A pipeline mixed oil detection method and device for product oil transportation

Abstract

The application provides a pipeline mixed oil detection method and device for product oil transportation, and relates to the technical field of product oil transportation.In the application, first parameter information corresponding to a target pipeline at a first time is acquired, the target pipeline comprises a plurality of sub-pipelines, and the first parameter information comprises an upstream flow value, a downstream flow value, an upstream pressure head value and a downstream pressure head value of each sub-pipeline in the plurality of sub-pipelines; based on the first parameter information, second parameter information corresponding to the target pipeline at the first time is obtained through a trained first neural network model, the second parameter information comprises mixed oil section position information and a mixed oil section length value; in the case that the mixed oil section length value is greater than or equal to a preset threshold value, it is determined that a mixed oil detection result of the target pipeline is mixed oil abnormality.The method provided by the application can realize rapid detection of mixed oil in a transportation pipeline, so that workers can quickly understand the mixed oil condition in the current transportation pipeline.

Description

Technical Field

[0001] This invention relates to the field of refined oil transportation technology, and in particular to a pipeline mixing detection method and apparatus for refined oil transportation. Background Technology

[0002] Refined petroleum products refer to gasoline, kerosene, diesel, and other alternative fuels such as ethanol gasoline and biodiesel that meet quality requirements and have the same purpose. The transportation of refined petroleum products typically includes four modes: waterway, rail, road, and pipeline. Compared to the other modes, pipeline transportation offers advantages such as strong adaptability to terrain and climate, low fuel loss, reduced likelihood of accidents, ease of automation and faster turnaround times, and lower overall transportation costs. Currently, refined petroleum product transportation often employs a method of sequentially transporting multiple types of petroleum products.

[0003] Oil mixing in pipelines is a significant issue in sequential oil transportation, greatly impacting the operation and management of these pipelines. Oil mixing occurs when two different types of oil come into direct contact during transport, causing mutual wetting and diffusion at the interface, resulting in oil blending. The presence of oil mixing not only affects the quality of the transported oil but also necessitates additional investment in post-transfer treatment.

[0004] The existing technologies cannot quickly detect and display mixed oil in the pipeline, making it impossible for staff to quickly understand the current situation of mixed oil in the pipeline, and thus impossible to quickly deal with the mixed oil, which seriously affects the quality of the transported oil. Summary of the Invention

[0005] This invention provides a method and apparatus for detecting mixed oil in pipelines used for transporting refined oil products. It enables rapid detection of mixed oil in the pipeline, allowing workers to quickly understand the current mixed oil situation and thus quickly handle the mixed oil, preventing it from affecting the quality of the transported oil.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] In a first aspect, a pipeline mixing detection method for transporting refined oil is provided. The method includes: acquiring first parameter information of a target pipeline at a first moment, the target pipeline including multiple sub-pipelines, the first parameter information including upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of each sub-pipeline; based on the first parameter information, obtaining second parameter information of the target pipeline at the first moment through a trained first neural network model, the second parameter information including mixing section location information and mixing section length; and determining that the mixing detection result of the target pipeline is an oil mixing anomaly if the mixing section length is greater than or equal to a preset threshold.

[0008] In one possible implementation of the first aspect, based on the first parameter information, the second parameter information corresponding to the target pipeline at the first moment is obtained through a trained first neural network model, including: inputting the first parameter information into the first neural network model and outputting the first prediction information corresponding to the first parameter information, the first prediction information including the predicted position of the mixing section and the predicted length of the mixing section of the target pipeline; inputting the first prediction information into the trained second neural network model and outputting the second prediction information, the second prediction information including the predicted upstream flow rate, predicted downstream flow rate, predicted upstream pressure head, and predicted downstream pressure head of each of the multiple sub-pipes corresponding to the first prediction information; inputting the second prediction information into the first neural network model and outputting the second parameter information corresponding to the target pipeline at the first moment.

[0009] In one possible implementation of the first aspect, before obtaining the second parameter information corresponding to the target pipeline at the first moment through the trained first neural network model based on the first parameter information, the method further includes: acquiring a training sample set, the training sample set including the third parameter information of each pipeline in multiple pipelines, each pipeline in multiple pipelines including multiple sub-pipelines, the third parameter information of each pipeline including the upstream flow rate value, downstream flow rate value, upstream pressure head value and downstream pressure head value of each sub-pipeline at each moment in multiple moments, and the mixing section location information and mixing section length value of each pipeline at each moment in multiple moments; training the first neural network model and the second neural network model according to the training sample set; and obtaining the trained first neural network model and the second neural network model when the first neural network model and the second neural network model meet the preset accuracy.

