An oil field pipeline inspection system and method

Abstract

The present application relates to the technical field of pipeline inspection, and particularly relates to an oil delivery station pipeline inspection system and method, which comprises an intelligent point inspection module and an intelligent cloud end inspection module, and comprises the intelligent point inspection module and the intelligent cloud end inspection module; the intelligent point inspection module is used for collecting operation data of each inspection point of the oil delivery station pipeline; the intelligent cloud end inspection module is used for performing risk assessment on the oil delivery station pipeline according to the operation data collected by the intelligent point inspection module to obtain a risk assessment result. The problems in the background art can be effectively solved: the traditional operation book and sign-in book working mode is not conducive to the supervision of the work of the inspectors, so that the occurrence of less inspection and missed inspection cannot be guaranteed, and is not conducive to the modern and scientific management of enterprises, and meanwhile, the existing point inspection system is not conducive to the rapid alarm of the sudden conditions of the pipeline, so that a large number of sudden accidents occur.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline inspection, in particular to an oil delivery station pipeline inspection system and method. BACKGROUND

[0002] With the device inspection technology, the device and pipeline can be judged without stopping and disassembling the station device, and the type, position and development trend of the fault can be further diagnosed, which provides a scientific basis for correct maintenance decision, thereby mastering the initiative of device maintenance management, increasing production and improving efficiency for enterprises.

[0003] The device maintenance based on inspection is a point inspection management system developed by Japan on the basis of introducing the preventive maintenance system of the United States. This modern maintenance management system taking point inspection as the core in the device operation stage is also called device full maintenance, and its main advantages are: maintenance cost is reduced by 20%-30%; device failure is greatly reduced; planned maintenance is strengthened, and maintenance efficiency is improved; device operation efficiency and production efficiency are greatly improved, so this device management method has been widely applied.

[0004] However, the traditional operation book and sign-in book working method is not conducive to supervising the work of the inspectors, so that it cannot guarantee the occurrence of less inspection and missed inspection, and is not conducive to the modern and scientific management of enterprises, and the existing inspection system is not conducive to rapid alarm for the sudden situation of the pipeline, so that a large number of sudden accidents occur. SUMMARY

[0005] The present application relates to the technical field of pipeline inspection, in particular to an oil delivery station pipeline inspection system and method.

[0006] The technical scheme of the oil delivery station pipeline inspection system of the present application is as follows:

[0007] The system comprises an intelligent inspection module and an intelligent cloud inspection module.

[0008] The intelligent inspection module is used for collecting operation data of each inspection point of the oil delivery station pipeline.

[0009] The intelligent cloud inspection module is used for risk assessment of the oil delivery station pipeline according to the operation data collected by the intelligent inspection module, and obtaining a risk assessment result.

[0010] The technical scheme of the oil delivery station pipeline inspection method of the present application is as follows:

[0011] establish an influence model formula, the influence model formula is: Wherein, Ai is the parameter of the comprehensive importance degree of the i th risk factor, Yn is the risk value, Ni is the i th data standardization strategy index;

[0012] Set the parameter Ai of the comprehensive importance degree of the risk factor in the influence model formula;

[0013] Collect operation data by using the pipeline inspection system of the oil delivery station of any one of the above, and the collected value is obtained by data comparison and cleaning to obtain a collected true value ;

[0014] The collected true value is imported into a data standardization strategy index formula, and the data standardization strategy index formula is: Wherein, Ni is the i th data standardization strategy index, is the maximum value of the safety range corresponding to the collected true value, is the minimum value of the safety range corresponding to the collected true value, The meaning of and is the value closest to , and Ni is imported into the influence model formula to obtain a risk value Yn;

[0015] The risk value Yn is compared with a risk threshold Y to perform risk alarm. The beneficial effects of the present application are as follows:

[0016] The problems in the background art can be effectively solved: the traditional operation book and sign-in book working method is not conducive to the supervision of the inspection personnel work, so that the occurrence of less inspection and missed inspection cannot be guaranteed, and is not conducive to the modern scientific management of enterprises, and the existing point inspection system is not conducive to the rapid alarm of the sudden situation of the pipeline, so that a large number of sudden accidents occur. BRIEF DESCRIPTION OF DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0018] Figure 1 is a structural schematic view of an oil delivery station pipeline inspection system according to an embodiment of the present application;

