the horizontal axis represents time in milliseconds, and the vertical axis represents pressure in MPa. The target oil and gas medium's pressure safety operating threshold is 0-11.8 MPa. At 527 milliseconds, the target oil and gas medium's pressure exceeded the upper limit of 11.8 MPa, indicating a data anomaly and issuing an abnormal control instruction.

[0138] However, if a sensor fails or is attacked by a cyberattack, and the temperature and pressure of the target oil and gas medium collected do not exceed the corresponding upper limits, the PI controller may not perform feedback control. In this case, the abnormal data has no physical impact on the system itself. In this case, the sensor failure cannot be detected, and the cyberattack on the sensor is deemed invalid by Jiangbei. As a result, the impact of abnormal data on the system itself may become increasingly greater over time, leading to safety incidents.

[0139] Considering the above problems, we can extract the time series features of sensor abnormal data and determine the abnormal type based on the time series features of abnormal data. Figure 1 In the intelligent identification system for oil and gas pipeline sensor failures shown in Figure 101, the data obtained by the second device is redundant with the data from the first device. During a cyberattack on the sensor, the injected false data occurs while the sensor is transmitting data to the first device. Therefore, even when the sensor itself is functioning normally, the second device can still output data that is close to the real data, effectively generating authentic data, even when the sensor is under cyberattack. Based on this, the distinction between sensor attacks and sensor failures can be based on the following: when the sensor is under cyberattack, the first device obtains false data, while the second device obtains authentic data. During a sensor failure, both the first and second devices obtain abnormal data. In other words, the timing of the abnormal data is a key factor in determining the type of abnormality.

[0140] Figure 6 This is another flow chart of a method for intelligently identifying faults in oil and gas pipeline network sensors provided in an embodiment of the present application. Figure 6 The method step S103 includes the following steps:

[0141] S1031: When both the first mutation time point and the second mutation time point are not empty, calculate and obtain the absolute value of the time difference between the first mutation time point and the second mutation time point.

[0142] Specifically, when the first mutation time point of the first device and the second mutation time point of the second device are both not empty, it is determined that the sensor is abnormal, and the absolute value of the time difference between the first mutation time point and the second mutation time point is calculated.

[0143] S1032: When the absolute value of the time difference is less than a second threshold, determine that the abnormality type is a sensor failure.

[0144] Specifically, the absolute value of the time difference between the first mutation time point and the second mutation time point is compared with a second threshold value. When the absolute value of the time difference is less than the second threshold value, the abnormality type of the sensor is determined to be a sensor failure.

[0145] S1033: When the absolute value of the time difference is greater than or equal to the second threshold, determine that the abnormality type is that the sensor is under a network attack.

[0146] Specifically, when the absolute value of the time difference between the first mutation time point and the second mutation time point is greater than or equal to a second threshold, it is determined that the abnormality type of the sensor is that the sensor is under a network attack.

[0147] Assume that the system operation time is T', the sampling frequency of the sensor is f, and the operating parameters of the first device are P feed (t), the operating parameters of the second device are P monitor (t), P feed (t) and P monitor (t) Introducing the change point statistics (NP-CUSUM) model, the cumulative sum of the log-likelihood ratios of the first device and the second device are S feed (t), S monitor (t), based on the cumulative sum of log-likelihood ratios S feed (t), S monitor (t), determine the first mutation time point T1 and the second mutation time point T2, and calculate the absolute value of the time difference δ between the first mutation time point and the second mutation time point, δ = |T1-T2|.

[0148] Specifically, the second threshold is set to σ. When the absolute value of the time difference δ is less than σ, the anomaly is determined to be a sensor failure. When the absolute value of the time difference δ is greater than or equal to σ, the anomaly is determined to be a sensor cyberattack. σ can be set based on actual conditions. For example, σ is set to 0.1T.

[0149] It is understandable that when there is no data mutation time point in the operating parameters of the first device and the second device, it is determined that there is no abnormality.

[0150] Each model simulation captures T' / f transient pressure values ​​(also known as pressure operating parameters). In the event of a sensor failure, both the first and second devices capture abnormal data. Mutation time points T1 and T2 for the first and second devices must exist, and the difference in the time points δ between the first and second devices is relatively small. In the event of a network attack on the sensor, the operating parameters of the second device can be considered normal, while the operating parameters of the first device will undergo a significant mutation at some point. Consequently, the difference δ between the first mutation time point T1 and the second mutation time point T2 is relatively large.

[0151] The embodiments of the present application can model sensor attacks and faults, extract the timing characteristics of controller output data, and determine intelligent identification of abnormality types based on real-time discrete operating parameters, further improving the information security of the gas transmission system and providing data basis for sensor maintenance.

[0152] Figure 7 A diagram of an application scenario of the intelligent identification method for oil and gas pipeline network sensor faults provided in an embodiment of the present application.

