Oil and gas transmission monitoring method and system based on artificial intelligence AI

Abstract

The embodiment of the invention discloses an oil and gas transmission monitoring method and system based on artificial intelligence AI. The method is executed by an edge controller of the oil and gas conveying system, and comprises the following steps: acquiring state parameters of conveying equipment of the oil and gas conveying system; generating a first feature map according to the state parameters of the conveying equipment of the oil and gas conveying system; wherein the first feature map is a three-dimensional feature map; the first feature map is formed by sequentially combining and stacking a plurality of two-dimensional feature maps; one two-dimensional feature map corresponds to one conveying device; generating a second feature map according to the first feature map; the second feature map is a three-dimensional feature map; the sorting of the feature elements in the second feature map is different from that of the feature elements in the first feature map; and generating a primary monitoring result according to the first feature map and the second feature map.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas transportation, and in particular, to an oil and gas transportation monitoring method and system based on Artificial Intelligence (AI). Background Art

[0002] With the increasing demand for energy, more and more oil and gas pipelines are being laid. Oil and gas are precious resources in themselves, and there may also be some corrosive gases in the oil and gas. Therefore, equipment is prone to corrosion and other phenomena when it ages. In some cases, manually monitoring a large-area oil and gas transportation system is costly and the accuracy of the monitoring results is not strong. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide an oil and gas transportation monitoring method and system based on Artificial Intelligence (AI). The technical solution of the present invention is implemented as follows: In a first aspect, an AI-based oil and gas transportation monitoring method is provided. The method is executed by an edge controller of an oil and gas transportation system, and the method includes: obtaining state parameters of a transportation device of the oil and gas transportation system; generating a first feature map according to the state parameters of the transportation device of the oil and gas transportation system; wherein the first feature map is a three-dimensional feature map; the first feature map includes a plurality of two-dimensional feature maps stacked in sequence; one of the two-dimensional feature maps corresponds to one of the transportation devices; generating a second feature map according to the first feature map; the second feature map is a three-dimensional feature map; the sorting of the feature elements in the second feature map is different from that of the feature elements in the first feature map; generating a primary monitoring result according to the first feature map and the second feature map.

[0004] In a second aspect, an AI-based oil and gas transportation monitoring method is provided, characterized in that the method is executed by a central controller of the oil and gas transportation system, and the method includes: receiving the primary monitoring result sent by the edge controller of the oil and gas transportation system; generating a fifth feature map according to the primary monitoring result; obtaining a secondary monitoring result according to the fifth feature map.

[0005] In a third aspect, an AI-based oil and gas transportation monitoring system is provided, characterized in that it includes: an edge controller for executing the method according to any technical solution of the first aspect; a central controller for executing the method according to any technical solution of the second aspect.

[0006] A fourth aspect provides an electronic device, comprising: a processor and a memory, where the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the AI-based oil and gas transportation monitoring method according to any one of the first aspect and / or the second aspect.

[0007] The technical solutions provided by the embodiments of the present invention at least have the following beneficial effects: By using an AI model for monitoring and predicting the oil and gas transportation system, the accuracy of the monitoring results can be improved and the labor cost can be reduced. Since the oil and gas transportation system is widely distributed and there are many transportation devices used, the monitoring effect can be greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings: Figure 1 It is a schematic structural diagram of an oil and gas pipeline provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of an oil and gas transportation system provided by an embodiment of the present invention; Figure 3 It is a schematic structural diagram of a control subsystem provided by an embodiment of the present invention; Figure 4 It is a schematic flowchart of a monitoring method for an oil and gas transportation system provided by an embodiment of the present invention; Figure 5 It is a schematic flowchart of a monitoring method for an oil and gas transportation system provided by an embodiment of the present invention; Figure 6 It is a schematic flowchart of another monitoring method for an oil and gas transportation system provided by an embodiment of the present invention Figure 7 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0010] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0011] As Figure 1 shown, an embodiment of the present disclosure provides an oil and gas pipeline, which includes a pipeline wall and a hollow channel inside the pipeline wall; the hollow channel is used for transporting oil and gas; the pipeline wall includes: a main layer 110; a bonding layer attached to the main layer 110; a protective layer attached to the bonding layer for protecting the oil and gas pipeline.

[0012] In some embodiments, the main layer 110 may be a metal layer. The main layer 110 is the main part of the pipeline wall. Exemplarily, the metal layer may be a steel pipe layer.

[0013] In some embodiments, the oil and gas pipeline may include a polymer plastic pipe with a metal mesh. Exemplarily, the plastic pipe may include, but is not limited to, a polyethylene pipe, a polyvinyl chloride pipe, or a fiberglass pipe. When the plastic pipe is formed, the metal mesh is placed in a forming mold and integrally formed with the pipe wall of the plastic pipe. The introduction of the metal mesh can enhance the strength of the plastic pipe. In some embodiments, the plastic pipe can be used in an oil and gas transportation system with a medium pipeline diameter or a small oil and gas transportation volume.

[0014] The pipeline wall is in a ring shape, and its main layer 110, bonding layer, and protective layer are all in a ring shape.

[0015] The bonding layer is mainly used to improve the adhesion between the protective layer and the main layer 110, reduce the detachment phenomenon of the protective layer from the main layer 110, extend the service life of the oil and gas pipeline, and thus reduce the monitoring and maintenance costs of the oil and gas pipeline. Exemplarily, through the selection of materials, the bonding layer can facilitate the recycling of the oil and gas pipeline. For example, the bonding layer can be easily detached from the main layer 110 by high-temperature pyrolysis, or the bonding layer can be detached through electrolysis or the addition of chemical substances. In this way, when the main layer 110 is not damaged or after the main layer 110 is repaired, it can be reused, thereby realizing the recycling of the oil and gas pipeline and reducing resource waste.

[0016] This protective layer can also be an anti-corrosion layer or a protective layer.

[0017] In some embodiments, the protective layer can be an intelligent protective layer. The material of the outer protective layer 112 includes shape memory alloy and / or intelligent coating. The shape memory alloy can restore its original shape through temperature change when there are minor cracks or deformations in the pipeline, repairing the pipeline; the intelligent coating can monitor the corrosion situation of the pipeline in real time. When corrosion is detected, it automatically releases corrosion inhibitors for protection and transmits the corrosion information to the monitoring system through the built-in sensor so that maintenance measures can be taken in a timely manner.

[0018] In some embodiments, the protective layer includes an inner protective layer 122; the bonding layer includes an inner bonding layer 121; the inner bonding layer 121 is attached to the inner surface of the main body layer 110 and is located between the inner protective layer 122 and the main body layer 110; and / or, the protective layer includes an outer protective layer 112; the bonding layer includes an outer bonding layer 111; the outer bonding layer 111 is attached to the outer surface of the main body layer 110 and is located between the outer protective layer 112 and the main body layer 110.

[0019] In some embodiments, the inner bonding layer 121 includes: A first primer layer, attached to the inner surface of the main body layer 110; An intermediate layer, attached to the primer layer; An interface modification layer, attached to the intermediate layer; the inner protective layer 122 is attached to the interface modification layer.

[0020] In some embodiments, the number of layers of the first primer layer can be one layer or multiple layers. Exemplarily, the first primer layer can specifically be an epoxy resin primer. This epoxy resin primer has excellent adhesion and corrosion resistance, and is particularly suitable as a primer on the metal surface.

[0021] Exemplarily, the epoxy resin in the epoxy resin primer is 40% - 70%, preferably 45% - 65%, for example, 46%, 48%; the phenolic epoxy resin is 15% - 40%, preferably 20 - 40%. Also exemplarily, the epoxy resin primer further includes a curing accelerator of 0.01% - 5%, preferably 2.5 - 3.5%; a leveling agent of 0.5% - 2.3%, preferably 1.2% - 1.8%; a defoaming agent of 0.5% - 2.3%, preferably 1.2% - 1.8%.

[0022] In some embodiments, the epoxy resin primer can also include some fillers, which will not be elaborated one by one here.

[0023] In some embodiments, the softening point of the epoxy resin in the epoxy resin primer is 85-130 °C. In this way, the epoxy resin primer can be pyrolyzed at high temperature, so that it can be well peeled off from the main body layer 110.

[0024] In some embodiments, the intermediate layer may include, but is not limited to, a polyurethane foam layer. The polyurethane foam layer has good heat insulation and buffering properties and can expand and separate at high temperatures.

[0025] In some embodiments, the intermediate layer may also include, but is not limited to, a glass fiber reinforced composite layer composed of a glass fiber reinforced composite material. This material is peeled off through a chemical reaction.

[0026] Overall, the intermediate layer can not only enhance the adhesion of the protective layer as a whole or the overall strength of the layers attached to the main body layer 110, but also enable the rapid detachment of the protective layer under specific conditions for the recycling of the main body layer 110.

[0027] In some embodiments, the interface modification layer can be composed of nanomaterials or metal-organic framework materials. The interface modification layer is used to further enhance the bonding force between the protective layer and the main body layer 110, and can also be glassed through a chemical reaction when needed. Exemplarily, the interface modification layer can be made of nano-silica. The metal-organic framework material can be HKUST-1. HKUST-1 is self-assembled by copper ions (Cu⁺) and trimesic acid (BTC) ligands through coordination bonds. In its structure, copper ions are connected to trimesic acid through carboxyl groups to form a three-dimensional porous network structure. This structure has a large specific surface area and regular pores, which are used to enhance the adsorption force of the inner protective layer 122 and also facilitate the separation of the inner protective layer 122 during subsequent pipeline maintenance or recycling.

[0028] In some embodiments, in the oil and gas pipeline structure, the interface modification layer is in a key position. It is located between the intermediate layer and the inner protective layer and plays an indispensable role in enhancing the bonding force between the protective layer and the main body layer, enhancing the corrosion resistance of the pipeline, and facilitating the maintenance and recycling of the pipeline. Through its special chemical composition and microstructure, the interface modification layer can form strong chemical bonds or strong physical adsorption with the intermediate layer and the inner protective layer, effectively enhancing the bonding force between the layers. For example, an interface modification layer composed of nano-silica or metal-organic framework material HKUST-1 has a large specific surface area, providing more active sites, which can be tightly combined with the adjacent layers to ensure that the protective layer is firmly attached to the main body layer, preventing the protective layer from falling off due to factors such as vibration and pressure changes during oil and gas transportation, and ensuring the protective performance of the pipeline. Optimize the internal microenvironment of the pipeline and block the penetration of corrosive substances in the oil and gas. Materials such as nano-silica have good barrier properties and can effectively prevent corrosive media such as water, sulfur, and carbon dioxide from contacting the main body layer, slowing down the corrosion rate of the main body layer. The three-dimensional porous network structure of the metal-organic framework material HKUST-1 can not only enhance the adsorption force of the inner protective layer but also adsorb and filter corrosive media, further improving the corrosion resistance of the pipeline and extending its service life.

[0029] Facilitate pipeline maintenance and recycling: When the pipeline needs to be maintained or recycled, the interface modification layer can be separated from other layers through specific chemical reactions. The softening point of the epoxy resin primer is 85 - 130 °C, and it can be peeled off from the main body layer by high-temperature pyrolysis. The polyurethane foam layer of the intermediate layer can expand and separate at high temperature, and the glass fiber reinforced composite material layer can be peeled off through chemical reactions. In this process, due to its special material properties, the interface modification layer can cooperate with the separation requirements of the overall structure, making the separation of each layer more efficient and convenient, reducing maintenance and recycling costs, and improving resource utilization rate.

[0030] The inner protective layer 122 can also be referred to as the inner anti-corrosion layer or inner isolation layer, mainly to isolate the transported oil and gas from the main body layer 110, avoiding direct contact between the main body layer 110 and the oil and gas, and reducing the direct contact corrosion of the oil and gas pipeline by certain substances in the oil and gas (such as water, sulfur, carbon dioxide) with the main body layer 110 (the oil and gas is the metal of the metal pipeline), resulting in leakage problems of the oil and gas pipeline.

[0031] Exemplarily, through the introduction of the inner bonding layer 121, the inner protective layer 122 can be well adsorbed on the inner surface of the pipeline, reducing leakage incidents, extending the service life of the oil and gas pipeline, and reducing the maintenance cost of the oil and gas pipeline.

[0032] In some embodiments, the inner anti-corrosion layer may include, but is not limited to: a fusion-bonded epoxy powder (FBE) layer, a liquid epoxy coating layer, a polyethylene (PE) coating, and / or a peelable scale coating layer.

[0033] The outer protective layer 112 of the oil and gas pipeline is mainly for the laying environment. Under different laying environments, the material, thickness, etc. of the outer protective layer 112 can be different.