[0019] Figure 2 is a schematic view of an intelligent point inspection unit of an oil delivery station pipeline inspection system based on machine learning according to an embodiment of the present application;

[0020] Figure 3 is a principle block diagram of a machine learning and data analysis unit of an oil delivery station pipeline inspection system based on machine learning according to an embodiment of the present application; DETAILED DESCRIPTION

[0021] As Figure 1 shown, the oil pipeline inspection system of the embodiment of the application comprises an intelligent point inspection module and an intelligent cloud inspection module.

[0022] The intelligent point inspection module is configured to collect operation data of each inspection point of the oil pipeline.

[0023] The intelligent cloud inspection module is configured to perform risk assessment on the oil pipeline based on the operation data collected by the intelligent point inspection module to obtain a risk assessment result.

[0024] Optionally, in the above technical solution, the network transmission module is further configured to send the operation data collected by the intelligent point inspection module to the intelligent point inspection module.

[0025] That is, the oil pipeline inspection system of the embodiment of the application comprises an intelligent point inspection module, a network transmission module and an intelligent cloud inspection module. The intelligent point inspection module is configured to collect data of inspection points of the oil pipeline. The network transmission module is configured to detect data transmission between the terminal and the intelligent cloud inspection module. The intelligent cloud inspection module is configured to receive data collected by the detection terminal, analyze and sort the data, and perform risk assessment on the oil pipeline in combination with the collected point data. The collected data includes temperature, noise, vibration, thickness and various gas concentration data. The analysis and sorting of the data includes data classification, that is, classifying the collected data according to data types. The data is also cleaned. The data cleaning process includes extracting data of a detection point, extracting parameters exceeding the safe range, extracting data of corresponding parameters of upstream and downstream inspection points, calculating the average value of the data of the corresponding parameters of the upstream and downstream inspection points, and determining that the parameter value exceeding the safe range is greater than 50% of the average value if the parameter value is a false value.

[0026] Optionally, in the above technical solution, the intelligent point inspection module comprises at least one intelligent point inspection device, and the intelligent point inspection device comprises a data collection unit, a communication unit, a main control unit and a positioning unit.

[0027] The data collection unit is configured to collect operation data of the inspection points of the oil pipeline. The positioning unit is configured to record a detection track of the intelligent point inspection device. The main control unit is configured to monitor the operation of the data collection unit. The communication unit is configured to perform data transmission between the intelligent point inspection device and the network transmission module.

[0028] The data acquisition unit is used for acquiring data information of a pipeline acquisition point, the positioning unit is used for recording a detection track of the intelligent point inspection device, the main control unit is used for monitoring an operation condition of the data acquisition unit, and the communication unit is used for signal transmission between the intelligent point inspection device and a network transmission module, so that the detection track of the point inspection device is recorded to acquire position information, which is beneficial to pushing work to be carried out at the position according to the position, and real-time display of maintenance and repair operation plans of various devices, and the wireless camera records operation steps in real time, wherein the operation data specifically includes operation control data and self-fault judgment data, and recording the track is beneficial to quickly acquiring data of a specified acquisition point, the communication unit constructs a wifi transmission network to transmit data, and movement and detection of the intelligent point inspection device are working principles of a robot, and specific details are known to those skilled in the art, and are not described herein.

[0029] Optionally, in the above technical solution, the data acquisition unit comprises an infrared temperature acquisition subunit, a noise acquisition subunit and a vibration acquisition subunit.

[0030] The infrared temperature acquisition subunit is used for acquiring temperature data information in a pipeline of an oil transportation station, and specifically:

[0031] The infrared temperature acquisition subunit is used for acquiring temperature data information in the pipeline, and converts infrared radiation energy into corresponding electrical signals through an optical system and focusing on a photoelectric detector, the signals are converted into temperature values of a measured target after being corrected by an amplifier and a signal processing circuit and a correction system contained therein.