[0153] See also Figure 7 The horizontal axis represents time in ms, and the vertical axis represents pressure in MPa. The pressure safety operation threshold is 0-11.8 MPa. At 527 ms, since the pressure value is greater than the upper limit of 11.8 KPa, the cumulative sum of log-likelihood ratios S calculated based on the operating parameters in the first device is feed (t) A sudden change occurred. At 589ms, when the pressure value was greater than the upper limit of the pressure safety operation of 11.8kPa, the cumulative sum of the log-likelihood ratios S calculated based on the operating parameters in the second device monitor (t) A mutation occurs. Based on the first mutation time point of the first device and the second mutation time point of the second device, it can be determined that the abnormality type is a physical fault of the sensor.

[0154] Figure 8 Another application scenario diagram of the oil and gas pipeline network sensor fault intelligent identification method provided in an embodiment of the present application.

[0155] See also Figure 8 The horizontal axis represents time in ms, and the vertical axis represents pressure in KPa. The pressure safety operation threshold is 0-11.8MPa. At 378ms, since the pressure value is greater than the upper limit of 11.8KPa, the cumulative sum of log-likelihood ratios S calculated based on the operating parameters in the first device is feed(t) a sudden change occurs. The cumulative sum of log-likelihood ratios S calculated based on the operating parameters in the second device feed (t) is a mutation that occurs immediately after the system is started. Based on the first mutation time point of the first device and the second mutation time point of the second device, it can be determined that the abnormality type is that the sensor is under a network attack.

[0156] In some embodiments, determining the abnormality type based on the mutation time point further includes:

[0157] When both the first mutation time point and the second mutation time point are empty, it is determined that the sensor operates normally.

[0158] Specifically, when the first mutation time point determined based on the first operating parameter of the first device and the second mutation time point determined based on the second operating parameter of the second device are both empty, it is determined that there is no data mutation in the operating parameters of the first device and the second device, that is, the sensor is operating normally.

[0159] The embodiment of the present application identifies the time series characteristics of sensor abnormal data and determines the cause of the abnormal type, thereby quickly and accurately identifying the sensor abnormality type based on the difference in the data mutation time points of two devices, thereby improving the security of the system.

[0160] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0161] It should be further noted that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0162] Figure 9 This is a schematic diagram of the structure of an intelligent device for identifying faults in oil and gas pipeline network sensors provided in an embodiment of the present application. Figure 9, intelligent identification device for oil and gas pipeline network sensor faults, including:

[0163] The first determination module 901 is configured to determine a threshold range of safe production process data of the target oil and gas medium based on the physical parameters of the target oil and gas medium;

[0164] A second determining module 902 is configured to obtain an operating parameter, and when the operating parameter is within the threshold range of the production safety process data, determine a mutation time point based on the operating parameter;

[0165] The third determining module 903 is configured to determine an abnormality type based on the mutation time point; wherein the abnormality type includes a sensor failure or a sensor being attacked by a network.

[0166] In some embodiments, the target oil and gas medium includes a first substance and a second substance; the physical parameter includes a first physical parameter of the first substance and a second physical parameter of the second substance; the first determination module 901 includes:

[0167] a first calculation unit, configured to calculate a mixing parameter of the target oil-gas medium based on the first physical parameter and the second physical parameter;

[0168] a first determining unit, configured to determine a relationship ratio between temperature and pressure based on the mixing parameter;

[0169] The second determination unit is used to determine the safety production process data threshold interval of the target oil and gas medium when the relationship ratio of the temperature and pressure is less than the first threshold; wherein the safety production process data threshold interval includes at least one of a target temperature interval and a target pressure interval.

[0170] Optionally, the second determining module 902 includes:

[0171] An acquisition unit, used for acquiring operating parameters;

[0172] A second calculation unit is configured to calculate a cumulative sum of log-likelihood ratios at each moment according to each of the operating parameters within a preset time period when the operating parameter is within the safety production process data threshold interval;

[0173] The third determining unit is configured to determine the mutation time point based on the cumulative sum of the log-likelihood ratios at the respective moments.

[0174] Optionally, the operating parameters include a first operating parameter of the first device and a second operating parameter of the second device; the mutation time point includes a first mutation time point of the first device and a second mutation time point of the second device;

[0175] The third determining module 903 includes:

[0176] a third calculating unit, configured to calculate an absolute value of a time difference between the first mutation time point and the second mutation time point when both the first mutation time point and the second mutation time point are not empty;

[0177] a fourth determining unit, configured to determine that the abnormality type is a sensor failure when the absolute value of the time difference is less than a second threshold;

[0178] or,

[0179] The fifth determining unit is configured to determine, when the absolute value of the time difference is greater than or equal to the second threshold, that the abnormality type is that the sensor is under a network attack.

[0180] Optionally, the third determining module 903 further includes:

[0181] The sixth determining unit is configured to determine that the sensor operates normally when both the first mutation time point and the second mutation time point are empty.