[0010] In one possible implementation of the first aspect, the method further includes: generating a three-dimensional model of the target pipeline based on the second parameter information, the three-dimensional model being used to display the oil mixing information in each of the multiple sub-pipes of the target pipeline; and displaying the three-dimensional model on a first interface.

[0011] In one possible implementation of the first aspect, generating a three-dimensional model of the target pipeline based on the second parameter information includes: obtaining the length information of each sub-pipeline included in the target pipeline and the oil information in the target pipeline; and generating a three-dimensional model of the target pipeline based on the length information of each sub-pipeline included in the target pipeline, the oil information in the target pipeline, and the second parameter information.

[0012] In one possible implementation of the first aspect, the method further includes: generating an alarm message when the oil mixing detection result of the target pipeline is an abnormal oil mixing; displaying the alarm message on a first interface, the alarm message being used to prompt the operator to handle the oil mixing in the target pipeline.

[0013] In one possible implementation of the first aspect, each of the multiple sub-pipes included in the target pipeline is equipped with a flow rate acquisition device and a pressure head acquisition device upstream and downstream, respectively, to acquire the first parameter information of the target pipeline at a first moment, including: acquiring the upstream flow rate and downstream flow rate of each of the multiple sub-pipes at the first moment through the flow rate acquisition device; acquiring the upstream pressure head and downstream pressure head of each of the multiple sub-pipes at the first moment through the pressure head acquisition device; and obtaining the first parameter information of the target pipeline at the first moment based on the upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of each of the multiple sub-pipes.

[0014] The beneficial effects of the present invention are as follows: The method provided by the present invention can realize the rapid detection of mixed oil in the conveying pipeline, so that the staff can quickly understand the current situation of mixed oil in the conveying pipeline, thereby realizing the rapid treatment of mixed oil and avoiding the impact of mixed oil on the quality of the conveyed oil.

[0015] Secondly, a pipeline mixing detection device for transporting refined oil is provided. The device includes: an acquisition unit for acquiring first parameter information of a target pipeline at a first moment, the target pipeline including multiple sub-pipelines, the first parameter information including upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of each sub-pipeline; a determination unit for obtaining second parameter information of the target pipeline at the first moment based on the first parameter information and through a trained first neural network model, the second parameter information including mixing section location information and mixing section length; and a detection unit for determining that the mixing detection result of the target pipeline is an oil mixing anomaly when the mixing section length is greater than or equal to a preset threshold.

[0016] In one possible implementation of the second aspect, the above-mentioned apparatus further includes a generation unit and a display unit; the generation unit is used to generate a three-dimensional model of the target pipeline according to the second parameter information, the three-dimensional model being used to display the oil mixing information in each of the multiple sub-pipes of the target pipeline; the display unit is used to display the three-dimensional model on a first interface.

[0017] In one possible implementation of the second aspect, the generating unit is further configured to generate alarm information when the oil mixing detection result of the target pipeline is abnormal; the display unit is further configured to display the alarm information on the first interface, the alarm information being used to prompt the operator to handle the oil mixing in the target pipeline.

[0018] Thirdly, an electronic device is provided, comprising a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform a pipeline oil mixing detection method for transporting refined oil as described in any implementation of the first aspect.

[0019] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform a pipeline oil mixing detection method for transporting refined oil as described in any implementation of the first aspect.

[0020] Fifthly, a computer program product is provided that, when run on a computer, causes the computer to execute a pipeline oil mixing detection method for transporting refined oil products as described in any implementation of the first aspect.

[0021] It is understood that the beneficial effects achieved by the pipeline oil mixing detection device for refined oil transportation described in the second aspect above, the electronic equipment described in the third aspect, the computer-readable storage medium described in the fourth aspect, and the computer program product described in the fifth aspect can be referred to the beneficial effects in the first aspect and any possible design mode therein, and will not be repeated here. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the hardware structure of a pipeline oil mixing detection device according to an embodiment of the present invention.