[0034] In some embodiments, the outer anti-corrosion layer can be mainly used to prevent the corrosion of moisture or various gases in the laying environment of the anti-corrosion main body layer 110, especially for preventing the rust of metals, etc.

[0035] In some embodiments, the outer bonding layer 111 includes: a second primer layer and / or a third primer layer; the materials of the second primer layer and the third primer layer are different.

[0036] In some embodiments, the second primer layer can include, but is not limited to, an epoxy resin primer layer. In some other embodiments, the third primer layer can be a two-component epoxy primer. In some embodiments, the second primer layer and the third primer layer can be stacked. In some embodiments, the third primer layer is mainly disposed in the area where the interface of the oil and gas pipeline is located, while the second primer layer can be disposed in the area outside the area where the interface is located. In some embodiments, the outer bonding layer 111 can include a separate third primer layer, which on the one hand enhances the bonding force, and on the other hand can reduce the cost compared with stacking the second primer layer and the third primer layer.

[0037] As Figure 2 shown, an embodiment of the present disclosure provides an oil and gas transportation system, including: A transportation device 200, which at least includes the oil and gas pipeline 100 described in any one of the foregoing; A sensing subsystem 210, configured to collect state parameters of the oil and gas pipeline; A control subsystem 220, connected to the sensing subsystem, and configured to monitor the oil and gas transportation system according to the state parameters of the sensing subsystem.

[0038] In some embodiments, the transportation device can be any device participating in oil and gas transportation in the oil and gas transportation system, and specifically can include, but is not limited to, one or more of an oil and gas pipeline, a valve, a pump, a compressor, a storage tank, and a metering device.

[0039] The pump can be used as a power source for oil and gas transportation. For example, the pump can be used to transport liquid petroleum, increase the pressure of the petroleum through mechanical action, overcome the pipeline resistance, and achieve the long-distance transportation of petroleum. The centrifugal pump is a common type, and it enables the petroleum to obtain energy through the high-speed rotation of the impeller.

[0040] Compressors are used to compress gaseous natural gas, increase its pressure, and improve the transportation efficiency. Centrifugal compressors and reciprocating compressors are widely used. Centrifugal compressors are suitable for large flow rate and medium to low pressure applications, while reciprocating compressors are more advantageous in high pressure and small flow rate situations. During the transportation of natural gas, compressor stations are distributed at intervals to continuously increase the pressure of natural gas and ensure its smooth transportation.

[0041] Storage tanks are used to store oil and gas, playing a role in buffering and regulating supply and demand. Crude oil storage tanks are mostly large metal tanks, and there are various types such as vertical cylindrical and horizontal cylindrical according to different storage requirements and environmental conditions. In the design, factors such as fire prevention, explosion prevention, and leakage prevention should be considered, and corresponding safety devices should be equipped, such as breather valves and safety valves. The breather valve can control the pressure in the tank to prevent damage to the tank body caused by excessive or too low pressure; the safety valve automatically opens to relieve pressure when the pressure exceeds the set value to ensure the safety of the storage tank. Exemplarily, the storage tank can be set between the oil and gas pipelines at both ends.

[0042] In the oil and gas transportation system, valves play a key role in control and regulation. Globe valves are used to cut off or connect the flow of oil and gas in the pipeline, and their closing parts move along the center line of the valve seat, with good sealing performance. Control valves can adjust the flow rate and pressure of oil and gas according to system requirements, such as controlling the flow rate by changing the valve opening. Check valves can prevent the reverse flow of oil and gas to protect the system safety. Installing check valves at positions such as the outlet of the pump can avoid damage to the equipment caused by the reverse flow of oil and gas when the pump stops working.

[0043] In order to accurately measure the transportation volume of oil and gas, metering equipment is essential. Turbine flowmeters use the fluid to drive the turbine to rotate and calculate the flow rate by measuring the rotation speed of the turbine, with the characteristics of high accuracy and good repeatability. Ultrasonic flowmeters measure the flow rate by detecting the time difference of ultrasonic wave propagation in the fluid. It has no moving parts and is easy to maintain, and is suitable for pipelines of various diameters. In occasions such as transfer metering, high-precision metering equipment can ensure the fairness and justice of trade and avoid disputes caused by metering errors.

[0044] In some embodiments, the state parameter can be any description that reflects the state of all aspects of the oil and gas transportation system or transportation equipment. Exemplarily, the state parameter can include but is not limited to the state parameters of the health status of each component (such as transportation equipment) in the oil and gas transportation system, the transportation parameters of oil and gas transportation, and the specific environmental parameters of the environment where the transportation equipment is located (such as micro environmental parameters), etc. Of course, this is only an example here, and the specific implementation is not limited to this.

[0045] In some embodiments, the sensing subsystem may include one or more sensors that are used in combination to improve the accuracy of system monitoring. Exemplarily, multi-sensor fusion monitoring is configured in conjunction with the terrain of the pipeline. Exemplarily, in an oil and gas pipeline project in a mountainous area, since the pipeline passes through complex terrain, including mountains, rivers, canyons, etc., environmental factors have a greater impact on pipeline safety. For this purpose, a sensing subsystem is constructed by deploying multiple sensors in cooperation. Sensor composition: An ultrasonic sensor is installed at every first interval (e.g., 50 meters) along the pipeline to detect changes in pipeline wall thickness and internal defects. In sections of the pipeline that are vulnerable to geological disasters such as rivers and canyons, a strain sensor is added at every second interval (30 meters) to monitor the stress and strain conditions of the pipeline due to terrain changes. At the same time, near key nodes of the pipeline, such as near key conveying equipment like valves and pump stations, infrared sensors and visual sensors are installed. The infrared sensors are used to detect abnormal surface temperatures of the pipeline, and the visual sensors monitor the operating status of the equipment and the surrounding environment. The interval between two adjacent sensors of different types is related to the detection capabilities of the sensors. For example, the first interval is greater than the second interval.

[0046] All sensors are connected to a local data acquisition terminal, which preliminarily processes and packages the data collected by the sensors. The data acquisition frequency is set according to the sensor type and monitoring requirements. For example, the ultrasonic sensor collects data every first duration (10 minutes), and the strain sensor collects data every second duration (e.g., 5 minutes) under normal circumstances, and switches to real-time collection when abnormal changes occur in the terrain type. The acquisition terminal sends the data to the control subsystem through a wireless transmission network. Exemplarily, the local data acquisition terminal can be a terminal connected to an edge controller. Additionally, the local data acquisition terminal can also be the edge controller itself.

[0047] In some embodiments, the acquisition methods in the sensing subsystem may include real-time acquisition, periodic acquisition, and trigger-based acquisition. For example, for periodic acquisition, an acquisition period can be set, and the sensors in the sensing subsystem collect data according to the acquisition period. Trigger-based acquisition can be performed by the edge controller or the central controller when a trigger event is detected to collect or change the acquisition parameters (e.g., increase the acquisition frequency or decrease the acquisition frequency).

[0048] In summary, through multi-sensor fusion monitoring, the operating status of the pipeline in complex terrain can be comprehensively grasped. For example, after a heavy rainstorm, the strain sensor detected an abnormal increase in the stress of a section of the pipeline passing through the mountain. Combining with the images of the surrounding mountains taken by the visual sensor, it was judged that there might be a risk of landslide, and relevant departments were notified in time for handling, thus avoiding pipeline damage and oil and gas leakage accidents.

[0049] In some embodiments, distributed fiber optic sensing is used to monitor long - distance pipelines. For example, for long - distance oil and gas pipelines, a sensing subsystem is constructed using distributed fiber optic sensing technology. The optical fiber is laid along the pipeline, and based on the photoelastic effect and Rayleigh scattering principle of the optical fiber, distributed measurement of parameters such as temperature and strain along the pipeline is achieved. The optical signal transmitted in the optical fiber will be scattered and reflected due to factors such as temperature changes and mechanical strains of the pipeline. By analyzing the reflected optical signal, real - time status information of the pipeline along the line can be obtained. The optical fiber is fixedly connected to the pipeline at specified intervals (e.g., 100 meters, 200 meters, or 300 meters) to ensure accurate perception of pipeline changes.

[0050] In some embodiments, a distributed fiber optic sensing demodulator processes and demodulates the optical signal reflected by the optical fiber, converting it into physical quantity data such as temperature and strain. These data are transmitted to the control center through the fiber optic network, and the software system of the control center analyzes the data in real - time. When it is detected that the temperature or strain of a certain section of the pipeline exceeds the normal range, the oil and gas transportation system immediately issues a warning message and accurately marks the abnormal location on the electronic map.

[0051] In some embodiments, such a distributed fiber optic sensing monitoring system has advantages such as high precision, long monitoring distance, and strong anti - interference ability. In practical applications, it has successfully detected pipeline slight deformations caused by external construction and temperature anomalies caused by temperature differences many times, providing accurate basis for pipeline maintenance and ensuring the safe operation of long - distance pipelines.

[0052] In some embodiments, the sensor subsystem and the control subsystem use environmental energy for power supply. For example, the sensor subsystem uses solar energy, geothermal energy, or vibration energy for power supply. For example, according to the area and environment through which the oil and gas pipeline passes, energy is collected to provide the energy consumption required for the subsequent operation of the sensor subsystem.

[0053] For example, if the oil and gas pipeline passes through a complex and curved terrain, which will generate large vibrations, these sensor subsystems and control subsystems can be configured with energy - harvesting components that collect vibration energy for power generation. For example, in windy conditions, vibration - based power generation components can also be carried on the sensors. For example, the power generation component can include piezoelectric wafers. Under wind energy or the vibration of the pipeline, the piezoelectric wafers mechanically deform and then recover to generate electricity.

[0054] For another example, if the oil and gas pipeline passes through a place with very good sunlight, solar energy can be collected through the installation of solar panels, etc., for power generation.

[0055] For another example, if there is geothermal energy in the area where the oil and gas pipeline passes, the sensor subsystem and the control subsystem can also be equipped with geothermal - collecting devices.

[0056] In this way, the sensor subsystem and the control subsystem can collect the energy consumption required for operation locally, achieving self-sufficiency in energy consumption, which is especially suitable for environments where there is no power grid or a suitable power grid laid.

[0057] The control subsystem can be used for the monitoring and control of the oil and gas transportation system. The control subsystem can be a multi-level control system. Exemplarily, it is an intelligent monitoring with collaborative control between the edge and the central controller. In a large oil and gas transportation network, the control subsystem consists of an edge controller and a central controller to achieve intelligent monitoring and precise control of the pipeline. An edge controller is set in each monitoring area, such as installed at each pipeline pumping station, valve chamber, etc. The edge controller is connected to the sensing subsystem within the area to collect the state parameters of the pipeline in real time, such as pressure, flow rate, temperature, etc. The edge controller has the ability of local data processing and decision-making, and can analyze the data according to the preset thresholds and rules. For example, when it detects that the pressure of a certain pipeline suddenly rises, the edge controller immediately starts the local emergency procedure, closes the relevant valves, prevents safety accidents caused by excessive pressure, and uploads the detailed abnormal information to the central controller.

[0058] In some embodiments, the edge controller is set according to the distance of the oil and gas pipeline or the laying area of the oil and gas pipeline.

[0059] Management and scheduling of the central controller: The central controller is located in the control center and is responsible for the macroscopic management and scheduling of the entire oil and gas transportation network. It receives the data uploaded by each edge controller, and through big data analysis and intelligent algorithms, comprehensively evaluates and predicts the operation status of the pipeline. According to the oil and gas transportation plan and the actual operation situation of the pipeline, the central controller sends control instructions to the edge controller to adjust parameters such as the transportation flow rate and pressure of the pipeline. At the same time, the central controller also has the functions of remote monitoring and operation, and can remotely control the edge devices in case of emergency.

[0060] In some embodiments, the central controller can also be divided into multiple levels. For example, the central controller can include two levels or three levels. Exemplarily, the central controller can include three levels, namely the local level, the regional level, and the central level. The higher the level of the central controller, the larger the laying area range of the corresponding oil and gas pipeline it processes, and the higher the processing level. In some embodiments, through the collaborative work of the edge controller and the central controller, the efficient monitoring and control of the oil and gas transportation system are achieved. During a peak period of oil and gas transportation, the central controller reasonably allocates resources and optimizes the transportation plan according to the demands of each region and the pipeline status, ensuring the stable supply of oil and gas, and at the same time improving the operation efficiency and safety of the system.

[0061] In some embodiments, a multinational oil and gas transportation enterprise adopts a cloud computing-based control subsystem to achieve remote operation and maintenance of oil and gas pipelines distributed in different regions. Exemplarily, the control subsystem is built on a cloud computing platform, and data storage, processing, and application programs are all deployed in the cloud. The pipeline data collected by the edge controller is uploaded to the cloud through the network, and the server cluster in the cloud stores and analyzes the data. Operation and maintenance personnel can log in to the cloud platform through the Internet using terminal devices such as computers and mobile phones to view the running status, historical data, and various generated reports of the pipeline in real time. At the same time, the cloud platform provides a remote control function, and operation and maintenance personnel can remotely adjust parameters and troubleshoot edge devices.