[0032] The noise acquisition subunit is used for measuring noise of the pipeline and equipment of the oil transportation station, and specifically:

[0033] The noise acquisition subunit is used for measuring noise of the pipeline and equipment, and converts various noise signals generated in an industrial field into electrical signals through a noise sensor, and the electrical signals are converted into digital signals after passing through a signal conditioning circuit.

[0034] The vibration acquisition subunit is used for measuring a vibration value of the pipeline of the oil transportation station.

[0035] Optionally, in the above technical solution, the data acquisition unit further comprises an ultrasonic thickness measurement acquisition subunit and a gas detection subunit.

[0036] The ultrasonic thickness measurement acquisition subunit is used for measuring a wall thickness of the pipeline of the oil transportation station, and specifically:

[0037] The ultrasonic thickness measurement collecting subunit is used for measuring the pipe wall thickness. A high-pressure impact wave is generated by a transmitting circuit to excite a probe, and an ultrasonic transmitting pulse wave is generated. The pulse wave is received by a receiving circuit after being reflected by a medium interface. After being counted and processed by a single-chip microcomputer, the thickness value is displayed on a liquid crystal display. The thickness of the sample is obtained by multiplying the propagation speed of the sound wave in the sample by half the time through the sample.

[0038] The gas detection subunit is used for measuring the concentration of various gases in the pipeline of the oil transportation station.

[0039] The latest wireless communication technology is set up to open the data transmission channel between the intelligent point inspection terminal and each handheld detector, realize the automatic uploading and receiving of detection data, improve the accuracy of data transmission and work efficiency, provide support for putting point inspection information processing into the computer management track, make the management work easy, efficient and low-cost, and make the equipment information numerical, and a multifunctional detector integrating vibration, wall thickness, temperature, distance and noise measurement is independently developed to realize the simplification of station point inspection operation equipment and the simplification of operation.

[0040] Optionally, in the above technical solution, the intelligent point inspection device further comprises an explosion-proof shell, and the data acquisition unit, the communication unit, the main control unit and the positioning unit are arranged in the explosion-proof shell.

[0041] Optionally, in the above technical solution, the intelligent point inspection device further comprises a temperature control unit, and the temperature control unit is used for controlling the temperature of the intelligent point inspection device during operation. Specifically,

[0042] The explosion-proof shell is attached to the outside of the intelligent point inspection device to protect the normal operation of the intelligent point inspection device, and the temperature control unit is used for controlling the temperature of the device during operation. In this way, the explosion-proof function is realized by integrating the main detection circuit of the multifunctional detector and various sensors designed and developed based on the intrinsically safe explosion-proof theory, and the temperature control unit is specifically an air conditioner temperature controller.

[0043] Optionally, in the above technical solution, the intelligent cloud end inspection module comprises an edge device management unit and a machine learning and data analysis unit.

[0044] The edge device management unit is used for providing the collection position and collection task attribute of the intelligent point inspection device, and the machine learning and data analysis unit is used for cleaning the operation data collected by the intelligent point inspection module, and analyzing the cleaned operation data by using a database comparison model and a machine learning model to obtain a risk assessment result of the pipeline of the oil transportation station. Specifically,

[0045] The intelligent cloud inspection module comprises an edge device management unit and a machine learning and data analysis unit, the edge device management unit is configured to provide a collection position and a collection task attribute of the intelligent point inspection device, the machine learning and data analysis unit is configured to compare a database reference model with a machine learning model, and analyze and clean the collected data to obtain a risk value and a data model, so that the collected data is imported and stored and updated by using the machine learning mode, the collection position is a position coordinate of a collection point, the collection task attribute is one or more of collection data types of the intelligent point inspection device, and the data model is specifically a three-dimensional model formed by importing the collection data of the collection point on a constructed three-dimensional image of a pipeline.

[0046] Optionally, in the technical solution, the machine learning and data analysis unit comprises a data comparison subunit, a data standardization subunit, a data modeling subunit and a data cleaning subunit, the data comparison subunit is configured to compare the collected data with stored data and collected data of nearby collection points, the data standardization subunit is configured to standardize the data by inputting the data into a unified standard model, the data modeling subunit is configured to establish a data model by importing the standardized data into an influence model formula, and the data cleaning subunit is configured to clean error values in the data.