[0023] Figure 2 This is a flowchart illustrating a pipeline oil mixing detection method for refined oil transportation, as shown in an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of the structure of a target pipeline according to an embodiment of the present invention;

[0025] Figure 4 This is a flowchart illustrating another pipeline oil mixing detection method for finished oil transportation, as shown in an embodiment of the present invention.

[0026] Figure 5 This is a flowchart illustrating another pipeline oil mixing detection method for refined oil transportation according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of the structure of a pipeline oil mixing detection device according to an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of another pipeline oil mixing detection device according to an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0030] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0031] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0032] Refined petroleum products refer to gasoline, kerosene, diesel, and other alternative fuels such as ethanol gasoline and biodiesel that meet quality requirements and have the same purpose. The transportation of refined petroleum products typically includes four modes: waterway, rail, road, and pipeline. Compared to the other modes, pipeline transportation offers advantages such as strong adaptability to terrain and climate, low fuel loss, reduced likelihood of accidents, ease of automation and faster turnaround times, and lower overall transportation costs. Currently, refined petroleum product transportation often employs a method of sequentially transporting multiple types of petroleum products.

[0033] Oil mixing in pipelines is a significant issue in sequential oil transportation, greatly impacting the operation and management of these pipelines. Oil mixing occurs when two different types of oil come into direct contact during transport, causing mutual wetting and diffusion at the interface, resulting in oil blending. The presence of oil mixing not only affects the quality of the transported oil but also necessitates additional investment in post-transfer treatment.

[0034] The existing technologies cannot quickly detect and display mixed oil in the pipeline, making it impossible for staff to quickly understand the current situation of mixed oil in the pipeline, and thus impossible to quickly deal with the mixed oil, which seriously affects the quality of the transported oil.

[0035] In view of this, embodiments of the present invention provide a pipeline mixing detection method and apparatus for transporting refined oil products. The method includes: acquiring first parameter information corresponding to a target pipeline at a first moment, wherein the target pipeline includes multiple sub-pipelines, and the first parameter information includes the upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of each sub-pipeline; based on the first parameter information, obtaining second parameter information corresponding to the target pipeline at the first moment through a trained first neural network model, wherein the second parameter information includes the mixing section location information and the mixing section length value; and determining that the mixing detection result of the target pipeline is an oil mixing anomaly if the mixing section length value is greater than or equal to a preset threshold.

[0036] The method provided in this invention can quickly detect mixed oil in the pipeline, enabling workers to quickly understand the current situation of mixed oil in the pipeline, thereby achieving rapid processing of the mixed oil and preventing it from affecting the quality of the transported oil.

[0037] In some embodiments, the pipeline oil mixing detection method for refined oil transportation provided in this invention can be executed by the pipeline oil mixing detection method 100 for refined oil transportation (hereinafter referred to as the pipeline oil mixing detection device 100). As an example, the pipeline oil mixing detection device 100 can be any device with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer, etc. The specific implementation of the pipeline oil mixing detection device 100 is not limited here.

[0038] Figure 1 A schematic diagram of the structure of a pipeline oil mixing detection device 100 provided in an embodiment of the present invention is shown. The pipeline oil mixing detection device 100 includes a processor 210, a memory 220, and a communication interface 230.

[0039] The processor 210 may include one or more processing cores. The processor 210 connects to various parts within the pipeline oil mixing detection device 100 using various interfaces and lines. It executes various functions of the pipeline oil mixing detection device 100 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 220, and by calling data stored in the memory 220. Optionally, the processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0040] The memory 220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 220 may include non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a stored program area. The stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as information acquisition functions, information processing functions, etc.), and instructions for implementing the various method embodiments described above.

[0041] The communication interface 230 is used to communicate with other devices, equipment, or communication networks, such as data storage devices, image processing devices, or Ethernet, wireless access networks (RAN), wireless local area networks (WLAN), etc.

[0042] Display 240 is used to display a three-dimensional model of the target pipeline and to display alarm information in the event of abnormal oil mixing in the target pipeline.

[0043] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, communication interface 230, and display 240) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be housed within the same device, i.e., as different devices within a single device, similar to the deployment of devices or components in a distributed system.

[0044] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the pipeline oil mixing detection device 100. In other embodiments of the present invention, the pipeline oil mixing detection device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0045] The following description, in conjunction with the accompanying drawings, illustrates the method for monitoring and displaying oil and gas pipeline networks according to embodiments of the present invention.