[0062] In some embodiments, leveraging the powerful computing power of cloud computing, the control subsystem realizes intelligent diagnosis and early warning of pipeline failures. By learning and analyzing a large amount of historical data, a pipeline failure prediction model is established. When the system detects abnormalities in pipeline operation data, the model predicts the possible types and times of failures and sends early warning messages to operation and maintenance personnel in a timely manner. For example, by analyzing the pressure, flow rate, and temperature data of the pipeline, it is predicted in advance that a certain section of the pipeline may be blocked, and operation and maintenance personnel arrange maintenance in advance to avoid the impact of pipeline blockage on transportation. The cloud computing-based control subsystem reduces the enterprise's hardware investment and operation and maintenance costs, improves the scalability and flexibility of the system. At the same time, it facilitates the enterprise to centrally manage and uniformly dispatch oil and gas pipelines globally, provides strong support for the digital transformation of the oil and gas industry, and has broad application prospects.

[0063] In some embodiments, the sensing subsystem includes at least one of the following: An ultrasonic sensor for detecting the oil and gas pipeline based on ultrasonic detection to obtain the state parameters; An infrared sensor for detecting the oil and gas pipeline based on infrared thermal imaging to obtain the state parameters; A vision sensor for detecting the state parameters based on visual imaging.

[0064] In some embodiments, the ultrasonic sensor can be used to detect the internal and external state parameters of the oil and gas pipeline, and is particularly suitable for detecting the internal parameters of the oil and gas pipeline. In one embodiment, the ultrasonic sensor can be arranged on the oil and gas pipeline, for example, at the interface of the oil and gas pipeline or on the top ring of the outer surface of the oil and gas pipeline.

[0065] In long-distance oil and gas pipelines, ultrasonic sensors are mainly used to detect one or more state parameters such as changes in the thickness of the pipeline wall, the presence of cracks inside, and the flow rate of the fluid in the pipeline. By emitting ultrasonic waves and receiving the reflected waves, the health status parameters of the pipeline are judged based on the time and intensity of the reflected waves. For example, when corrosion or wear occurs on the pipeline wall, the ultrasonic waves will change during propagation, and the sensor can capture these changes and convert them into electrical signals, which are transmitted to the control subsystem for analysis.

[0066] Exemplarily, an ultrasonic sensor is installed at a certain distance (such as 50 - 100 meters, for example 60 meters, etc.) along the long-distance oil and gas pipeline. At key parts such as elbows, tees, and valves of the pipeline, the density of the sensors is appropriately increased because these parts are more likely to have problems such as stress concentration and corrosion. At the same time, when the pipeline crosses special sections such as rivers, railways, and highways, the sensors are also arranged in a denser pattern to ensure real-time monitoring of these high-risk areas.

[0067] In some embodiments, the ultrasonic sensor emits ultrasonic pulses at a certain time interval (such as every 1 minute). When the ultrasonic waves encounter the pipeline wall or internal defects, part of the ultrasonic waves will be reflected back and received by the sensor. The control subsystem calculates information such as the thickness of the pipeline wall and the position and size of internal defects based on the time difference between the transmitted wave and the reflected wave and the intensity of the reflected wave. If it is detected that the thickness of the pipeline wall is lower than the safety threshold or there are large cracks, the system will immediately issue an alarm to notify the maintenance personnel to handle it.

[0068] In some embodiments, in order to reduce unnecessary detections of the ultrasonic sensor, a long period and a short period are set. Ultrasonic detection is performed at the starting period of the long period. If the state of the oil and gas pipeline, valve, pump, or other oil and gas transportation systems detected at the starting period of the long period is good and meets the first condition, the ultrasonic sensor enters the sleep state during the remaining period of the long period and wakes up for detection in the next long period. For example, the state of the oil and gas pipeline, valve, pump, or other oil and gas transportation systems is in an abnormal state or a warning state. This warning state is between the abnormal and healthy states, indicating that although the equipment can still be used, its health status is not good, and it will soon enter the abnormal state if not repaired.

[0069] In some embodiments, if the state of the oil and gas transportation system is not good or does not meet the first condition, periodic detection is performed according to the short period during the remaining period of the long period. The duration of the short period is less than the duration of the long period. Exemplarily, the long period can be 8 times, 16 times, 24 times, or 32 times, etc. the short period, so as to save the power consumption of the ultrasonic sensor and extend the service life of the ultrasonic sensor.

[0070] Exemplarily, the duration setting of the long period is determined comprehensively according to one or more of the throughput of the oil and gas pipeline, the transportation rate, the terrain type of the environment where it is located, and the climate type of the environment where it is located. The greater the throughput and transportation rate of the oil and gas pipeline, the shorter the duration setting of the long period, otherwise the longer. That is, the throughput and transportation rate of the oil and gas pipeline are negatively correlated with the duration of the long period. The duration of the short period is positively correlated with the duration of the long period.

[0071] In some embodiments, the first condition may include but is not limited to at least one of the following: There is no current oil and gas transportation task; The current is a regular oil and gas transportation task; The weather and other environments in the current area are normal. For example, there are no special weathers such as heavy rain and mountain torrents; No other special triggering events occur currently.

[0072] In some embodiments, the central controller can determine whether the oil and gas currently transported is particularly corrosive to the oil and gas pipeline or whether the transportation pressure of the current oil and gas pipeline is particularly high according to the oil and gas components detected at the input port of the oil and gas pipeline or according to the instructions input by the upper-level control device. Otherwise, it can be considered that there is no special triggering event.

[0073] In some other embodiments, the special triggering event may further include detecting that some oil and gas pipelines adjacent to or within a specified distance are in a state of waiting for repair or being under repair.

[0074] In some embodiments, in the oil and gas storage tank, the ultrasonic sensor is used to accurately measure the liquid level height of the oil and gas in the tank. By measuring the time for the ultrasonic wave to be emitted from the sensor to the liquid surface and then reflected back, and combining the propagation speed of the ultrasonic wave in the air, the distance from the liquid surface to the sensor can be calculated, thereby obtaining the liquid level height. This is of great significance for reasonably arranging the storage and transportation of oil and gas and preventing accidents such as overfilling the tank.

[0075] In some embodiments, an ultrasonic sensor is installed at the center position of the top of the storage tank to ensure that the sensor can vertically emit ultrasonic waves downward to accurately measure the liquid level. For large storage tanks, in order to improve the measurement accuracy and reliability, multiple sensors can also be arranged at different heights and circumferential directions of the tank body for multi-point measurement and data comparison.

[0076] In some embodiments, during the detection process, the sensor continuously emits ultrasonic pulses. When the ultrasonic wave encounters the liquid surface, it is reflected back and received by the sensor. The control subsystem calculates the liquid level height according to the propagation time and speed of the ultrasonic wave and displays the data on the monitoring interface in real time. At the same time, the system will set the upper and lower threshold values of the liquid level. When the liquid level exceeds the upper limit or is lower than the lower limit, an alarm will be automatically issued to remind the operator to take corresponding measures.

[0077] In some embodiments, the ultrasonic sensor can detect whether there is a leakage in the oil and gas pipeline. When a leakage occurs in the pipeline, ultrasonic signals will be generated at the leakage point. The sensor can capture these signals and determine the location and degree of the leakage by analyzing the characteristics of the signals. Detecting the leakage in a timely manner can avoid the waste of oil and gas resources and environmental pollution, and ensure the safe operation of the pipeline.

[0078] In some embodiments, an ultrasonic sensor is installed at regular intervals (such as 20 - 50 meters) along the oil and gas pipeline to focus on monitoring the welds, joints and other parts of the pipeline that are prone to leakage. At the same time, when the pipeline passes through sensitive areas such as densely populated areas and water source protection areas, the layout density of the sensors is increased to improve the sensitivity of leakage detection.

[0079] In some embodiments, the sensor monitors the ultrasonic signals around the pipeline in real time. When abnormal ultrasonic signals are detected, the system will analyze the signals to determine whether they are leakage signals. If it is confirmed that they are leakage signals, the control subsystem will roughly determine the location of the leakage according to the intensity and propagation time of the signals, and mark it on the map through the Geographic Information System (GIS). At the same time, the system will immediately issue an alarm to notify relevant personnel for emergency handling.

[0080] In some embodiments, the control subsystem can obtain the identification and location information of the sensors fixedly set in each sensing subsystem. In this way, when an abnormality is detected, the control subsystem can accurately determine the abnormal location or abnormal component of the oil and gas pipeline according to the identification and location information of the sensing subsystem.

[0081] On a long-distance oil and gas pipeline, due to the long-term influence of internal and external pressures, corrosion and other factors, small leakage points may appear. The leaked oil and gas will produce a temperature difference with the surrounding environment. The infrared sensor can detect this temperature anomaly to detect the leakage situation in a timely manner. Exemplarily, an infrared sensor is installed at regular intervals (for example, 50 meters) along the pipeline, and the installation height is a certain height (for example, 1 - 1.5 meters) from the pipeline surface to ensure that the sensor can fully cover the pipeline surface. At the elbows, welds, valves and other parts of the pipeline that are prone to leakage, the number of sensors is appropriately increased to improve the detection accuracy. The infrared sensor can be installed on the protective net of the oil and gas pipeline.

[0082] In some embodiments, the infrared sensor collects the infrared radiation data on the pipeline surface at a frequency of once per second and converts it into temperature data.

[0083] In some embodiments, the sensor transmits the collected temperature data to the control subsystem. The control subsystem pre-sets a normal temperature range. When the temperature in a certain area deviates from the normal range by more than 5°C, the system marks this area as a suspected leakage point.

[0084] In some embodiments, once a suspected leakage point is detected, the control subsystem immediately sends out an audible and visual alarm signal and displays the location of the leakage point and the temperature anomaly on the monitoring interface. At the same time, the system automatically notifies the maintenance personnel to go to the site for further inspection and handling.

[0085] In some embodiments, in the oil and gas transportation pump station, various equipment such as pumps and motors may overheat during long-term operation. This will not only affect the normal operation of the equipment, but may also lead to safety accidents. By using infrared sensors to monitor these equipment in real time, overheating problems can be detected in time and measures can be taken.

[0086] Sensor distribution: Install infrared sensors around each piece of equipment in the pump station to ensure that the temperature on the surface of the equipment can be directly detected. For large equipment, multiple sensors may need to be installed to cover different parts of the equipment. The sensors are installed at a position 0.5 - 1 meter away from the surface of the equipment to avoid being interfered by equipment vibration and the surrounding environment.

[0087] In some embodiments, the infrared sensor can collect the temperature data on the surface of the equipment every 30 seconds.

[0088] Data analysis: The control subsystem sets a safe temperature upper limit for each piece of equipment according to the type and operating parameters of the equipment. When the sensor detects that the temperature on the surface of the equipment exceeds this upper limit, the system determines that the equipment is overheating.

[0089] Alarm and handling: Once equipment overheating is detected, the control subsystem immediately issues an alarm and automatically reduces the operating power of the equipment or stops the equipment from running to prevent further damage to the equipment. At the same time, the system notifies the maintenance personnel to inspect and repair the equipment.

[0090] In some embodiments, in the oil and gas storage tank area, the change of environmental temperature will affect the state of oil and gas in the storage tank. Too high environmental temperature may cause the pressure in the tank to rise, increasing the risk of leakage and explosion. By using infrared sensors to monitor the environmental temperature in the tank area, measures can be taken in time to adjust the pressure in the tank to ensure storage safety.

[0091] In some embodiments, a plurality of infrared sensors are installed around and on the top of the storage tank area to comprehensively monitor the ambient temperature of the tank area. The sensors are installed at a height of 2 - 3 meters from the ground to avoid the influence of ground radiation and other interference factors. The infrared sensors collect ambient temperature data every 1 minute. The control subsystem analyzes the collected temperature data to determine whether the ambient temperature is within the safe range. If the ambient temperature continues to rise and approaches or exceeds the preset safety threshold, the system will issue a warning signal.

[0092] In some embodiments, when a warning signal is received, the staff will activate the cooling system of the tank area or take other cooling measures to reduce the ambient temperature and the pressure inside the tank. At the same time, the system will continuously monitor the temperature change until the temperature returns to the safe range.

[0093] In some embodiments, as Figure 3 shown, the control subsystem includes: An edge controller 221, which is set in the area to be monitored; the edge controller is connected to the sensing subsystem for receiving the status parameters provided by the sensing subsystem and controlling the operation of the sensor subsystem; A central controller 222, which is connected to the edge controller for communicating with the edge controller and controlling the operation of the edge controller.