[0047] The data comparison subunit is configured to compare the collected data with stored data and collected data of nearby collection points, the data standardization subunit is configured to standardize the data by inputting the data into a unified standard model, the data modeling subunit is configured to establish a data model by importing the standardized data into an influence model formula, and the data cleaning subunit is configured to clean error values in the data.

[0048] Optionally, in the technical solution, the data standardization subunit further comprises a data standardization strategy, and the data standardization strategy index formula is wherein, Ni is the i th data standardization strategy index, is a maximum value of a safety range corresponding to a collection true value, is a minimum value of the safety range corresponding to the collection true value, the meaning of and the value closest to is introduced into the influence model formula, wherein, N final value is rounded to an integer.

[0049] The oil field pipeline inspection system is described by the following embodiments.

[0050] The embodiment realizes intelligent inspection of people by means of a "pad + sensor" mode, the pad solves the problems of edge computing and data transmission, the sensor solves the problems of seeing and hearing, and the people serve as a mobile carrier to realize movement in the station yard, the positioning device built in the pad realizes recording of the movement track, the pad pushes the work to be carried out at the position according to the position, real-time display of the maintenance and repair operation plan of each device, real-time recording of the operation steps by the wireless camera, realization of operation according to the regulations, recording according to the regulations, intelligent video monitoring, and the specific scheme is as follows: Figure 1 Figure 3 As shown in the figure, an oil delivery station pipeline inspection system based on machine learning includes an intelligent point inspection module, a network transmission module, and an intelligent cloud inspection module, the intelligent point inspection module is used for collecting data of the inspection points of the oil delivery station pipeline, the network transmission module is used for detecting data transmission between the terminal and the intelligent cloud inspection module, and the intelligent cloud inspection module is used for accepting data collected by the detection terminal, analyzing the data as a whole, and combining the collected point data to evaluate the risk of the oil delivery station pipeline.

[0051] The intelligent detection module includes at least one intelligent point inspection device, the intelligent point inspection device includes a data acquisition unit, a communication unit, an explosion-proof shell, a main control unit, a positioning unit, and a temperature control unit, the data acquisition unit is used for collecting data information of the pipeline collection points, the positioning unit is used for recording the detection track of the intelligent point inspection device, the main control unit is used for monitoring the operation of the data acquisition unit, and the communication unit is used for signal transmission between the intelligent point inspection device and the network transmission module, so as to record the detection track of the point inspection device and collect position information, which is beneficial to push the work to be carried out at the position according to the position, real-time display of the maintenance and repair operation plan of each device, real-time recording of the operation steps by the wireless camera, and separation of the device and the pad, the Beidou (GPS) positioning device built in the pad realizes recording of the movement track.

[0052] The intelligent cloud inspection module includes an edge device management unit and a machine learning and data analysis unit, the edge device management unit is used for providing the collection position and collection task attribute of the intelligent point inspection device, the machine learning and data analysis unit is used for comparison between a database model and a machine learning model, data analysis and data cleaning of the collected data, and derivation of a risk value and a data model, so that the collected data is imported, and the data is stored and updated in the manner of machine learning;