[0046] Figure 2 This is a flowchart illustrating a pipeline mixing detection method for transporting refined oil, provided as an embodiment of the present invention. Optionally, this method can be performed by someone with... Figure 1 The pipeline oil mixing detection device 100 with the hardware structure shown is executed. The method may include the following steps:

[0047] S1. Obtain the first parameter information corresponding to the target pipeline at the first moment.

[0048] Specifically, the target pipeline includes multiple sub-pipelines, and the first parameter information includes the upstream flow rate, downstream flow rate, upstream head value, and downstream head value of each sub-pipeline.

[0049] For example, see Figure 3 , Figure 3 This is a schematic diagram of a target pipeline according to an embodiment of the present invention. The target pipeline A includes five sub-pipelines, namely sub-pipeline 1, sub-pipeline 2, sub-pipeline 3, sub-pipeline 4, and sub-pipeline 5. When two types of oil are transported sequentially in the target pipeline A, the pipeline oil mixing detection device 100 acquires the first parameter information corresponding to the target pipeline A at a first moment. The first parameter information includes the upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of each of the five sub-pipelines.

[0050] It should be understood that the target pipe A above is only an example. The target pipe may include more or fewer sub-pipes than the target pipe A above. The length of each sub-pipe may be the same or different. The embodiments of the present invention do not impose any special restrictions on the specific implementation of the target pipe.

[0051] In one possible implementation, each of the multiple sub-pipes comprising the target pipeline has a flow rate acquisition device and a head acquisition device installed upstream and downstream, respectively. Specifically, S1 includes the following steps: acquiring the first parameter information corresponding to the target pipeline at a first moment, including:

[0052] The upstream and downstream flow rates of each sub-pipe in the multiple sub-pipes are obtained at the first moment using the flow rate acquisition device; the upstream and downstream pressure head values ​​of each sub-pipe in the multiple sub-pipes are obtained at the first moment using the pressure head acquisition device; and the first parameter information of the target pipe at the first moment is obtained based on the upstream flow rate, downstream flow rate, upstream pressure head value and downstream pressure head value of each sub-pipe in the multiple sub-pipes.

[0053] It should be noted that the flow rate acquisition device can be a flow sensor, and the pressure head acquisition device can be a pressure head sensor. This embodiment of the invention does not impose any particular restrictions on the specific implementation of the flow rate acquisition device and the pressure head acquisition device.

[0054] S2. Based on the first parameter information, the second parameter information corresponding to the target pipeline at the first moment is obtained through the trained first neural network model. The second parameter information includes the position information of the mixing section and the length value of the mixing section.

[0055] Specifically, since the upstream flow rate and upstream head value of the two sub-pipes are the same, and the length of the mixed oil section inside the two sub-pipes is different, the downstream flow rate and downstream head value of the two sub-pipes are affected by the oil quality in the mixed oil section inside the pipeline, and the downstream flow rate and downstream head value of the two sub-pipes are different. Based on the above principle, the first neural network model is pre-trained. The first neural network model is used to output the location information of the mixed oil section and the length value of the mixed oil section corresponding to the sub-pipe at the first moment according to the upstream flow rate, downstream flow rate, upstream head value and downstream head value of the input sub-pipe at the first moment.

[0056] In one possible implementation, see Figure 4 The above S2 specifically includes the following steps:

[0057] S21. Input the first parameter information into the first neural network model and output the first prediction information corresponding to the first parameter information. The first prediction information includes the predicted location of the mixed oil section of the target pipeline and the predicted length of the mixed oil section.

[0058] S22. Input the first prediction information into the trained second neural network model and output the second prediction information. The second prediction information includes the upstream flow prediction value, downstream flow prediction value, upstream pressure head prediction value and downstream pressure head prediction value of each of the multiple sub-pipes corresponding to the first prediction information.

[0059] Specifically, the second neural network model is pre-trained and is used to output the predicted upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of the sub-pipe at the first moment, based on the input information of the mixing section location and length of the mixing section at the first moment.

[0060] S23. Input the second prediction information into the first neural network model and output the second parameter information corresponding to the target pipeline at the first time.

[0061] It should be understood that the way the second prediction information is input into the first neural network model to output the second parameter information corresponding to the target pipeline at the first time is the same as in S21 above, and will not be repeated here.

