Oil and gas surface engineering safety risk point monitoring method

Abstract

The invention provides an oil and gas surface engineering safety risk point monitoring method. The method comprises the following steps: acquiring video data of a construction site in real time; splitting the video data to obtain a time image sequence; extracting corresponding target images from the time image sequence in sequence; inputting the target image into a pre-constructed risk identification model, and outputting a risk source identification result; analyzing the target image, and judging whether a target person corresponding to a risk source exists in the target image or not; based on the risk source identification result, first early warning information is generated and sent; cutting a target person in the target image to obtain a target person image; inputting the target figure graph into a pre-constructed face recognition model, and outputting a face recognition result; generating second early warning information based on the risk source recognition result and a face recognition result corresponding to the risk source recognition result, and sending the second early warning information; the related personnel related to the risk source can be determined.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas surface engineering safety risk point monitoring, and in particular to a method for oil and gas surface engineering safety risk point monitoring. Background Art

[0002] In recent years, the construction of oil and gas surface projects has progressed rapidly. However, due to the large area, multiple cross-operations, and large number of personnel at the construction site, there are certain management difficulties and safety accidents are prone to occur. If it relies on manual supervision, the efficiency is low and loopholes are prone to occur. Therefore, the existing camera equipment at the construction site is used with the support of computer vision to monitor the safety risk points at the construction site. Compared with traditional supervision methods, it can effectively discover problems, reduce the workload of supervisors, and improve supervision efficiency. However, the existing safety risk point monitoring method cannot effectively lock in the relevant personnel who cause the risk source. Summary of the invention

[0003] In response to the above-mentioned problems, the present application proposes a method for monitoring safety risk points of oil and gas surface engineering, which solves the technical problem that the existing safety risk point monitoring methods cannot effectively lock in the relevant personnel causing the risk source. It can monitor the risk sources in the oil and gas surface engineering construction process and identify the relevant personnel related to the risk source.

[0004] The present application provides a method for monitoring safety risk points of oil and gas surface engineering, including:

[0005] Obtain video data from the construction site in real time;

[0006] Splitting the video data to obtain a time image sequence;

[0007] extracting corresponding target images from the time image sequence in sequence;

[0008] Inputting the target image into a pre-built risk identification model and outputting a risk source identification result;

[0009] If there is a risk source in the risk source identification result, analyzing the target image to determine whether there is a target person corresponding to the risk source in the target image;

[0010] If there is no target person corresponding to the risk source in the target image, generating and sending first warning information based on the risk source identification result;

[0011] If a target person corresponding to the risk source exists in the target image, the target person in the target image is cropped to obtain an image of the target person;

[0012] Input the target person image into a pre-built face recognition model and output the face recognition result;

[0013] A second warning message is generated and sent based on the risk source identification result and the face recognition result corresponding to the risk source identification result.

[0014] In some embodiments, including:

[0015] Preprocessing the target image, wherein the preprocessing includes:

[0016] Remove noise from the image through Gaussian filtering algorithm;

[0017] The contrast of the image is enhanced through histogram equalization and contrast stretching methods.

[0018] In some embodiments, including:

[0019] According to preset time intervals, corresponding target images are extracted from the time image sequence in sequence.

[0020] In some embodiments, including:

[0021] In the case that there is no target person corresponding to the risk source in the target image, acquiring sensitive images between the current target image and the previous target image in the time image sequence from back to front in time;

[0022] Analyze the sensitive image to determine whether there is a target person corresponding to the risk source in the sensitive image;

[0023] When it is determined that a target person corresponding to a risk source exists in a sensitive image, the target person in the sensitive image is cropped to obtain an image of the target person.

[0024] In some embodiments, when there is a risk source in the risk source identification result, analyzing the target image to determine the target person corresponding to the risk source includes:

[0025] Generate a risk source detection frame according to the risk source in the target image;

[0026] Generating a person detection frame based on a person in a target image, wherein the target image includes at least one person;

[0027] Acquire the first center coordinates of the risk source detection frame and the second center coordinates of the person detection frame;

[0028] Based on the first center coordinates and the second center coordinates, a distance between the risk source detection frame and the person detection frame is calculated;

[0029] The person corresponding to the person detection frame with the smallest distance in the target image is determined as the target person corresponding to the risk source.

[0030] In some embodiments, the method further comprises:

[0031] When there are multiple minimum distance person detection frames in the same sensitive image, the persons corresponding to all the person detection frames are determined as target persons corresponding to the risk source.

[0032] In some embodiments, the method further comprises:

[0033] When there are minimum distance person detection frames in different sensitive images, the person corresponding to the minimum distance person detection frame in the sensitive image that is located in the front in time sequence is determined as the target person corresponding to the risk source.

[0034] In some embodiments, the method further comprises:

[0035] In the case where there is a risk source in the target image, recording the start time corresponding to the risk source;

[0036] When the corresponding risk source in the target image disappears, the end time corresponding to the risk source is recorded;

[0037] Calculate the existence time of the risk source through the start time and the end time;

[0038] When the existence time of the risk source exceeds a preset threshold, third warning information is generated and sent.