[0053] ​The data acquisition unit includes an infrared temperature acquisition subunit, a noise acquisition subunit, a vibration acquisition subunit, an ultrasonic thickness measurement acquisition subunit and a gas detection subunit. The infrared temperature acquisition subunit is used for acquiring temperature data information in the pipeline. The infrared radiation energy is collected through an optical system, focused on a photoelectric detector and converted into a corresponding electrical signal. The signal is amplified by an amplifier and a signal processing circuit, and is converted into a temperature value of the measured target after being corrected by a correction system contained therein. The noise acquisition subunit is used for measuring the noise of the pipeline and equipment. Various noise signals generated in the industrial field are converted into electrical signals by a noise sensor. The electrical signals enter a noise data acquisition device after being processed by a signal conditioning circuit, and are converted into digital signals. The vibration acquisition subunit is used for measuring the vibration value of the pipeline. The ultrasonic thickness measurement acquisition subunit is used for measuring the wall thickness of the pipeline. A high-pressure shock wave generated by a transmitting circuit excites the probe to generate an ultrasonic emission pulse wave. The pulse wave is received by a receiving circuit after being reflected by the medium interface. The thickness value is displayed on a liquid crystal display after being counted and processed by a single-chip microcomputer. The thickness of the sample is obtained by multiplying the propagation speed of the sound wave in the sample by half the time through the sample. The gas detection subunit is used for measuring the concentration of various gases in the pipeline. The latest wireless communication technology is used to open the data transmission channel between the intelligent point inspection terminal and each handheld detector, to realize automatic uploading and receiving of detection data, to improve the accuracy of data transmission and work efficiency, to provide support for point inspection information processing into computer management track, to make management work easy, efficient and low-cost, to make equipment information numerical, to independently develop a multifunctional detector integrating vibration, wall thickness, temperature, distance and noise measurement, to realize field point inspection operation equipment simplification and operation simplification, and to integrate all sensors, main control boards, analog quantity acquisition cards and lithium batteries on the equipment. The equipment has five gas sensors and can simultaneously detect the concentration of all equipped gases, including oxygen volume concentration detection, hydrogen sulfide millionth concentration detection, carbon monoxide millionth concentration detection and methane gas volume concentration detection. The user can customize and query the parameters and data of the equipment, and the operation is simple and humanized. The equipment has data storage and data export temperature and humidity detection functions.

[0054] The explosion-proof shell is attached to the outside of the intelligent point inspection equipment to protect the normal operation of the intelligent point inspection equipment. The temperature control unit is used to control the temperature of the equipment during operation. The multifunctional detector developed by using the intrinsic safety type explosion-proof theory integrates the main detection circuit and various sensors to realize the explosion-proof function. The integrated box takes certain warming measures to ensure the normal start and use of the flat panel,

[0055] The sensor and the main control board meet the normal start and use requirements.

[0056] The machine learning and data analysis unit comprises a data comparison subunit, a data standardization subunit, a data modeling subunit and a data cleaning subunit, the data comparison subunit is used for comparing the collected data with the stored data and the data collected by nearby collection points, the data standardization subunit is used for standardizing the data by inputting the data into a unified standard model, the data modeling subunit is suitable for importing the standardized data into an influence model formula to establish a data model, and the data cleaning subunit is used for cleaning error values in the data, so that the collected data is analyzed and cleaned;

[0057] The data standardization subunit further comprises a data standardization strategy, and the data standardization strategy index formula is The data standardization strategy index is Ni, is a maximum value of a safety range corresponding to a collected real value, is a minimum value of the safety range corresponding to the collected real value, The meaning of Ni is and the value closest to , and Ni is introduced into the influence model formula, wherein, the final value of N is rounded to an integer.

[0058] Through the embodiment, the intelligent inspection of people can be realized by using the mode of "tablet + sensor", the tablet solves the problems of edge computing and data transmission, the sensor solves the problems of seeing and hearing, the people serve as a mobile carrier to realize movement in the station, the positioning device built in the tablet records the movement track, the tablet pushes the work to be carried out at the position according to the position, the work plan of maintenance and repair of each device is displayed in real time, the operation steps are recorded in real time by the wireless camera, the operation according to the regulations is realized, the recording according to the regulations is realized, and intelligent video monitoring is realized.

[0059] The oil delivery station pipeline inspection method provided by the embodiment of the application comprises the following steps:

[0060] S1, an influence model formula is established, and the influence model formula is as follows: Wherein, Ai is a parameter of the comprehensive importance degree of the i th risk factor, Yn is a risk value, and Ni is a data standardization strategy index of the i th.

[0061] S2, the parameter Ai of the comprehensive importance degree of the risk factor in the influence model formula is set.

[0062] S3, the running data is collected by using the oil delivery station pipeline inspection system, and the collected value is compared and cleaned to obtain a collected real value .

[0063] S4, the collected real value is input into the influence model formula to obtain a risk value The data standardization strategy index formula is introduced as follows: wherein Ni is the ith data standardization strategy index, is the maximum value of the safety range corresponding to the collected real value, is the minimum value of the safety range corresponding to the collected real value, has the meaning of and the value closest to , and Ni is introduced into the influence model formula to obtain a risk value Yn;

[0064] S5, comparing the risk value Yn with a risk threshold Y to perform risk alarm.