[0062] As described above, the method provided in this embodiment of the invention obtains the upstream flow prediction value, downstream flow prediction value, upstream pressure head prediction value, and downstream pressure head prediction value of each sub-pipe in multiple sub-pipes through a second neural network model. Then, the upstream flow prediction value, downstream flow prediction value, upstream pressure head prediction value, and downstream pressure head prediction value of each sub-pipe in multiple sub-pipes are input into a first neural network model, and the obtained second parameter information is output. This can effectively improve the accuracy of the second parameter information, thereby improving the accuracy of oil mixing prediction, so that operators can accurately understand the oil mixing situation in the target pipeline.

[0063] In some embodiments, prior to step S2 described above, the present invention further includes the following steps:

[0064] Obtain a training sample set, which includes the third parameter information of each pipe in multiple pipelines. Each pipe includes multiple sub-pipes. The third parameter information of each pipe includes the upstream flow rate, downstream flow rate, upstream head value, and downstream head value of each sub-pipe at each time point in multiple time periods, as well as the location information and length of the mixing section of each pipe at each time point in multiple time periods. Train the first neural network model and the second neural network model based on the training sample set. If the first neural network model and the second neural network model meet the preset accuracy, obtain the trained first neural network model and the second neural network model.

[0065] The training sample set includes data on abnormal oil mixing and data on normal oil mixing. If the amount of data in the training sample set is less than a preset value, the pipeline oil mixing detection device 100 expands the training sample set to obtain a training sample set with a data amount greater than the preset value.

[0066] S3. If the length of the mixed oil section is greater than or equal to the preset threshold, the mixed oil detection result of the target pipeline is determined to be an abnormal mixed oil condition.

[0067] If the length of the mixed oil section is less than a preset threshold, the oil mixing detection result of the target pipeline is determined to be normal. In this case, the operator does not need to handle the mixed oil in the pipeline.

[0068] As can be seen from S1-S3 above, the method provided by the embodiments of the present invention can quickly detect mixed oil in the conveying pipeline, so that the staff can quickly understand the current mixed oil situation in the conveying pipeline, and thus realize the rapid treatment of mixed oil, avoiding the impact of mixed oil on the quality of the conveyed oil.

[0069] However, the second parameter information mentioned above includes the location information and length value of the oil mixing section of each sub-pipe in the target pipeline. When the target pipeline includes a large number of sub-pipes, the operator cannot quickly and intuitively understand the oil mixing situation in the current target pipeline, resulting in a poor user experience.

[0070] To address the above problems, in some embodiments, see [reference] Figure 5 The method provided in this embodiment of the invention further includes the following steps:

[0071] S51. Generate a three-dimensional model of the target pipeline based on the second parameter information.

[0072] Specifically, the 3D model is used to display the oil mixing information in each of the multiple sub-pipes within the target pipeline;

[0073] Optionally, S41 above specifically includes the following steps: obtaining the length information of each sub-pipe included in the target pipeline and the oil information in the target pipeline; generating a three-dimensional model of the target pipeline based on the length information of each sub-pipe included in the target pipeline, the oil information in the target pipeline, and the second parameter information.

[0074] In other words, the pipeline oil mixing detection device 100 acquires the length information of each sub-pipe included in the target pipeline and the oil information in the target pipeline. Then, the pipeline oil mixing detection device 100 constructs a virtual three-dimensional model of the target pipeline. Next, the pipeline oil mixing detection device 100 marks each sub-pipe on the three-dimensional model based on the multiple sub-pipes included in the target pipeline. Finally, based on the second parameter information of the target pipeline, the oil information inside each sub-pipe is marked on the three-dimensional model to obtain the three-dimensional model of the target pipeline at the first moment.

[0075] S52. Display the 3D model on the first interface.

[0076] Furthermore, the pipeline oil mixing detection device 100 can also determine the second parameter information of the target pipeline at multiple consecutive moments, then generate a three-dimensional model of the target pipeline at multiple consecutive moments based on the second parameter information of the target pipeline at multiple consecutive moments, and finally generate a three-dimensional animation based on the three-dimensional model of the target pipeline at multiple consecutive moments, and display it on the first interface.

[0077] The method by which the pipeline oil mixing detection device 100 determines the second parameter information of the target pipeline at multiple consecutive moments is the same as the method by which it determines the second parameter information at the first moment. The first moment can be any one of the multiple consecutive moments, which will not be elaborated here.