[0039] An embodiment of the present application provides an electronic device, including:

[0040] A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the methods described above is executed.

[0041] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the methods described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By acquiring video data of the construction site in real time; splitting the video data to obtain a time image sequence; extracting corresponding target images from the time image sequence in sequence; inputting the target image into a pre-built risk identification model and outputting a risk source identification result; when a risk source exists in the risk source identification result, analyzing the target image to determine whether there is a target person corresponding to the risk source in the target image; when the target person corresponding to the risk source does not exist in the target image, generating and sending a first warning message based on the risk source identification result; if a target person corresponding to the risk source exists in the target image, cropping the target person in the target image to obtain a target person image; inputting the target person image into a pre-built face recognition model and outputting a face recognition result; generating and sending a second warning message based on the risk source identification result and a face recognition result corresponding to the risk source identification result; the risk sources in the oil and gas surface engineering construction process can be monitored, and the relevant personnel related to the risk sources can be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0045] Figure 1 A schematic diagram of the implementation process of a method for monitoring safety risk points in oil and gas surface engineering provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0048] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0049] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0051] Based on the problems existing in the related art, the embodiment of the present application provides a method for monitoring safety risk points of oil and gas surface engineering, and the execution subject of the monitoring method can be an electronic device. The electronic device can be various types of terminals such as a laptop, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content De l ivery Network, CDN), and big data and artificial intelligence platforms.

[0052] In some embodiments, the functions implemented by the monitoring method provided in the embodiments of the present application can be implemented by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0053] The present application provides a method for monitoring safety risk points in oil and gas surface engineering. Figure 1 A schematic diagram of the implementation process of a method for monitoring safety risk points in oil and gas surface engineering provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0054] Obtain video data from the construction site in real time;

[0055] In the embodiment of the present application, cameras can be installed at the construction site to obtain video data of the construction site in real time, and the number of cameras can be configured according to actual needs.

[0056] Splitting the video data to obtain a time image sequence;

[0057] In the embodiment of the present application, the video data can be split according to the video frames to obtain a temporal image sequence including a plurality of images.

[0058] extracting corresponding target images from the time image sequence in sequence;

[0059] In the embodiment of the present application, the images in the time image sequence are sorted based on time, so the corresponding target images can be extracted from the time image sequence in sequence according to the order of time.

[0060] Inputting the target image into a pre-built risk identification model and outputting a risk source identification result;

[0061] In the embodiment of the present application, the risk identification model is obtained based on neural network training. For example, the risk identification model can be obtained by training the YOLOv5 model. The risk source can be scattered glass, scattered rivets, improperly placed equipment or improperly placed materials, etc.

[0062] If there is a risk source in the risk source identification result, analyzing the target image to determine whether there is a target person corresponding to the risk source in the target image;

[0063] In some embodiments, when there is a risk source in the risk source identification result, analyzing the target image to determine whether there is a target person corresponding to the risk source in the target image includes:

[0064] Generate a risk source detection frame according to the risk source in the target image;

[0065] Generating a person detection frame based on a person in a target image, wherein the target image includes at least one person;

[0066] Acquire the first center coordinates of the risk source detection frame and the second center coordinates of the person detection frame;

[0067] Based on the first center coordinates and the second center coordinates, a distance between the risk source detection frame and the person detection frame is calculated;

[0068] The person corresponding to the person detection frame with the smallest distance in the target image is determined as the target person corresponding to the risk source.

[0069] In the embodiment of the present application, by calculating the distance between the risk source detection frame and the person detection frame, the person corresponding to the person detection frame with the smallest distance is selected as the target person, and the target person can be determined according to the distance between the risk source and the person. It is understandable that the risk source may not be caused by a single construction worker. Therefore, in the case where there are multiple minimum distance person detection frames in the target image, the people corresponding to all the person detection frames are determined as the target people corresponding to the risk source to avoid missing the target person.

[0070] If there is no target person corresponding to the risk source in the target image, generating and sending first warning information based on the risk source identification result;

[0071] In an embodiment of the present application, the risk source may not be man-made. In this case, the target person corresponding to the risk source does not exist in the target image. Therefore, a first warning information can be generated based on the risk source identification result, and the first warning information can be sent to a terminal configured by relevant personnel to facilitate relevant personnel to promptly deal with and eliminate the risk source.

[0072] If a target person corresponding to the risk source exists in the target image, the target person in the target image is cropped to obtain an image of the target person;

[0073] In an embodiment of the present application, when there is a target person corresponding to a risk source in a target image, the person in the target image can be cropped, a corresponding detection frame can be generated according to the coordinates of the target person, and then the target person in the detection frame can be separated from the target image to obtain the target person image.