[0065] The following embodiments are used for detailed explanation, specifically including:

[0066] S101, establishing an influence model formula;

[0067] S102, setting a parameter Ai based on the comprehensive importance degree of risk factors, wherein the comprehensive importance degree of risk factors is obtained by scoring the importance degree of collected data by 500 experts, the score being 1-10, and then averaging the scores of the collected data to obtain the parameter of the collected data;

[0068] S103, collecting data of a patrol system, and the collected value is subjected to data comparison and cleaning to obtain a collected real value;

[0069] The process of data comparison and cleaning is that, for a detection point data, the parameter exceeding the safety range is extracted, and the data of the corresponding parameters of the upstream and downstream checkpoints are extracted, the average value of the data of the corresponding parameters of the upstream and downstream checkpoints is calculated, if the parameter value exceeding the safety range is greater than 50% of the average value, it is considered that this is a detection error value, and if the parameter value exceeding the safety range is less than or equal to 50% of the average value, it is considered that this is a collected real value.

[0070] The collected value of the patrol system is each data, which is noise intensity data, pipeline vibration data, pipeline thickness data and gas concentration data, and the real values of these data constitute a data sequence (Yn), wherein is the ith item of the data sequence, and n is the number of data items;

[0071] S104, collecting the real value The data standardization strategy index formula is introduced as follows: wherein Ni is the ith data standardization strategy index, is the maximum value of the safety range corresponding to the collected real value, is the minimum value of the safety range corresponding to the collected real value, ​The meaning of and The closest The value of Ni is introduced into the influence model formula;

[0072] S105, export risk value Yn;

[0073] S106, risk value Yn is compared with risk threshold Y, and risk alarm is performed.

[0074] In S101, the influence model formula is represented as Wherein, Ai is the parameter of the comprehensive importance degree of the i th risk factor, Yn is the risk value, and Ni is the i th data standardization strategy index.

[0075] Optionally, in S103, the specific steps of data comparison and cleaning are: the collected value is brought into the data standardization strategy index formula to obtain the data standardization strategy index Ni value, and for Ni value greater than 2, the data of the upstream collection point of the collection position and the downstream collection point of the collection position is imported to verify the accuracy of the collection value of the collection position, so that the “error value” in the collection value is checked and verified. Because the environment of some positions is relatively harsh, noise causes error values in the collected values.

[0076] Optionally, in S106, the step of arranging the risk values Yn of each monitoring point in descending order is further included, which is beneficial to arrange and alarm the risk values of the safety hazards detected by the point inspection equipment, so as to improve the processing speed of the risk.

[0077] Wherein, in S103, the specific steps of data comparison and cleaning are: the collected value is brought into the data standardization strategy index formula to obtain the data standardization strategy index Ni value, and for Ni value greater than 2, the data of the upstream collection point of the collection position and the downstream collection point of the collection position is imported to verify the accuracy of the collection value of the collection position, so that the “error value” in the collection value is checked and verified.

[0078] Optionally, in S106, the step of arranging the risk values Yn of each monitoring point in descending order is further included, which is beneficial to arrange and alarm the risk values of the safety hazards detected by the point inspection equipment, so as to improve the processing speed of the risk.

[0079] The data acquisition unit comprises an infrared temperature acquisition subunit, a noise acquisition subunit, a vibration acquisition subunit, an ultrasonic thickness measurement acquisition subunit and a gas detection subunit. The infrared temperature acquisition subunit is used for acquiring temperature data information in the pipeline. Infrared radiation energy is collected through an optical system, focused on a photoelectric detector and converted into a corresponding electrical signal. The signal is converted into a temperature value of the measured target after being corrected by a correction system contained in the amplifier and signal processing circuit. The noise acquisition subunit is used for measuring the noise of the pipeline and equipment. Various noise signals generated in the industrial field are converted into electrical signals by a noise sensor. The electrical signals are converted into digital signals after passing through a signal conditioning circuit and entering a noise data acquisition device. The vibration acquisition subunit is used for measuring the vibration value of the pipeline. The ultrasonic thickness measurement acquisition subunit is used for measuring the wall thickness of the pipeline. A high-pressure shock wave generated by a transmitting circuit excites a probe to generate an ultrasonic emission pulse wave. The pulse wave is received by a receiving circuit after being reflected by a medium interface. The thickness value is displayed on a liquid crystal display after being counted and processed by a single-chip microcomputer. The thickness of the sample is obtained by multiplying the propagation speed of the sound wave in the sample by half the time of passing through the sample. The gas detection subunit is used for measuring the concentration of various gases in the pipeline.