[0182] Optionally, the safety production process data threshold interval includes an upper boundary and a lower boundary; the second determination module 902 includes:

[0183] A generating unit is configured to generate an alarm message when the operating parameter is less than the lower limit or greater than the upper limit; wherein the alarm message is used to indicate abnormal operation of the sensor.

[0184] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present application may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0185] Figure 10 This is a schematic diagram of the structure of the storage device provided in the embodiment of the present application. Figure 10 As shown, the storage device may include: a transceiver 1001 , a processor 1002 , and a memory 1003 .

[0186] The processor 1002 executes the computer-executable instructions stored in the memory, so that the processor 1002 performs the solution in the above embodiment. The processor 1002 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0187] The memory 1003 is connected to the processor 1002 via a system bus and communicates with the processor 1002. The memory 1003 is used to store computer program instructions.

[0188] The transceiver 1001 can be used to detect physical parameters, operating parameters, etc. of the target oil and gas medium.

[0189] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, among others. The system bus can be divided into an address bus, a data bus, a control bus, and so on. For ease of illustration, the figure shows only one thick line, but this does not imply that there is only one bus or only one type of bus. Transceivers are used to enable communication between the database access device and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and non-volatile memory.

[0190] An embodiment of the present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes the technical solution of the method for intelligently identifying oil and gas pipeline network sensor faults in the above embodiment.

[0191] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, it can implement the technical solution of the oil and gas pipeline network sensor fault intelligent identification method in the above embodiment.

[0192] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0193] If the integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0194] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0195] In the above embodiments, the description of each embodiment has its own focus. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0196] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0197] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. An intelligent identification method for oil and gas pipeline network sensor faults, characterized in that: include: Determining a threshold range of safe production process data of the target oil and gas medium based on physical parameters of the target oil and gas medium; Acquiring an operating parameter, and when the operating parameter is within the threshold range of the safe production process data, determining a mutation time point based on the operating parameter; Based on the mutation time point, the abnormality type is determined; wherein the abnormality type includes sensor failure or sensor being attacked by a network.

2. The method according to claim 1, characterized in that The target oil and gas medium includes a first substance and a second substance; the physical parameters include a first physical parameter of the first substance and a second physical parameter of the second substance; The step of determining a threshold range of safe production process data of the target oil and gas medium based on the physical parameters of the target oil and gas medium includes: Calculating a mixing parameter of the target oil-gas medium based on the first physical parameter and the second physical parameter; determining a relationship ratio of temperature and pressure based on the mixing parameter; When the relationship ratio of the temperature and pressure is less than a first threshold, a safety production process data threshold interval of the target oil and gas medium is determined; wherein the safety production process data threshold interval includes at least one of a target temperature interval and a target pressure interval.

3. The method according to claim 1, characterized in that The acquiring of the operating parameters, and determining the mutation time point based on the operating parameters when the operating parameters are within the safety production process data threshold range, includes: Get running parameters; When the operating parameter is within the threshold range of the safe production process data, the cumulative sum of the log-likelihood ratios at each moment is calculated based on the operating parameters within a preset time period; The mutation time point is determined based on the cumulative sum of the log-likelihood ratios at the various moments.

4. The method according to claim 3, characterized in that The operating parameters include a first operating parameter of the first device and a second operating parameter of the second device; the mutation time point includes a first mutation time point of the first device and a second mutation time point of the second device; The determining of the abnormality type based on the mutation time point includes: When both the first mutation time point and the second mutation time point are not empty, calculating the absolute value of the time difference between the first mutation time point and the second mutation time point; When the absolute value of the time difference is less than a second threshold, determining that the abnormality type is a sensor failure; or, When the absolute value of the time difference is greater than or equal to the second threshold, it is determined that the abnormality type is that the sensor is under a network attack.

5. The method according to claim 4, characterized in that The determining of the abnormality type based on the mutation time point further includes: When both the first mutation time point and the second mutation time point are empty, it is determined that the sensor operates normally.

6. The method according to any one of claims 1 to 5, characterized in that The safety production process data threshold interval includes an upper boundary and a lower boundary; after obtaining the operating parameters, it includes: When the operating parameter is less than the lower limit or greater than the upper limit, an alarm message is generated; wherein the alarm message is used to indicate that the sensor is operating abnormally.

7. An intelligent device for identifying faults in oil and gas pipeline network sensors, characterized in that: include: A first determining module is configured to determine a threshold range of safe production process data of a target oil and gas medium based on physical parameters of the target oil and gas medium; A second determination module is configured to obtain an operating parameter, and when the operating parameter is within the threshold range of the safe production process data, determine a mutation time point based on the operating parameter; The third determination module is configured to determine an abnormality type based on the mutation time point; wherein the abnormality type includes a sensor failure or a sensor being attacked by a network.

8. A storage device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

Images