[0078] As can be seen from S51-S52 above, the method provided by the embodiments of the present invention can display the three-dimensional model corresponding to the target pipeline based on the second parameter information of the target pipeline. Operators can quickly and intuitively view the oil mixing situation inside the target pipeline through the three-dimensional model and take timely action to avoid the oil quality being reduced due to excessive oil mixing length.

[0079] Furthermore, in one possible implementation, the method provided by the embodiments of the present invention further includes the following steps: generating alarm information when the oil mixing detection result of the target pipeline is abnormal; displaying the alarm information on a first interface, the alarm information being used to prompt the operator to handle the oil mixing in the target pipeline.

[0080] Specifically, the alarm information can be an image or text displayed on the first interface, or it can be a light or sound displayed on the first interface. This embodiment of the invention does not specifically illustrate the specific implementation of the alarm information.

[0081] As can be seen from the above, the embodiments of the present invention can prompt operators to deal with the mixed oil in the target pipeline in a timely manner through alarm information, so as to avoid unnecessary losses.

[0082] The above mainly describes the solutions of the embodiments of the present invention from a methodological perspective. It is understood that, in order to achieve the above functions, the pipeline oil mixing detection device 100 includes at least one of the hardware structures and software modules corresponding to each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present invention.

[0083] In this embodiment of the invention, the pipeline oil mixing detection device 100 can be divided into functional units according to the above method example. For example, the pipeline oil mixing detection device 100 can be divided into functional units corresponding to various functions, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or software functional units. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0084] For example, Figure 6 A schematic diagram of a pipeline oil mixing detection device 100 provided in an embodiment of the present invention is shown. The pipeline oil mixing detection device 100 includes: an acquisition unit 110, used to acquire first parameter information corresponding to a target pipeline at a first moment, the target pipeline including multiple sub-pipelines, the first parameter information including the upstream flow rate value, downstream flow rate value, upstream pressure head value and downstream pressure head value of each sub-pipeline; a determination unit 120, used to obtain second parameter information corresponding to the target pipeline at the first moment based on the first parameter information and through a trained first neural network model, the second parameter information including the oil mixing section location information and the oil mixing section length value; and a detection unit 130, used to determine that the oil mixing detection result of the target pipeline is an oil mixing anomaly when the oil mixing section length value is greater than or equal to a preset threshold.

[0085] Optional, see Figure 7 The pipeline oil mixing detection device 100 also includes a generation unit 140 and a display unit 150; the generation unit 140 is used to generate a three-dimensional model of the target pipeline according to the second parameter information, and the three-dimensional model is used to display the oil mixing information in each of the multiple sub-pipes of the target pipeline; the display unit 150 is used to display the three-dimensional model on a first interface.

[0086] Optionally, the generation unit 140 is also used to generate alarm information when the oil mixing detection result of the target pipeline is abnormal; the display unit 150 is also used to display the alarm information on the first interface, and the alarm information is used to prompt the operator to deal with the oil mixing in the target pipeline.

[0087] It should be understood that specific descriptions of the above-mentioned optional methods can be found in the foregoing method embodiments, and will not be repeated here. Furthermore, explanations and descriptions of the beneficial effects of any of the pipeline oil mixing detection devices 100 provided above can be found in the corresponding method embodiments, and will not be repeated here.

[0088] This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the various embodiments described above. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0089] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the aforementioned pipeline oil mixing detection device 100 and one or more ports. Optionally, the functions supported by this chip are as described above and will not be repeated here.

[0090] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0091] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0092] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0093] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for detecting oil mixing in pipelines used for transporting refined oil products, characterized in that, The method includes: Obtain the first parameter information corresponding to the target pipeline at the first moment. The target pipeline includes multiple sub-pipes. The first parameter information includes the upstream flow rate, downstream flow rate, upstream head value and downstream head value of each of the multiple sub-pipes. Based on the first parameter information, the second parameter information corresponding to the target pipeline at the first moment is obtained by the trained first neural network model. The second parameter information includes the position information of the oil mixing section and the length value of the oil mixing section. If the length of the mixed oil section is greater than or equal to a preset threshold, the oil mixing detection result of the target pipeline is determined to be an oil mixing anomaly. The step of obtaining the second parameter information corresponding to the target pipeline at the first time step based on the first parameter information through the trained first neural network model includes: The first parameter information is input into the first neural network model, and the first prediction information corresponding to the first parameter information is output. The first prediction information includes the predicted location of the oil mixing section of the target pipeline and the predicted length of the oil mixing section. The first prediction information is input into the trained second neural network model, and the second prediction information is output. The second prediction information includes the upstream flow prediction value, downstream flow prediction value, upstream pressure head prediction value and downstream pressure head prediction value of each of the multiple sub-pipes corresponding to the first prediction information. The second prediction information is input into the first neural network model, and the second parameter information corresponding to the target pipeline at the first time point is output.