[0080] It should be noted that, according to the viewpoint of system theory, the risk emergence of a single risk factor node cannot cause an accident to occur; when risk transmission causes risk emergence of most nodes, the system will reach a critical collapse state, at which time any internal or external disturbance can cause an accident to occur. It can be seen that the system safety depends not only on the overall risk state, but also on the risk state of the internal direct cause node that cannot be disposed of. Therefore, the data of the upstream acquisition point of the collection position and the downstream acquisition point of the collection position are imported to verify the accuracy of the collection value of the collection position. In this way, the “wrong value” in the collection value is checked and verified.

[0081] The embodiment can realize the following: a pipeline inspection method is proposed, the collection value is introduced into the influence model formula, the risk value and the data model of the pipeline section are obtained, and the risk value is provided to the monitoring room. This is conducive to arranging the risk value of the safety hidden danger perceived by the point inspection equipment to alarm, thereby improving the processing speed of the risk.

[0082] It should be noted that the probability density function of risk emergence obeys a random exponential distribution with a certain mean value. When risk occurs, the risk probability at time T is where Yn(T) is the risk value at time T.

[0083] The embodiment is based on the above-mentioned pipeline inspection method for an oil pipeline station. The collection value is introduced into the influence model formula, the risk value and the data model of the pipeline section are obtained, and the risk value is provided to the monitoring room. This is conducive to arranging the risk value of the safety hidden danger perceived by the point inspection equipment to alarm, thereby improving the processing speed of the risk.

[0084] Compared with the prior art, the present application has the following beneficial effects:

[0085] 1) The present application realizes intelligent inspection of people by means of "pad + sensor", the pad solves the problems of edge computing and data transmission, the sensor solves the problems of seeing and hearing, the people serve as a mobile carrier to realize movement in the station yard, the positioning device built in the pad realizes recording of the movement track, the pad pushes the work to be carried out at the position according to the position, real-time display of the maintenance work plan of each device, real-time recording of the operation steps by the wireless camera, realization of operation according to the regulations, recording according to the regulations, and intelligent video monitoring.

[0086] 2) The present application proposes a pipeline inspection method, introduces the collected value into an influence model formula, obtains the risk value and data model of the pipeline section, and provides to the monitoring room, which is beneficial to arrange alarm according to the risk value of the safety hidden danger perceived by the point inspection equipment, so as to improve the processing speed of the risk.

[0087] In the above embodiments, although the steps are numbered S1, S2, etc., it is only a specific embodiment given by the present application, and those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present application. It can be understood that in some embodiments, some or all of the above embodiments can be included.

[0088] The implementation of the above steps of the pipeline inspection method of the oil delivery station can refer to the content in the above embodiment of the pipeline inspection system of the oil delivery station, which will not be repeated here.

[0089] Those skilled in the art know that the present application can be implemented as a system, a method or a computer program product.

[0090] Therefore, the present disclosure can be embodied in the form of a complete hardware, a complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present application can also be embodied in the form of a computer program product in one or more computer readable media, which contains computer readable program code.

[0091] Any combination of one or more computer readable medium can be utilized. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0092] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above embodiments are exemplary, and are not to be understood as limiting the present application, and that changes, modifications, substitutions and variations of the above embodiments can be made by those skilled in the art within the scope of the present application.