[0094] In some embodiments, the control subsystem is based on refined control of real-time data analysis. For example, in a large oil and gas transportation network, the core of the control subsystem is to make precise control decisions based on real-time data. The edge controller is closely connected to the sensing subsystem and continuously collects key parameters such as the pressure, flow rate, and temperature of the pipeline. Every 5 seconds, the edge controller preliminarily processes and analyzes these data and compares them with the preset normal operation threshold range.

[0095] In some other embodiments, when it is detected that the pressure of a certain section of the pipeline rises abnormally, for example, exceeding 10% of the normal pressure value, the edge controller immediately activates the local emergency program. First, close the valve downstream of this section of the pipeline to prevent the pressure from rising further and causing a safety accident. At the same time, upload the detailed abnormal data, including the pressure change curve, surrounding temperature, and flow rate data, etc., to the central controller through a high-speed network with a response speed in milliseconds.

[0096] Exemplarily, the central controller receives a large amount of data from each edge controller and deeply mines the data using big data analysis techniques and complex algorithm models. For example, by combining historical data and current real-time data, it predicts the development trend of abnormal pressure, and determines whether it is a short-term fluctuation or a serious hidden danger that may cause pipeline rupture. If it is determined to be a serious hidden danger, the central controller quickly sends control instructions to multiple relevant edge controllers to coordinate the closing of valves in the surrounding pipelines, adjust the conveying flow rate, and re-plan the oil and gas conveying path to ensure the safe and stable operation of the entire conveying network.

[0097] In some embodiments, in the normal operating state, the central controller optimizes the conveying flow rate distribution of the pipeline according to the real-time collected flow rate data and the oil and gas demand prediction in each region. For example, during the morning peak gas usage period in a certain city, based on the real-time flow rate and pressure data feedback by the edge controller in that region, the central controller accurately controls the valve opening degree of the relevant pipeline, increases the oil and gas conveying volume to the city, ensures that the energy supply meets the demand, and at the same time avoids the risks of energy waste and excessive pipeline pressure caused by over-conveying.

[0098] In some embodiment sets, the control subsystem has adaptive control to cope with complex environmental changes. For example, when the oil and gas pipeline crosses a complex geological area or a region with variable climate, the control subsystem needs to have the ability of adaptive control. Taking the oil and gas pipeline crossing the seismic zone as an example, the edge controller is equipped with high-precision seismic monitoring sensors and works in coordination with traditional oil and gas pipeline monitoring sensors.

[0099] When the seismic monitoring sensor detects a seismic wave signal, the edge controller immediately enters the emergency response mode. On the one hand, it quickly closes the key valves of the pipeline in the earthquake-prone area to prevent oil and gas leakage caused by pipeline rupture due to the earthquake. On the other hand, it quickly uploads the earthquake-related data and the real-time status data of the pipeline periphery to the central controller.

[0100] In some embodiments, after receiving the data, the central controller combines the Geographic Information System (GIS) data and the pipeline network model to evaluate the influence range and degree of the earthquake on the pipeline system. For the pipeline area less affected by the earthquake, the central controller adjusts the parameters of the edge controller, such as changing the conveying pressure and flow rate of the pipeline, to maintain the minimum conveying while ensuring safety, so as to ensure the energy supply to key users. For the severely damaged area, the central controller formulates a detailed repair plan and remotely controls the relevant equipment through the edge controller, sets warning signs, and prevents irrelevant personnel from approaching the dangerous area.

[0101] In some embodiments, in desert areas with variable climates, pipelines face the challenges of large day-night temperature differences and sandstorm erosion. The edge controller monitors the temperature changes and surface conditions of the pipeline in real time. When the temperature drops rapidly, it automatically adjusts the pipeline's insulation system, increasing the coverage of insulation materials or activating heating devices to prevent the pipeline from becoming brittle due to low temperature and the transported medium from solidifying. At the same time, according to the sandstorm monitoring data, when the sandstorm is strong, it adjusts the operating modes of relevant equipment, such as reducing the rotational speed of the fan, to avoid excessive wear of the equipment caused by the sandstorm. The central controller then optimizes the operation strategies of the pipeline network in the entire desert area based on the information fed back by each edge controller, balancing the needs of energy transportation and equipment protection to ensure the long-term stable operation of the pipeline in harsh environments.

[0102] In some embodiments, the control subsystem utilizes deep learning algorithms to deeply analyze the massive data collected by the sensing subsystem over a long period. By establishing a pipeline fault prediction model, it can not only accurately predict common faults but also give early warnings of potential complex fault hazards. Using historical leakage data and real-time monitoring parameters as training samples, the model can learn the subtle data change patterns before leakage occurs, thereby detecting abnormalities early and gaining more time for maintenance to reduce accident risks.

[0103] The control subsystem makes intelligent decisions to optimize the transportation strategy. Exemplarily, reinforcement learning techniques are introduced to enable the control subsystem to automatically optimize the transportation strategy according to the real-time oil and gas transportation requirements, pipeline operation status, and energy market dynamics. The control subsystem continuously tries different transportation schemes and adjusts its decisions based on the feedback results to maximize transportation efficiency and minimize costs. When meeting the oil and gas demands in different regions at different times, it comprehensively considers factors such as pipeline pressure, flow rate, and energy consumption, and dynamically adjusts the transportation route and flow distribution to improve energy utilization efficiency.

[0104] In some embodiments, the control subsystem is based on quantum encryption communication to ensure data security: Given the sensitivity of the data in the oil and gas transportation system, quantum encryption technology is used to encrypt the data transmitted between the control subsystem and the sensing subsystem. Quantum encryption utilizes the principles of quantum mechanics and has the characteristics of being non-eavesdroppable and non-crackable, ensuring the security and integrity of the data during transmission, effectively preventing security accidents caused by data leakage, and protecting the key information of the oil and gas transportation system.

[0105] In some embodiments, a multi-redundancy security backup system: To prevent system paralysis caused by a single point of failure, a multi-redundancy security backup system is constructed. Multiple copies of critical devices and data are backed up and distributed at different geographical locations. When the primary system fails, the backup system can quickly and seamlessly switch to maintain the normal operation of the system. Redundant devices are set in the central controller and the edge controller, and important data is backed up remotely to ensure that the system can still operate stably in the event of natural disasters or equipment failures.

[0106] In some embodiments, the control subsystem can use blockchain technology to enhance data credibility and traceability: Blockchain technology is introduced to perform distributed storage and encrypted recording of key data during the oil and gas transportation process. Each data block contains a timestamp, data content, and the hash value of the previous data block, forming an immutable chain structure. This not only enhances data credibility but also enables full-process traceability of data, facilitating tracking and auditing of the source, transportation path, usage, etc. of oil and gas, meeting regulatory requirements, and improving industry transparency and management efficiency.

[0107] In some embodiments, the visual sensor includes: a first type of monitor fixedly installed in the monitored target area and / or a mobile monitor that can move based on a patrol instruction.

[0108] In some embodiments, the fixed monitor is fixedly installed at a specific location in the oil and gas transportation system, and its installation location or identifiers such as device numbers related to the location are known to the control subsystem.

[0109] Exemplarily, in key areas of oil and gas pipelines, such as valves and pumping stations, the first type of fixedly installed monitor uses a high-definition camera and is equipped with intelligent image analysis software. The software of the first type of monitor uses deep learning algorithms to automatically identify problems such as corrosion marks, deformation, and the presence of foreign object attachment on the pipeline surface. When an anomaly is detected, the first type of monitor automatically marks it and sends an alarm to the control subsystem, while storing the relevant image data in a local server for subsequent query and analysis. For example, in a renovation project of an old oil and gas pipeline, a pipeline corrosion point was promptly detected through the intelligent analysis function of the fixed monitor, providing the accurate location and detailed information for the repair work and greatly shortening the repair time.

[0110] In some embodiments, the mobile monitor may include, but is not limited to, an unmanned aerial vehicle (UAV) equipped with a vision sensor and / or an autonomous vehicle equipped with a vision sensor. Exemplarily, the UAV autonomously patrols the oil and gas pipeline according to a preset patrol route and time interval. During the patrol, the UAV uses the vision sensor to capture high-definition images of the pipeline and transmits the images back to the control center in real time via wireless communication technology. When encountering complex terrain or dangerous areas, the UAV can automatically adjust its flight altitude and angle to ensure clear images are obtained. In addition, the UAV is also equipped with a megaphone and warning lights, which can warn the scene and remind nearby personnel to pay attention to safety when abnormal situations are detected. For example, in the inspection of oil and gas pipelines in mountainous areas, the UAV can easily cross complex terrain and effectively monitor areas that are difficult to reach.

[0111] In some embodiments, the mobile monitor has a positioning function. For example, the mobile monitor includes a positioning chip that can perform precise positioning, perform positioning while collecting images, and upload the collection location of the image when uploading the image. In this way, when the control subsystem analyzes an abnormality based on the image, it can determine the abnormal location of the oil and gas transportation system according to the collection location, facilitating maintenance.

[0112] In some embodiments, in addition to using ultrasonic sensors, infrared sensors, and vision sensors, the sensing subsystem also integrates pressure sensors and humidity sensors. The pressure sensors are installed at key nodes of the oil and gas pipeline (e.g., pipeline valves, branch ports of the oil and gas pipeline) to monitor the pressure changes inside the pipeline in real time. Once the pressure abnormally rises or falls, an alarm is immediately sent to the control subsystem. The humidity sensor is used to detect the humidity of the oil and gas inside the pipeline to avoid pipeline corrosion caused by excessive humidity. For example, in an oil and gas transportation system in a desert area, due to the dry climate, the change in the humidity of the oil and gas inside the pipeline may affect the transportation safety. The humidity sensor can timely detect abnormal humidity and take corresponding measures, such as adding drying equipment.

[0113] Cooperative optimization embodiment of the control subsystem: The communication between the edge controller and the central controller uses a mobile network, such as 4G communication or 5G communication, to achieve high-speed data transmission and real-time interaction. In some embodiments, the edge controller and the central controller can also use wired communication or satellite communication according to the network layout. Exemplarily, satellite communication can be used as a backup communication system for the system. When the mobile communication or wired communication fails, satellite communication can be enabled for emergency information transmission between the edge controller and the central controller.

[0114] The edge controller not only receives the status parameters of the sensing subsystem, but also can perform preliminary analysis and processing on the data according to the preset algorithm. When a slight pipeline leak is detected, the edge controller immediately activates the local emergency handling procedure, such as closing the nearby valves, to prevent the leak from expanding. At the same time, it quickly transmits the detailed leak information, including the location, leak volume, etc., to the central controller. Based on this information, the central controller coordinates the resources in multiple regions, organizes the repair team for emergency repair, and adjusts the conveying flow of other pipelines to ensure the stable operation of the entire conveying system.

[0115] Example of intelligent analysis of fixed monitors: In key areas of oil and gas pipelines, such as valves, pumping stations, etc., the first type of fixed monitors are equipped with high-definition cameras and intelligent image analysis software. The software uses deep learning algorithms to automatically identify problems such as corrosion marks, deformation on the pipeline surface, and the presence of foreign object attachment. When abnormalities are detected, it automatically marks and sends an alarm to the control subsystem, and at the same time stores the relevant image data in the local server for subsequent query and analysis. For example, in a renovation project of an old oil and gas pipeline, through the intelligent analysis function of the fixed monitor, a pipeline corrosion point was timely detected, providing the accurate location and detailed information for the repair work, and greatly shortening the repair time.

[0116] The mobile monitor uses the method of an unmanned aerial vehicle (UAV) equipped with a vision sensor. The UAV autonomously patrols the oil and gas pipeline according to the preset patrol route and time interval. During the patrol, the UAV uses the vision sensor to take high-definition images of the pipeline and transmits the images back to the control center in real time through wireless communication technology. When encountering complex terrain or dangerous areas, the UAV can automatically adjust the flight height and angle to ensure clear images are obtained. In addition, the UAV is also equipped with a loudspeaker and warning lights, and can warn the scene when abnormal situations are found, reminding nearby personnel to pay attention to safety. For example, in the inspection of oil and gas pipelines in mountainous areas, the UAV can easily cross complex terrain and effectively monitor areas that are difficult to reach.

[0117] In some embodiments, the control subsystem can generate patrol instructions as needed to control the mobile monitoring device to patrol and collect data. The mobile monitoring device can collect blind spots that cannot be monitored by the sensing subsystem in daily monitoring, so as to enhance the more comprehensive monitoring of the oil and gas conveying system.

[0118] In some embodiments, the edge controller can act as an edge gateway, or the edge controller can be integrated with the edge gateway.