2. The method according to claim 1, characterized in that, Before obtaining the second parameter information corresponding to the target pipeline at the first time step based on the first parameter information and through the trained first neural network model, the method further includes: Obtain a training sample set, which includes the third parameter information of each of the multiple pipelines. Each of the multiple pipelines includes multiple sub-pipes. The third parameter information of each pipeline includes the upstream flow rate, downstream flow rate, upstream head value and downstream head value of each sub-pipe at each time of multiple times, as well as the location information of the mixing section and the length value of the mixing section at each time of multiple times. The first neural network model and the second neural network model are trained based on the training sample set; If the first neural network model and the second neural network model meet the preset accuracy, the trained first neural network model and the second neural network model are obtained.

3. The method according to claim 2, characterized in that, The method further includes: A three-dimensional model of the target pipeline is generated based on the second parameter information. The three-dimensional model is used to display the oil mixing information in each of the multiple sub-pipes of the target pipeline. The three-dimensional model is displayed on the first interface.

4. The method according to claim 3, characterized in that, The step of generating a three-dimensional model of the target pipeline based on the second parameter information includes: Obtain the length information of each sub-pipeline included in the target pipeline and the oil information in the target pipeline; A three-dimensional model of the target pipeline is generated based on the length information of each sub-pipeline included in the target pipeline, the oil information in the target pipeline, and the second parameter information.

5. The method according to claim 4, characterized in that, The method further includes: If the oil mixing detection result of the target pipeline is abnormal, an alarm message is generated; The alarm information is displayed on the first interface, and the alarm information is used to prompt the operator to deal with the mixed oil in the target pipeline.

6. The method according to claim 4, characterized in that, The target pipeline includes multiple sub-pipes, each of which has a flow rate acquisition device and a head acquisition device installed upstream and downstream, respectively. Acquiring the first parameter information corresponding to the target pipeline at a first moment includes: The flow rate acquisition device acquires the upstream and downstream flow rates of each of the plurality of sub-pipes at the first moment. The head value acquisition device acquires the upstream head value and downstream head value of each of the plurality of sub-pipes at the first moment; The first parameter information corresponding to the target pipeline at the first moment is obtained based on the upstream flow rate, downstream flow rate, upstream pressure head, and downstream pressure head of each of the plurality of sub-pipes.

7. A pipeline mixing detection device for transporting refined oil products, characterized in that, The apparatus, used in any one of claims 1-6, comprises: The acquisition unit is used to acquire the first parameter information corresponding to the target pipeline at a first moment. The target pipeline includes multiple sub-pipelines. The first parameter information includes the upstream flow rate, downstream flow rate, upstream head value and downstream head value of each of the multiple sub-pipelines. The determining unit is used to obtain the second parameter information corresponding to the target pipeline at the first moment based on the first parameter information and through the trained first neural network model. The second parameter information includes the position information of the oil mixing section and the length value of the oil mixing section. The detection unit is used to determine that the oil mixing detection result of the target pipeline is an oil mixing anomaly when the length value of the oil mixing section is greater than or equal to a preset threshold.

8. The apparatus according to claim 7, characterized in that, The device further includes a generation unit and a display unit; The generation unit is used to generate a three-dimensional model of the target pipeline based on the second parameter information. The three-dimensional model is used to display the oil mixing information in each of the multiple sub-pipes of the target pipeline. The display unit is used to display the three-dimensional model on the first interface.

9. The apparatus according to claim 8, characterized in that, The generating unit is further configured to generate alarm information when the oil mixing detection result of the target pipeline is abnormal; the display unit is further configured to display the alarm information on the first interface, and the alarm information is used to prompt the operator to handle the oil mixing in the target pipeline.

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