Claims

1. An oil field station pipeline inspection system, comprising: The intelligent point inspection module and the intelligent cloud inspection module are included. The intelligent point inspection module is used for collecting operation data of each inspection point of the oil transportation station pipeline. The intelligent cloud inspection module is used for performing risk assessment on the oil transportation station pipeline according to the operation data collected by the intelligent point inspection module to obtain a risk assessment result. The intelligent cloud inspection module includes an edge device management unit and a machine learning and data analysis unit. The edge device management unit is used for providing collection positions and collection task attributes of the intelligent point inspection device. The machine learning and data analysis unit is used for performing data cleaning on the operation data collected by the intelligent point inspection module, and performing analysis on the cleaned operation data by using a database comparison model and a machine learning model to obtain the risk assessment result of the oil transportation station pipeline. The machine learning and data analysis unit includes a data comparison subunit, a data standardization subunit, a data modeling subunit and a data cleaning subunit. The data comparison subunit is used for comparing collected data with stored data, and comparing the collected data with data collected by nearby collection points. The data standardization subunit is used for standardizing data by inputting the data into a unified standard model. The data modeling subunit is used for importing the standardized data into an influence model formula to establish a data model. The data cleaning subunit is used for cleaning error values in the data. The data standardization subunit further comprises a data standardization strategy, and an index formula of the data standardization strategy is wherein Ni is an index of the i th data standardization strategy, is a maximum value of a safety range corresponding to a real value collected, is a minimum value of a safety range corresponding to a real value collected, has the meaning of and the value closest to , the Ni is introduced into an influence model formula for risk assessment, and the influence model formula is: wherein Ai is a parameter of a comprehensive importance degree of the i th risk factor, Yn is a risk value, Ni is an index of the i th data standardization strategy, and Ai represents a parameter of a comprehensive importance degree of a risk factor in the influence model formula.

2. An oil terminal pipeline inspection system according to claim 1, wherein, A network transmission module is further included, and the network transmission module is used for sending the operation data collected by the intelligent point inspection module to the intelligent cloud inspection module.

3. An oil terminal pipeline inspection system according to claim 2, wherein, The intelligent point inspection module includes at least one intelligent point inspection device, and the intelligent point inspection device includes a data collection unit, a communication unit, a main control unit and a positioning unit. The data collection unit is used for collecting operation data of inspection points of the oil transportation station pipeline. The positioning unit is used for recording a detection track of the intelligent point inspection device. The main control unit is used for monitoring an operation condition of the data collection unit. The communication unit is used for performing data transmission between the intelligent point inspection device and the network transmission module.

4. An oil terminal pipeline inspection system according to claim 3, wherein, The data collection unit includes an infrared temperature collection subunit, a noise collection subunit and a vibration collection subunit. The infrared temperature collection subunit is used for collecting temperature data information in the oil transportation station pipeline. The noise collection subunit is used for measuring noise of the oil transportation station pipeline and equipment. The vibration collection subunit is used for measuring a vibration value of the oil transportation station pipeline.

5. An oil terminal pipeline inspection system according to claim 4, wherein, The data collection unit further includes an ultrasonic thickness measurement collection subunit and a gas detection subunit. The ultrasonic thickness measurement collection subunit is used for measuring a wall thickness of the oil transportation station pipeline. The gas detection subunit is used for measuring concentrations of various gases in the oil transportation station pipeline.

6. An oil terminal pipeline inspection system according to claim 5, wherein, The intelligent point inspection device further includes an explosion-proof shell, and the data collection unit, the communication unit, the main control unit and the positioning unit are arranged in the explosion-proof shell.

7. An oil terminal pipeline inspection system according to claim 6, wherein, The intelligent point inspection device further includes a temperature control unit, and the temperature control unit is used for controlling a temperature of the intelligent point inspection device during operation.

8. A method of inspecting a pipeline of an oil delivery site, characterized by, The influence model formula is established. ​ Setting the parameter Ai of the comprehensive importance degree of the risk factor in the influence model formula; The operation data is collected by using the oil field station pipeline inspection system of any one of claims 1 to 7, and the collected values are compared and cleaned to obtain collected true values ; The real value is collected Import the data standardization strategy index formula, import Ni into the influence model formula, and obtain the risk value Yn; Comparing the risk value Yn with the risk threshold value Y to make risk alarm.

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