[0119] In some embodiments, the edge controller can also serve as a collection device for mobile monitoring devices. The collection device may include one or more accommodation spaces. Exemplarily, the edge controller can be arranged in a control room and serve as the master device in the control room. Exemplarily, there are one or more accommodation spaces in the control room. One accommodation space can be used for a drone to park in. In some embodiments, the accommodation space is simultaneously a charging position for the drone or the unmanned vehicle. The accommodation space includes an opening that can be opened or closed and locked. For example, an intelligent lock that is automatically locked by electromagnetic means, and the intelligent lock is controlled by the edge controller. The accommodation space can also serve as a protection device for mobile monitoring devices. Exemplarily, charging contacts are arranged on the surface of the drone or the mobile vehicle. When entering the corresponding accommodation space, the charging contacts are coupled with the charging pile to charge the drone or the unmanned vehicle. For example, the charging contacts can be arranged on the lower surface of the drone's wings or on the side of the unmanned vehicle.

[0120] When the edge controller needs the mobile monitoring device to patrol, it sends an open instruction to the intelligent lock, the accommodation space opens, and a patrol instruction is sent to the mobile monitoring device. The patrol instruction includes but is not limited to at least one of the following: patrol area information, patrol time information (for example, start time information and / or intelligent time information), information such as the key points of patrol monitoring, etc. If the mobile monitoring device detects a fault, it can request to return to the warehouse in advance (that is, return to the corresponding accommodation space) through communication with the edge controller.

[0121] In the embodiments of the present disclosure, the mobile monitoring device has charging contacts, and in the equipment room where the edge controller is located, there is a charging pile that is electrically connected to and locked with the charging contacts. In this way, the mobile charging device can be stably placed in the accommodation space to prevent damage caused by the movement of the mobile monitoring device in the accommodation space due to harsh environments such as strong winds.

[0122] In some embodiments, the central controller uses big data analysis technology to comprehensively analyze historical oil and gas transmission load data, pipeline status parameters, and factors such as weather and season, and establish an accurate prediction model. According to the prediction results, the monitoring strategy is adjusted in advance. For example, during the winter heating period, when it is predicted that the oil and gas demand in a certain area increases, the central controller strengthens the monitoring frequency of the pipelines in that area in advance, increases the investment in detection equipment, and coordinates the edge controller to make emergency preparations to ensure the safe transmission of the pipeline under high load operation.

[0123] In some embodiments, the oil and gas transportation system is linked with the surrounding environmental monitoring system and fire protection system. When a visual sensor or other sensors detect a pipeline leak and the leaked oil and gas may pollute the environment, the central controller immediately sends a warning message to the environmental monitoring system and starts the environmental emergency response procedure. At the same time, if the leaked oil and gas reaches a certain concentration and there is a fire risk, the central controller will automatically trigger the fire protection system, start the fire extinguishing device and evacuation procedure to ensure the safety of personnel and equipment. This multi-system linked monitoring method greatly improves the safety and reliability of the oil and gas transportation system.

[0124] In summary, the oil and gas transportation system provided by the embodiments of the present disclosure has high intelligence, reduces manual patrol or detection, and is especially suitable for places with extremely harsh environments.

[0125] In some embodiments, the oil and gas transportation system is also equipped with an industrial robot, which can be a humanoid robot and can perform simple abnormal processing or routine abnormal processing when an abnormality is detected. For example, when it is found through a visual sensor that the outer protective layer of the oil and gas pipeline is peeled off, the industrial robot can make modifications to improve the protection performance of the oil and gas pipeline. When the industrial robot cannot handle the abnormality, it notifies the staff for repair and handling.

[0126] In some embodiments, the oil and gas transportation system further includes an inhibitor injection device. The inhibitor injection device is installed on the oil and gas pipeline and is used to inject an inhibitor into the oil and gas pipeline to reduce the corrosion of the oil and gas on the oil and gas pipeline, valves or pumps.

[0127] In some embodiments, the inhibitor injection device includes an electromagnetic valve and is electrically connected to the edge controller for controlling the injection of the inhibitor according to the inhibitor injection device. Specifically, the inhibitor injection device starts the injection of the inhibitor, stops the injection of the inhibitor, controls the injection amount or injection speed of the inhibitor, etc. according to the injection instruction of the edge controller. In some embodiments, the control subsystem will control the injection of the inhibitor according to the corrosion prediction curve of the components such as the oil and gas pipeline in the currently transported oil and gas. Usually, when the corrosion prediction curve indicates serious corrosion, more inhibitor will be injected or the inhibitor will be injected more frequently, otherwise it can be the opposite, so as to achieve precise control of the inhibitor injection.

[0128] As Figure 4 described, the embodiments of the present disclosure provide a monitoring method for an oil and gas transportation system, including: S110: The edge controller of the oil and gas transportation system performs primary monitoring based on the life cycle of each transportation device and in combination with the status parameters collected on site; S120: The central controller of the oil and gas transportation system obtains a secondary monitoring result based on the information provided by the edge controller.

[0129] Exemplarily, there is a long-distance cross-border oil and gas pipeline passing through multiple different geographical regions and climate environments, and the life cycle of the pipeline covers multiple stages such as construction, operation, and maintenance. To ensure the safe and stable operation of the pipeline, the oil and gas transportation system monitoring method of the present disclosure is adopted.

[0130] In some embodiments, the oil and gas transportation system may be the transportation system described in any of the foregoing embodiments, but is not limited to the foregoing oil and gas transportation system. Exemplarily, the oil and gas transportation system includes Figure 1 an oil and gas transportation pipeline, and may also not be limited to the oil and gas transportation system provided in the foregoing embodiments.

[0131] For the primary monitoring in S110, based on the life cycle and status parameters: during the pipeline construction stage, the edge controller performs primary monitoring according to life cycle information such as the pipeline design life and material characteristics, combined with the pipeline installation quality detection parameters collected on site (such as weld quality, flatness of pipeline laying, etc.). For example, during the pipeline laying process, the edge controller real-time obtains the weld data detected by the ultrasonic flaw detector. When a minor defect is detected in the weld, the position is immediately marked, and it is judged whether construction needs to be suspended for repair according to the preset standard.

[0132] During the pipeline operation stage, the edge controller combines the status parameters collected by various sensors on site, such as the pipeline internal pressure value obtained by the pressure sensor, the oil and gas temperature obtained by the temperature sensor, and the oil and gas flow rate obtained by the flow sensor, for real-time monitoring. For example, when the edge controller detects that the pressure of a certain section of the pipeline suddenly rises and exceeds the normal fluctuation range, according to the life cycle stage and historical data of the pipeline, it is judged that it may be a downstream valve failure or pipeline blockage, and the local emergency plan is immediately activated, such as closing the nearby safety valve, and reporting the detailed situation to the central controller.

[0133] In some embodiments, the edge controller uses AI algorithms for primary monitoring. Exemplarily, the edge controller uses one or more neural networks in the deep learning algorithm to process the status parameters to achieve monitoring and perform corresponding processing based on the monitoring results.

[0134] Exemplarily, as Figure 5 shown, S110 may include: S111: Obtain the status parameters of the transportation equipment of the oil and gas transportation system; S112: Generate a first feature map according to the status parameters of the transportation equipment of the oil and gas transportation system; wherein, the first feature map is a three-dimensional feature map; the first feature map includes a plurality of two-dimensional feature maps sequentially combined and stacked; one of the two-dimensional feature maps corresponds to one of the transportation equipment; S113: Generate a second feature map based on the first feature map; the second feature map is a three-dimensional feature map; the sorting of the feature elements in the second feature map is different from that of the feature elements in the first feature map; S114: Generate a primary monitoring result based on the first feature map and the second feature map.

[0135] In some embodiments, the edge controller may directly use the first feature map, the second feature map, and the third feature map as inputs to obtain a primary monitoring result.

[0136] In some other embodiments, the edge controller may obtain a first prediction result based on the first feature map; obtain a second prediction result based on the second feature map; obtain a third prediction result based on the third feature map; and obtain a primary monitoring result of the target area corresponding to the edge controller according to the first prediction result, the second prediction result, and the third prediction result.

[0137] In some embodiments, the edge controller may obtain a first prediction result based on the first feature map; obtain a second prediction result based on the second feature map; and fuse the first prediction result, the second prediction result, and the third feature map to obtain a primary monitoring result of the target area corresponding to the edge controller.

[0138] In some embodiments, the orderly combination and stacking of multiple two-dimensional feature maps to form a first feature map may include: Stack the first feature map in sequence according to the transportation path formed by each transportation pipeline of the oil and gas transportation system and the order of transportation equipment such as pipelines, valves, pumps, and oil and gas tanks that the oil and gas pass through in sequence during transportation in the transportation path.

[0139] In some embodiments, one of the two-dimensional feature maps includes the characteristic parameters of one transportation device. Exemplarily, the characteristic parameters of one transportation device include at least one of the following: The device parameters of the transportation device, which depend on the manufacturing of the device and are used to describe the inherent attributes or characteristics of the transportation device. Exemplarily, for an oil and gas pipeline, the structural parameters such as the diameter of the oil and gas pipeline, the thickness of the pipeline wall, the thickness of the protective layer, and the material of the main body layer; in some other embodiments, the device parameters may also include performance parameters. For example, the maximum output power of a pump, the change rate of the maximum output power of the pump per unit time, etc.

[0140] The maintenance parameters of the transportation device, which may include historical failure parameters and historical maintenance parameters; The life cycle parameters of the transportation device. Different devices from different manufacturers, with different materials or models have different life cycles according to historical statistical laws, etc.; The status parameters collected by the sensing subsystem.

[0141] In some embodiments, the characteristic parameters of a conveying device at least include the state parameters of the conveying device.

[0142] In one embodiment, the state parameters collected by the sensing subsystem of a conveying device may include the state parameters within the current time window. Further, the state parameters collected by the sensing subsystem of a conveying device may also include: the state parameters of one or more historical time windows before the current time window.

[0143] The two-dimensional feature map of a conveying device has multiple feature rows and multiple feature columns; different types of characteristic parameters are filled in specific positions of the two-dimensional feature map according to a preset order.

[0144] The two-dimensional feature maps of each conveying device are stacked to form a first feature map. In some embodiments, the stacking of multiple two-dimensional feature maps in sequence to form a first feature map may include: According to the topology of the oil and gas conveying system, each conveying path is determined. According to the order of conveying devices such as pipelines, valves, pumps, oil and gas tanks, etc. that the oil and gas passes through in sequence during conveying in the conveying path, the two-dimensional feature maps of these conveying devices are stacked in sequence to obtain the first feature map. Exemplarily, if there are multiple conveying paths, the conveying devices on different conveying paths are stacked at intervals in the first feature map. The two-dimensional feature maps corresponding to adjacent conveying devices on the same conveying path are stacked adjacent to each other. In this way, if a conveying device belongs to N conveying paths at the same time, the two-dimensional feature map of this conveying device appears N times in the first feature map. By adopting this method, the edge controller can determine the states of different conveying paths with a very small amount of calculation. For example, whether the conveying path can convey oil and gas at the next moment, the amount of oil and gas that can be conveyed, and other related information.

[0145] In some other embodiments, the stacking of multiple two-dimensional feature maps in sequence to form a first feature map may also include: According to the topological structure of the oil and gas transportation system, numbers are set for the transportation equipment. According to the direction of oil and gas transportation, different equipment at the same position is sequentially coded, and the two-dimensional feature maps of each transportation equipment are stacked according to their codes to obtain the first feature map. For example, there are points A and B in the direction of the oil and gas transportation pipeline. There are M transportation equipment at point A and N transportation equipment at point B. Point A is closer to the source of oil and gas transportation than point B. When coding, the M transportation equipment at point A are coded first, and then the N transportation equipment at point B are coded. The M transportation equipment at point A are sequentially coded according to the adjacent relationship between the transportation equipment. The N transportation equipment at point B are sequentially coded according to the adjacent relationship between the transportation equipment. When stacking the two-dimensional feature maps, they are stacked from the direction of the source of oil and gas transportation to the transportation equipment at the location of the destination according to the equipment codes in sequence. In this way, it can be easily seen where the abnormal points in the oil and gas transportation are. The two-dimensional feature map of each transportation equipment will only appear once in the first feature map, reducing the data volume of the first feature map.

[0146] In some other embodiments, the combination and stacking of multiple two-dimensional feature maps in sequence to form the first feature map may further include: Carry out type coding for the transportation equipment according to the equipment type, and carry out position coding according to the location where the transportation equipment is located; stack the two-dimensional feature maps of each transportation equipment according to the type coding and the position coding to obtain the first feature map. In this way, there is also no problem that the two-dimensional feature map of a transportation equipment appears multiple times in the first feature map. At the same time, by combining the type coding and the position coding, when an abnormality is found, the type and location of the abnormal equipment can also be determined. For example, the equipment type may at least include power equipment such as pipelines and pumps, temporary storage equipment such as tanks, and flow control equipment such as valves, etc.

[0147] In some embodiments, the monitored transportation equipment within a target area is sorted according to a preset order, and each two-dimensional feature map is stitched in place to obtain the first feature map.

[0148] In specific implementation, in order to obtain different prediction results, a preset sorting method can be adopted according to the required prediction results.

[0149] In some embodiments, a second feature map is generated according to the acquisition time of the state parameters in the first feature map. One two-dimensional feature map in the second feature map includes the state parameters of each conveying device within the same acquisition time in the target area and the key parameters of the conveying device. The key parameter can be a limit parameter generated based on one or more of the device parameters, maintenance parameters, and life cycle parameters. Exemplarily, the limit parameter can be used to indicate the maximum working capacity of the conveying device at the moment corresponding to the state parameter. For example, the maximum conveying capacity or conveying rate of an oil and gas pipeline. Parameters such as the maximum opening and closing amount of a valve, the maximum output amount of a pump, etc. Exemplarily, the limit parameter can also include the maximum change amount of the conveying device per unit time determined according to the performance parameter. For example, the maximum change amount of the power of a pump or the maximum opening and closing amounts of a gate.

[0150] In some embodiments, the third feature map can be generated according to the oil and gas transportation plan or oil and gas transportation task of the target area. For example, the third feature map can be generated according to the oil and gas transmission plan of the target area. Exemplarily, the third feature map can be based on the measured flow parameters, transportation plan parameters, etc. of one or more time windows before the current time window.

[0151] In some embodiments, the primary monitoring result can be used to indicate the state of the target area within the current time window. In other embodiments, the primary monitoring result can also be used to indicate the state of the target area within the next time window or at the next moment.

[0152] In some embodiments, the state corresponding to the primary monitoring result can include but is not limited to at least one of the following: The first state, indicating that the target area is in a normal state; The second state, indicating that the target area is in an overloaded state; The third state, indicating that the target area is in an underloaded state; The fourth state, indicating that the target area is in an abnormal state.

[0153] In some embodiments, if it is in the second state or the fourth state, then at least one of the following is also included along with the primary monitoring result: The device information of the conveying device in an overloaded or abnormal state, such as the device type, device number, device location, etc.; The abnormal information of the abnormal conveying device, such as the abnormal type, abnormal duration, and severity of the abnormality.

[0154] For example, the abnormal type can include reversible abnormality and irreversible abnormality. The abnormal duration can be the duration length of maintaining the abnormal state. The severity of the abnormality can be divided according to the damage degree of the conveying device and the damage degree to the oil and gas transportation of the oil and gas transportation system.

[0155] When the edge controller determines that the first-level monitoring result indicates that the target area is in the fourth state, emergency handling is initiated. The emergency handling includes emergency handling that can be controlled by the edge controller. For example, stop the operation of some conveying devices and enable standby devices, or restart abnormal devices, re-plan the conveying path within the target area, re-divide the paths on multiple paths in the target area, and re-divide the oil and gas conveying tasks on different paths. At the same time, the emergency handling can also include reporting to the superior central controller to achieve an alarm. The edge controller sends an alarm to the staff's device. In some cases, if the edge controller discovers an abnormality, but the abnormality can be eliminated through re-adjustment of the conveying path and restart of the abnormal device, then no alarm will be sent to the staff's device, but only a backup will be made to the superior central controller.

[0156] In some embodiments, the edge controller also combines the fourth feature map to obtain a corrected result of the first-level monitoring result.

[0157] In some embodiments, the fourth feature map is generated according to the capability parameters of the edge controller. The capability parameters indicate the abnormal handling operations that the edge controller can perform. When generating the first-level monitoring result by combining the fourth feature map, the corrected result of the first-level monitoring result at this time can be the result of whether the edge controller can maintain the normal operation of the target area in the next moment or the next time window within its limit capabilities. In this way, the edge controller can determine whether to send an alarm to the central controller or the staff's portable device according to the corrected result.

[0158] The edge controller also combines the fourth feature map, and the obtained corrected result of the first-level monitoring result may include: obtaining the corrected result of the first-level monitoring result according to the first feature map, the second feature map, the third feature map, and the fourth feature map.

[0159] In some embodiments, obtaining the corrected result of the first-level monitoring result according to the first feature map, the second feature map, the third feature map, and the fourth feature map includes: Obtaining the corrected result of the first-level monitoring result according to the first prediction result, the second prediction result, the third prediction result, and the fourth feature map; wherein, the first prediction result, the second prediction result, and the third prediction result can be used to obtain the first-level monitoring result, or, the first prediction result, the second prediction result, and the third prediction result can be used to characterize the first-level monitoring result; In some embodiments, obtaining the corrected result of the first-level monitoring result according to the first feature map, the second feature map, the third feature map, and the fourth feature map includes: obtaining the corrected result of the first-level monitoring result according to the first-level monitoring result and the fourth feature map.

[0160] In some embodiments, the edge controller sends the primary monitoring result and / or the corrected result of the primary monitoring result to the central controller.

[0161] In some embodiments, the edge controller sends the first prediction result, the second prediction result, and the third prediction result to the central controller to facilitate the central controller's fusion of prediction results at different levels.

[0162] In some embodiments, when a preset condition is met, the edge controller sends the first feature map, the second feature map, and the third feature map to the central processor. To reduce transmission latency and the amount of transmitted data, the edge controller compresses and / or downsamples the first feature map, the second feature map, and the third feature map before sending them to the central processor.

[0163] In some embodiments, when a preset condition is met, the edge controller sends the key feature elements in the feature maps corresponding to the generation of the first prediction result, the second prediction result, and / or the third prediction result to the central processor.

[0164] In some embodiments, meeting the preset condition may include, but is not limited to, at least one of the following: The central controller instructs for reporting; The primary monitoring result indicates that the target area is in the second state or the fourth state; The corrected result of the primary monitoring result indicates that the abnormality in the target area cannot be self-corrected; The corrected result of the primary monitoring result shows that the abnormality in the target area can be repaired, but after a certain period of time, it may cause damage to some conveying equipment that cannot be reversed or accidents in subsequent oil and gas transportation.

[0165] In summary, if the preset condition is met, when sending the primary monitoring result to the central controller, at least one of the following is sent to the central controller: the first feature map, the second feature map, the third feature map, the first prediction result, the second prediction result, the third prediction result, the key feature elements of the first feature map, the key feature elements of the second feature map, the key feature elements of the third feature map; the first prediction result is generated by the edge controller based on the first feature map; the second prediction result is generated by the edge controller based on the second feature map; the third prediction result is generated by the edge controller based on the third feature map.

[0166] In some embodiments, the first feature map, the second feature map, the third feature map, the first prediction result, the second prediction result, the third prediction result, the key feature elements of the first feature map, the key feature elements of the second feature map, and the key feature elements of the third feature map can be used by the central controller to correct or verify the primary monitoring result.

[0167] In some embodiments, the edge controller also determines the confidence level of the first-level monitoring results generated by itself. This confidence level is synchronized to the central controller and can be used by the central controller to determine whether to correct or verify one of the triggering factors of the first-level monitoring results.

[0168] In some embodiments, the edge controller can use a large model to uniformly process multiple of the first feature map, the second feature map, the third feature map, and the fourth feature map.

[0169] In other embodiments, to reduce the difficulty of model training and also reduce the size of the model, the edge controller can use different models or different branches within a model to separately process one or more of the first feature map, the second feature map, the third feature map, and the fourth feature map.

[0170] When fusing multiple prediction results or fusing the intermediate results of model (e.g., neural network) processing, one or more of the following fusion methods can be adopted: Decision-level fusion: Decision-level fusion is performed after the prediction results at each level are generated, and is fused according to certain decision rules. Decision-level fusion has a voting method. For classification problems, the prediction results at each level are like a voter, and each "voter" votes for different classes, and finally the class with the most votes is selected as the fused decision result.

[0171] Feature-level fusion: During the operation of the neural network, features at different levels are fused, and then predictions are made based on the fused features. The detailed features at the bottom layer and the abstract features at the high layer can be concatenated to form a new feature matrix.

[0172] Model-level fusion: Different-level neural network models are fused, and each model is responsible for processing information at a specific level. Parallel model fusion can be adopted, where multiple models at different levels run simultaneously, each generating prediction results, and then these results are fused in some way.

[0173] When performing fusion, an attention mechanism can be introduced to achieve fusion at different levels in order to obtain more accurate monitoring results and / or prediction results.

[0174] During the first-level monitoring process, the edge controller uses a neural network to deeply analyze various feature maps. Exemplarily, the edge controller obtains the first feature map, the second feature map, and the third feature map, which contain rich state information of the conveying device. These feature maps are processed using a convolutional neural network (CNN). The convolutional layer of the CNN can automatically extract local features from the feature maps, such as identifying key features such as abnormal pressure fluctuations and flow rate mutations from the state parameters of the conveying device.

[0175] The dimensionality of the features is reduced through the pooling layer, reducing the computational amount while retaining important information. The fully connected layer comprehensively analyzes the extracted features and outputs a preliminary prediction result of the state of the target area. When processing the monitoring data of the oil and gas pipeline, the CNN can extract the local features of parameters such as pressure and temperature at different positions of the pipeline from the first feature map, combine the key parameters of the equipment in the second feature map and the information related to the oil and gas transmission plan in the third feature map, and predict whether there is a leakage risk in the pipeline, whether the transportation is normal, etc.

[0176] Exemplarily, the edge controller can also adopt a recurrent neural network (RNN), especially a long short-term memory network (LSTM) to process time series data. Since the first feature map and the second feature map contain the state parameters of different time windows, the LSTM can effectively capture the time series features of the data and learn the changing trend of the state of the transportation equipment over time. Through the analysis of the historical and current state parameters by the LSTM, the future operating state of the equipment can be predicted more accurately, and potential problems can be discovered in advance. When monitoring the operation of a pump, the LSTM can predict whether the pump will fail at a certain time in the future based on the changes in parameters such as the past flow rate and power of the pump, so as to arrange maintenance in advance and avoid the interruption of oil and gas transportation caused by pump failure.

[0177] Regarding the secondary monitoring results in S120, they include the large area monitoring results and / or the joint monitoring results. The large area monitoring can include the monitoring of multiple adjacent or related target areas.

[0178] In some embodiments, the area range that an edge controller can monitor is limited. In this case, a large area is divided into multiple target areas. The multiple target areas report the monitoring results of each target area to the central controller, and the central controller summarizes to obtain the large area monitoring results of the large area.

[0179] In some embodiments, as Figure 6 shown, the secondary monitoring for the central controller to obtain the secondary monitoring results may include: S121: Receive the primary monitoring results sent by the edge controller of the oil and gas transportation system; S122: Generate a fifth feature map according to the primary monitoring results; S123: Obtain the secondary monitoring results according to the fifth feature map.

[0180] In some embodiments, the central controller fuses the primary monitoring results of multiple target areas received to obtain the secondary monitoring results.

[0181] In some embodiments, the central controller generates a fifth feature map based on the first-level monitoring results of multiple target areas received; and generates a second-level monitoring result based on the fifth feature map and the sixth feature map. The sixth feature map is at least used to characterize the association relationship between target areas. For example, the association relationship represents the positional relationship between different target areas, the connection relationship, the positional relationship between the destination areas for transportation, etc.

[0182] In some embodiments, obtaining the second-level monitoring result according to the fifth feature map includes: The central controller generates a second-level monitoring result based on the fifth feature map, the sixth feature map, and the seventh feature map. The seventh feature map is used to characterize the overall oil and gas transportation plan or transportation demand.

[0183] In some embodiments, if a preset condition is satisfied, when the central controller receives the first-level monitoring result from the edge controller, it will also receive at least one of the following: the first feature map, the second feature map, the third feature map, the first prediction result, the second prediction result, the third prediction result, the key feature elements of the first feature map, the key feature elements of the second feature map, the key feature elements of the third feature map; the first prediction result is generated by the edge controller according to the first feature map; the second prediction result is generated by the edge controller according to the second feature map; the third prediction result is generated by the edge controller according to the third feature map.

[0184] In some embodiments, the first feature map, the second feature map, the third feature map, the first prediction result, the second prediction result, the third prediction result, the key feature elements of the first feature map, the key feature elements of the second feature map, and the key feature elements of the third feature map can be used by the central controller to correct or verify the first-level monitoring result.

[0185] In some embodiments, the edge controller also determines the confidence level of the first-level monitoring result generated by itself. This confidence level is synchronized to the central controller and can be used by the central controller as one of the trigger factors for determining whether to correct or verify the first-level monitoring result.

[0186] In some embodiments, when the central controller verifies or corrects the first-level monitoring result, it can comprehensively determine whether the first-level monitoring result of a certain target area is abnormal based on the first-level monitoring results of multiple target areas, and if it is abnormal, it will initiate verification or correction.

[0187] In some embodiments, if the confidence level received from the edge controller is low, the correction or verification of the first-level monitoring result is initiated.

[0188] In some embodiments, if the value of the first-level monitoring result is within an abnormal range, the correction or verification of the first-level monitoring result is initiated.

[0189] When monitoring large areas to obtain large area monitoring results, the central controller receives information uploaded by each edge controller and performs secondary monitoring on the oil and gas pipelines in the target area belonging to the same large area. For example, for pipeline areas in different countries or regions, the central controller analyzes the overall operating status of the area, including average pressure, flow rate change trends, etc. If the flow rate in a certain area is found to continue to decline, the central controller determines through data analysis that there may be a leak or equipment failure in the pipeline in the area.

[0190] When jointly monitoring to obtain joint monitoring results, the central controller jointly monitors multiple areas. When a problem occurs in a certain area, it considers its impact on other areas. For example, if a pipeline leaks in one area, the central controller coordinates other areas to adjust the delivery flow based on the pipeline connection relationship and oil and gas transportation network model of each area to avoid serious impact on the entire transportation system. At the same time, the central controller mobilizes maintenance resources and arranges professionals to go to the fault area for repairs.

[0191] For large-area monitoring, the central controller generates the fifth feature map from the first-level monitoring results of multiple target areas, and combines the sixth feature map (characterizing the correlation between target areas) and the seventh feature map (characterizing the overall oil and gas transportation plan or transportation demand). These feature maps are processed using graph neural networks (GNNs), which can effectively analyze graph structure data and mine complex relationships between target areas. GNNs can be used to discover flow correlations and pressure conduction relationships between pipelines in different regions, thereby making a more comprehensive and accurate assessment of the oil and gas transportation situation in the entire region. When analyzing oil and gas pipelines in multiple adjacent areas, GNNs can determine whether anomalies in a certain area will affect other areas based on the positional relationship, connectivity relationship, and positional relationship of the transportation destination area, combined with the first-level monitoring results of each area, and how to adjust the transportation strategy to ensure the stability of the overall transportation.

[0192] In joint monitoring, the central controller can use deep reinforcement learning algorithms. Deep reinforcement learning combines the powerful feature extraction capabilities of deep learning and the decision optimization capabilities of reinforcement learning, allowing the central controller to make smarter decisions based on real-time status information in multiple regions. Based on the pipeline connection relationship and oil and gas transportation network model of each region, the central controller continuously tries different control strategies through deep reinforcement learning algorithms, and adjusts decisions based on feedback results to achieve optimal control of the entire transportation system. When a pipeline leak occurs in a certain area, the deep reinforcement learning algorithm can quickly calculate the best flow adjustment plan, coordinate other areas to adjust the transportation flow, and reasonably arrange maintenance resources to minimize the impact of the accident.

[0193] Exemplarily, an offshore oil and gas field transports oil and gas to a land terminal through subsea pipelines, and the entire transportation system faces complex marine environments and high-risk factors. The monitoring method of the present disclosure is adopted to ensure the safety of the transportation system.

[0194] The edge controller considers the life cycle of the subsea pipeline. Since the seabed environment has a greater impact on pipeline corrosion, it conducts primary monitoring by combining the pipeline corrosion detection data collected on-site (such as the pipeline potential changes obtained through potential detection sensors). For example, in the initial stage of pipeline operation, the edge controller predicts the remaining service life of the pipeline anti-corrosion coating based on the designed service life of the pipeline anti-corrosion coating and the currently detected corrosion rate. When the detected corrosion rate accelerates, it issues a warning in a timely manner.

[0195] The edge controller collects the status parameters on the offshore platform and the pipeline in real time, such as the operating status of the equipment on the platform and the oil and gas composition in the pipeline. When the detected abnormal increase in the impurity content in the oil and gas is detected, the edge controller judges, based on the life cycle of the pipeline and historical data, that it may be a malfunction of the offshore oil and gas production equipment that affects the quality of the oil and gas, and immediately takes measures, such as adjusting the operating parameters of the filtering equipment, and reports the situation to the central controller.

[0196] The central controller conducts secondary monitoring on the pipelines in the offshore oil and gas field area and the land terminal area. It analyzes the production situation in the offshore area and the receiving situation in the land terminal to judge the overall operating efficiency of the transportation system. For example, when the central controller finds that the production volume of the offshore oil and gas field increases, but the receiving volume at the land terminal does not increase correspondingly, by analyzing the status information of each area, it judges that it may be that the transportation capacity of the subsea pipeline is limited or there is a problem with the processing equipment at the land terminal.

[0197] During the joint monitoring process, the central controller conducts coordinated management of the offshore and onshore areas. If a leakage accident occurs in the offshore pipeline, the central controller immediately coordinates the offshore platform to close the relevant valves to reduce the leakage volume, and at the same time notifies the land terminal to adjust the receiving plan and arrange standby storage facilities. And organize professional offshore maintenance teams and onshore support teams for joint emergency treatment.

[0198] The central controller obtains the secondary monitoring results based on the information provided by the edge controller, and using a neural network for feature processing can effectively improve the accuracy of the monitoring results.

[0199] Exemplarily, the central controller obtains the information reported by the edge controller, which includes the primary monitoring results of multiple target areas, and generates the fifth feature map based on this. The fifth feature map is subjected to a convolution operation using a CNN. Through convolution kernels of different sizes and strides, local features therein are extracted. For parameters such as pressure and flow rate in each area of the oil and gas transportation system, the CNN can capture the change trends and local abnormal features of the data. When processing the flow rate data of multiple areas, the convolution kernel can focus on the flow rate fluctuations in different areas and extract features such as flow rate mutations and abnormal stability. The pooling operation can reduce the dimension of the features after convolution, reduce the computational amount while retaining key information, and make subsequent processing more efficient. Through multiple layers of convolution and pooling of the CNN, complex monitoring data can be converted into more representative feature vectors, providing strong support for obtaining accurate subsequent results.

[0200] Exemplarily, the sixth feature map (representing the association relationship between target areas) and the seventh feature map (representing the overall oil and gas transportation plan or transportation demand) are combined, and these features are fused with the features extracted from the fifth feature map. An RNN, especially a long short-term memory network (LSTM), is used to process the fused features. The LSTM can effectively process sequential data, capture the dynamic associations between different areas and the features that change over time. Considering the pipeline connection relationships and the oil and gas transportation network model of different areas, the LSTM can learn how the changes in one area affect other areas and how the overall transportation plan affects each area. When a pipeline leaks in a certain area, the LSTM can analyze the scope and degree of the impact on other areas based on the area association relationship and the transportation plan, so as to more accurately evaluate the state of the overall transportation system.

[0201] Exemplarily, an attention mechanism is introduced in the neural network processing process to assign different weights to different features. For features related to the current monitoring focus, such as when an abnormality occurs in a certain area, the features of that area and the areas associated with it are given higher weights; while for features with less impact, their weights are reduced. In this way, the neural network can pay more attention to key information and improve the accuracy of the results. When analyzing the abnormal pipeline pressure in a certain area, the attention mechanism will increase the attention to the pressure, flow rate and related parameter features of the surrounding areas of that area, and ignore some temporarily irrelevant information, enabling the central controller to more accurately judge the impact of the abnormal situation on the overall system.

[0202] Exemplarily, to enable the neural network to accurately process features and obtain precise results, a large amount of historical monitoring data is required to train the model. This data should cover the oil and gas transportation conditions under different operating conditions, including various scenarios such as normal operation, abnormal leakage, and equipment failure. During the training process, a suitable loss function is selected, such as the mean squared error loss function (MSE) for regression problems and the cross-entropy loss function for classification problems. The parameters of the neural network are continuously adjusted through the backpropagation algorithm to make the prediction results of the model closer to the actual situation. The model is regularly evaluated and optimized, and the model is updated according to new monitoring data to adapt to the dynamic changes of the oil and gas transportation system, ensuring the accuracy and timeliness of the model.

[0203] For example, the urban gas transmission pipeline network covers the entire city and connects numerous residential and commercial users. To ensure the normal gas supply to urban residents and the safe operation of the pipeline network, the monitoring method of the present disclosure is adopted. The edge controller performs primary monitoring based on the life cycle information of urban gas pipelines, such as the construction time and design capacity, and combines the user gas load data collected on-site. For example, in the initial stage of the operation of gas pipelines in newly built urban areas, the edge controller predicts the gas demand of users according to the planned population and commercial development of the area. When the actually collected gas load exceeds the predicted value, the edge controller timely adjusts the pressure and flow rate of the pipeline to ensure stable gas supply. The edge controller real-time collects the state parameters of the pipeline, such as the leakage detection data of the pipeline and the on-off state of the valve. When it detects a slight leakage in a certain section of the pipeline, the edge controller determines whether immediate repair or temporary plugging measures are required based on the life cycle and historical maintenance records of the pipeline, and reports the relevant information to the central controller.

[0204] The central controller performs secondary monitoring on the gas pipelines in different regions of the city, analyzes the gas demand and supply situation in each region, and ensures the balance between supply and demand. For example, during the winter heating period, the central controller adjusts the gas supply volume in different regions in real time according to the temperature changes and user feedback in each region. When the gas demand in a certain region suddenly increases, the central controller coordinates the gas volume allocation of pipelines in other regions.

[0205] The central controller conducts joint monitoring of the entire urban gas transmission pipeline network. When a large-scale failure or emergency occurs, such as some pipelines being damaged due to an earthquake, the central controller quickly activates the emergency plan and conducts emergency responses in conjunction with multiple departments such as fire and maintenance. It commands the maintenance personnel to rush to the failure area for emergency repair, and at the same time notifies the users in the relevant areas to take safety precautions.

[0206] During the oil and gas transportation process, the edge controller and the central controller achieve system monitoring and exception handling by processing the feature maps. When encountering common exceptions such as pipeline leaks and abnormal pressures caused by equipment failures, the processing flows and instruction issuances of the two have their own focuses, jointly ensuring the safe and stable operation of the oil and gas transportation system.

[0207] For the abnormal handling of pipeline leaks: Edge controller: The edge controller receives multi-source data collected by the sensing subsystem, such as pressure, flow rate, temperature, ultrasound, infrared, etc., and generates a first feature map containing pipeline equipment parameters (pipe diameter, wall thickness, etc.), maintenance parameters (historical leak records, etc.), life cycle parameters, and real-time status parameters. By changing the permutation and combination of the parameters in the feature map, a second feature map is generated, and at the same time, a third feature map is obtained according to the oil and gas transmission plan. If the first feature map shows a sudden drop in pressure in a certain section of the pipeline, abnormal flow rate, and the ultrasound sensor detects an abnormal signal, the second feature map further highlights the changes in these abnormalities in the time series. Combining the third feature map to judge that the abnormality affects the transmission plan, the edge controller comprehensively obtains a primary monitoring result that the pipeline may be leaking. At this time, the edge controller starts local emergency handling, closes the nearby valves to prevent the leakage from expanding, re-plans the transmission path in this area, and enables the standby pipeline. At the same time, it reports detailed exception information to the central controller, including the leakage location (determined by the sensor location), the possible leakage volume (estimated based on the changes in pressure and flow rate), the duration of the abnormality, etc., as well as the first, second, and third feature maps or key feature elements, facilitating further analysis by the central controller.

[0208] Central controller: Receives the information uploaded by each edge controller to generate a fifth feature map, combines the sixth feature map representing the regional association relationship and the seventh feature map of the overall transmission plan. If the fifth feature map shows an abnormality in a certain area, the sixth feature map indicates that this area is closely related to the surrounding areas, and the seventh feature map shows that the abnormality in this area affects the overall transmission plan, the central controller determines this as a serious problem. Sends instructions to the edge controllers in this area and the surrounding areas, requiring them to strengthen the monitoring of relevant pipelines, increase the detection frequency of ultrasound and infrared sensors, and use mobile monitoring devices such as drones to conduct a comprehensive inspection of the pipelines. Coordinates other areas to adjust the transmission flow rate to ensure the overall transmission stability. Organizes a professional repair team to carry corresponding equipment and materials to rush to repair the leakage area, and notifies the surrounding residents and units to take safety precautions to prevent safety accidents.

[0209] For the abnormal handling of pressure caused by equipment failures: Edge Controller: If the first feature map shows that the power of the pump is abnormal and the pipeline pressure exceeds the normal range, the second feature map presents the development trend of these abnormalities, and the third feature map indicates that the current transportation task is affected, the edge controller determines that the equipment failure causes the pressure abnormality. Stop the operation of the faulty equipment, start the standby pump, and adjust the opening of the surrounding valves to stabilize the pipeline pressure. Report the information of the faulty equipment (type, number, location), the pressure abnormality data (current pressure value, change trend), the possible cause of the failure (speculated based on historical data and current parameters), and the relevant feature map data to the central controller.

[0210] Central Controller: Analyze the fifth, sixth, and seventh feature maps. If it is found that the equipment failure affects the pressure and flow in multiple regions and has a greater impact on the overall transportation plan. Send instructions to the edge controllers in the affected regions, requiring them to closely monitor the changes in pipeline pressure and flow and adjust the transportation parameters according to the actual situation. Arrange technical personnel to remotely guide or go to the site to repair the faulty equipment. Before the equipment is repaired, optimize the overall transportation strategy, such as adjusting the operating parameters of other equipment, to ensure that the oil and gas transportation meets the needs of key users, and at the same time avoid other equipment from failing due to overloading.

[0211] In some embodiments, the method further includes: The central controller obtains the oil and gas transportation plan, determines the oil and gas transportation load of each region according to the oil and gas transportation plan, and generates a first type of monitoring instruction and a second type of monitoring instruction based on the oil and gas transportation load and in combination with the state parameters of the oil and gas pipelines in each region reported by the edge controller; The edge controller performs the first type of monitoring on the oil and gas pipeline according to the first type of monitoring instruction and performs the second type of monitoring on the oil and gas pipeline according to the second type of monitoring instruction; the first type of monitoring is daily monitoring; the second type of monitoring is different from the first type of monitoring.

[0212] Exemplarily, the second type of monitoring is key monitoring or abnormal monitoring other than daily monitoring. For example, when a certain section of the oil and gas pipeline has just been repaired and starts transporting oil and gas again, the edge controller will receive the second type of monitoring for key monitoring at this time. For example, at this time, the edge controller can generate a patrol instruction according to the second type of monitoring instruction to control the mobile monitoring device to conduct patrols to increase key monitoring.

[0213] In some embodiments, the state parameters detected by the sensor subsystem may include a first type of parameter and a second type of parameter. The parameter types of the first type of parameter and the second type of parameter are different or the dimensional sizes of the parameters are different. The first type of parameter can be used for daily monitoring. The second type of parameter can correspond to the second type of monitoring.

[0214] In some embodiments, the first type of monitoring instructions require the edge controller to monitor the oil and gas pipeline according to the daily monitoring standards. The specific instruction content includes: collecting the pressure, temperature, and flow rate data of the pipeline every 10 minutes; checking whether the valves of the pipeline are in the normal open or closed state; monitoring whether there are any abnormal changes in the surrounding environment of the pipeline (such as whether there is construction, whether there are people approaching, etc.).

[0215] The second type of monitoring instructions: monitoring instructions generated for specific situations. For example, when the pipeline pressure in a certain area fluctuates greatly, the central controller issues the second type of monitoring instructions, requiring the edge controller to collect the pressure data every 2 minutes and analyze the pressure change trend; check whether there are signs of pipeline leakage, and increase the inspection frequency of key parts of the pipeline (such as welds, joints, etc.).

[0216] Both the first type of monitoring instructions and the second type of monitoring instructions are centered around the safe operation of the oil and gas transportation system, aiming to ensure the normal supply of gas and the safety of users. The differences between the first type of monitoring instructions and the second type of monitoring instructions are as follows: the first type of monitoring instructions are routine monitoring arrangements to ensure the basic operation monitoring of the pipe network; the second type of monitoring instructions are enhanced monitoring when abnormal gas usage situations or abnormal pipeline parameters occur, with stronger pertinence and timeliness.

[0217] During the oil and gas transportation process, the central controller generates different types of monitoring instructions based on the oil and gas transportation plan and the status parameters reported by the edge controller to cope with various abnormal situations. The following presents specific embodiments in combination with two common abnormal situations: pipeline corrosion and abnormal flow.

[0218] Regarding pipeline corrosion anomalies: Central controller: After obtaining the oil and gas transportation plan, it determines the oil and gas transportation load of each region according to the gas usage demand and pipeline layout of each region. After receiving the status parameters of the oil and gas pipeline in a certain region reported by the edge controller, it is found that the corrosion rate of a certain section of the pipeline exceeds the normal range. Combining the transportation load, the central controller judges that the pipeline corrosion situation may affect the safety of oil and gas transportation. It generates the first type of monitoring instructions, requiring the edge controller to collect the pressure, temperature, and flow rate data of this pipeline according to the daily monitoring standards every certain period (such as 10 minutes); check the pipeline valve status; monitor whether there are any abnormal changes in the surrounding environment of the pipeline. It generates the second type of monitoring instructions. Regarding the pipeline corrosion anomaly, it requires the edge controller to encrypt the collection of corrosion-related data of this pipeline, such as detecting the change in pipeline wall thickness every 5 minutes through an ultrasonic thickness gauge; using an infrared sensor to monitor the surface temperature distribution of the pipeline to check whether there is any local temperature anomaly caused by corrosion; increasing the inspection frequency of this pipeline, arranging personnel to focus on checking the severely corroded parts, and reporting any abnormal situation in a timely manner.

[0219] Edge Controller: After receiving the first type of monitoring instruction, it executes according to the daily monitoring standards to ensure continuous monitoring of the pipeline's normal parameters. After receiving the second type of monitoring instruction, it immediately adjusts the monitoring strategy. It arranges for professional personnel to use ultrasonic thickness gauges and infrared sensors to detect the pipeline more frequently, and uploads the collected data to the central controller in real time. If it is found that the pipeline corrosion situation worsens, such as a significant reduction in wall thickness or the appearance of local high-temperature points, the edge controller starts the local emergency procedure, closes the relevant valves, prevents pipeline rupture and leakage, and reports to the central controller urgently, waiting for further instructions.

[0220] Regarding abnormal flow: Central Controller: After determining the transportation load of each region according to the oil and gas transportation plan, it is found that the actual flow in a certain region deviates greatly from the planned flow and lasts for a long time. Combining the pipeline status parameters reported by the edge controller, it judges that there may be problems such as pipeline leakage or equipment failure. It generates the first type of monitoring instruction, requiring the edge controller to continue to conduct routine monitoring of pipeline pressure, temperature, valve status, etc. according to the daily monitoring standards. It generates the second type of monitoring instruction, requiring the edge controller to focus on monitoring the flow changes of the pipeline in this region, collect flow data every 2 minutes, and analyze the flow change trend; check whether the equipment related to the flow, such as pumps, valves, etc. are operating normally; use drones equipped with vision sensors to conduct a comprehensive inspection of the pipeline in this region to check for obvious leakage signs.

[0221] Edge Controller: According to the first type of monitoring instruction, it maintains the daily monitoring work. According to the second type of monitoring instruction, it strengthens the monitoring of the flow and equipment inspection. If it is found that the abnormal flow is caused by a certain valve failure, the edge controller tries to remotely adjust the valve opening. If the problem cannot be solved, it immediately closes the valve, starts the standby valve, and reports the information and handling situation of the faulty equipment to the central controller. During the entire handling process, it continuously uploads the relevant data to the central controller so that the central controller can timely understand the situation and make more reasonable decisions.

[0222] Combined with Figure 7 As shown, an embodiment of the present application provides an electronic device, including a processor 10 and a memory 11. Optionally, the device may further include a communication interface 12 and a bus 9. Among them, the processor 10, the communication interface 12, and the memory 11 can complete mutual communication through the bus 9. The communication interface 12 can be used for information transmission. The processor 10 can call the logical instructions in the memory 11 to execute the queue-based voiceprint data processing method of the above embodiment.

[0223] In addition, when the logical instructions in the above-mentioned memory 11 are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium.

[0224] The memory 11 serves as a computer-readable storage medium and can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of the present application. The processor 10 executes functional applications and data processing by running the program instructions / modules stored in the memory 11, that is, implements the monitoring method of the queue-based oil and gas transportation system in the above embodiments.

[0225] The memory 11 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 11 may include high-speed random access memory and may also include non-volatile memory.

[0226] The electronic device can be used as an edge controller, a central controller, or an edge control terminal, etc.

[0227] The embodiments of the present application provide a computer program product. The computer program product includes a computer program stored on a storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is enabled to execute the monitoring method of the oil and gas transportation system described above.

[0228] The above computer-readable storage medium can be a transient computer-readable storage medium or a non-transient computer-readable storage medium.

[0229] The technical solutions of the embodiments of the present application can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes one or more instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium can be a non-transient storage medium, including: various media such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks, or optical discs that can store program codes, or can also be a transient storage medium.

[0230] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0231] The embodiments or examples disclosed in this application are not exhaustive. They are only illustrations of some embodiments or examples and do not constitute specific limitations on the protection scope of this disclosure. Without conflict, each step in a certain embodiment or example can be implemented as an independent example, and the steps can be combined arbitrarily. For example, the solution after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be exchanged arbitrarily. In addition, the optional methods or optional examples in a certain embodiment or example can be combined arbitrarily; furthermore, the various embodiments or examples can be combined arbitrarily. For example, some or all of the steps of different embodiments or examples can be combined arbitrarily, and a certain embodiment or example can be combined arbitrarily with the optional methods or optional examples of other embodiments or examples.

[0232] In several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0233] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0234] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0235] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0236] The above are only the preferred embodiments of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. An oil and gas transportation monitoring method based on artificial intelligence AI, characterized in that, The method is executed by an edge controller of an oil and gas transportation system, and the method includes: Obtaining status parameters of a transportation device of the oil and gas transportation system; Generating a first feature map according to the status parameters of the transportation device of the oil and gas transportation system; wherein, the first feature map is a three-dimensional feature map; the first feature map includes a plurality of two-dimensional feature maps stacked in sequence; one of the two-dimensional feature maps corresponds to one of the transportation devices; Generating a second feature map according to the first feature map; the second feature map is a three-dimensional feature map; the sorting of the feature elements in the second feature map is different from that of the feature elements in the first feature map; Generating a primary monitoring result according to the first feature map and the second feature map.

2. The method according to claim 1, wherein Generating a primary monitoring result according to the first feature map and the second feature map, including: obtaining a primary monitoring result according to the first feature map, the second feature map, and a third feature map; the third feature map is generated according to an oil and gas transportation plan or an oil and gas transportation task within a target area.

3. The method according to claim 2, wherein The method further includes: Obtaining a correction result of the primary monitoring result according to a fourth feature map; the fourth feature map is generated according to the capability parameters of the edge controller.

4. The method according to claim 2, characterized in that The method further includes: If a preset condition is satisfied, when sending the primary monitoring result to a central controller, sending at least one of the following to the central controller: The first feature map, the second feature map, the third feature map, a first prediction result, a second prediction result, a third prediction result, key feature elements of the first feature map, key feature elements of the second feature map, key feature elements of the third feature map; The first prediction result is generated by the edge controller according to the first feature map; The second prediction result is generated by the edge controller according to the second feature map; The third prediction result is generated by the edge controller according to the third feature map.

5. An oil and gas transportation monitoring method based on artificial intelligence AI, characterized in that, The method is executed by a central controller of the oil and gas transportation system, and the method includes: Receiving a primary monitoring result sent by the edge controller of the oil and gas transportation system; Generating a fifth feature map according to the primary monitoring result; Obtaining a secondary monitoring result according to the fifth feature map.

6. The method according to claim 5, characterized in that, The obtaining the secondary monitoring result according to the fifth feature map includes: Generating a secondary monitoring result according to the fifth feature map and a sixth feature map; the sixth feature map is at least used to characterize the association relationship between different target areas.

7. The method according to claim 6, wherein The obtaining the secondary monitoring result according to the fifth feature map includes: generating a secondary monitoring result according to the fifth feature map and a seventh feature map, and the seventh feature map is used to characterize the overall oil and gas transportation plan or transportation demand.

8. The method according to claim 6, wherein The secondary monitoring result includes a regional monitoring result and / or a joint monitoring result; the regional monitoring result includes monitoring results of multiple target areas; the joint monitoring result includes monitoring results obtained from the correlation between multiple target areas.

9. An oil and gas transportation monitoring system based on artificial intelligence AI, characterized in that, Including: An edge controller, configured to execute the method according to any one of claims 1 to 5; A central controller, configured to execute the method according to any one of claims 6 to 8.

10. An electronic device, characterized in that, Including: A processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the AI-based oil and gas transportation monitoring method according to any one of claims 1 to 5 or 6 to 8.

Images