[0074] Input the target person image into a pre-built face recognition model and output the face recognition result;

[0075] In an embodiment of the present application, the face recognition model is obtained based on neural network training. Exemplarily, the face recognition model can be obtained by training using a YOLOv5 model.

[0076] A second warning message is generated and sent based on the risk source identification result and the face recognition result corresponding to the risk source identification result.

[0077] In the embodiment of the present application, a second warning information is generated based on the risk source identification result and the face recognition result corresponding to the risk source identification result. The second warning information may include the type of risk source and the construction personnel information causing the risk source, and the second warning information is sent to the terminal configured by the relevant personnel, so that the relevant personnel can eliminate the risk source in time, and the corresponding construction personnel information can be strengthened. In summary, the risk sources in the oil and gas surface engineering construction process can be monitored, and the relevant personnel related to the risk source can be determined.

[0078] In some embodiments, including:

[0079] Preprocessing the target image, wherein the preprocessing includes:

[0080] Remove noise from the image through Gaussian filtering algorithm;

[0081] The contrast of the image is enhanced through histogram equalization and contrast stretching methods.

[0082] In the embodiment of the present application, the noise in the image is removed by the Gaussian filtering algorithm, which can reduce the noise and interference in the image. The contrast of the image is enhanced by the histogram equalization and contrast stretching method, so that the details in the image are clearly discernible. It can facilitate the subsequent use of the target image.

[0083] In some embodiments, including:

[0084] According to preset time intervals, corresponding target images are extracted from the time image sequence in sequence.

[0085] In the embodiment of the present application, corresponding target images are extracted from the time image sequence in sequence according to preset time intervals, which can reduce the processing amount and improve processing efficiency.

[0086] In some embodiments, including:

[0087] In the case that there is no target person corresponding to the risk source in the target image, acquiring sensitive images between the current target image and the previous target image in the time image sequence from back to front in time;

[0088] Analyze the sensitive image to determine whether there is a target person corresponding to the risk source in the sensitive image;

[0089] When it is determined that a target person corresponding to a risk source exists in a sensitive image, the target person in the sensitive image is cropped to obtain an image of the target person.

[0090] In the embodiment of the present application, since the corresponding target image is extracted from the time image sequence at a preset time interval, when the target person corresponding to the risk source does not exist in the target image, the target person may not exist, or the target person may simply not exist in the target image. Therefore, the sensitive images between the current target image and the previous target image in the time image sequence can be acquired sequentially from back to front in time, and the sensitive images are analyzed to determine whether there is a target person corresponding to the risk source in the sensitive image. The premise for determining whether there is a target person corresponding to the risk source in the sensitive image is that there is a risk source in the sensitive image, and then by calculating the distance between the risk source detection frame and the person detection frame, the person corresponding to the person detection frame with the smallest distance is selected as the target person. Then, the target person in the sensitive image is cropped to obtain the target person image for subsequent use.

[0091] In some embodiments, the method further comprises:

[0092] When there are multiple minimum distance person detection frames in the same sensitive image, the persons corresponding to all the person detection frames are determined as target persons corresponding to the risk source.

[0093] In an embodiment of the present application, the risk source may not be caused by a single construction worker. Therefore, when there are multiple minimum distance person detection frames in the sensitive image, the persons corresponding to all the person detection frames are determined as the target persons corresponding to the risk source to avoid missing the target person.

[0094] In some embodiments, the method further comprises:

[0095] When there are minimum distance person detection frames in different sensitive images, the person corresponding to the minimum distance person detection frame in the sensitive image that is located in the front in time sequence is determined as the target person corresponding to the risk source.

[0096] In an embodiment of the present application, there may be minimum distance person detection frames in different sensitive images respectively. Therefore, the person corresponding to the minimum distance person detection frame in the sensitive image that is located in the front in time sequence is determined as the target person corresponding to the risk source to avoid misselection of the target person.

[0097] In some embodiments, the method further comprises:

[0098] In the case where there is a risk source in the target image, recording the start time corresponding to the risk source;

[0099] When the corresponding risk source in the target image disappears, the end time corresponding to the risk source is recorded;

[0100] Calculate the existence time of the risk source through the start time and the end time;

[0101] When the existence time of the risk source exceeds a preset threshold, third warning information is generated and sent.

[0102] In an embodiment of the present application, the existence time of the risk source can be calculated by the start time and end time corresponding to the risk source. The existence time of the risk source is the difference between the start time and the end time. When the existence time of the risk source exceeds a preset threshold, it indicates that the relevant personnel have not eliminated the risk source in time and there are certain safety hazards. A third warning information can be generated and sent to the terminal configured by the relevant personnel for reminder.

[0103] It should be noted that in the embodiment of the present application, if the above-mentioned monitoring method is implemented in the form of a software function module, and when it is sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) perform all or part of the method described in each embodiment of the present application. And the aforementioned storage medium includes: various media that can store program codes such as U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), disk or optical disk. In this way, the embodiment of the present application is not limited to any specific hardware and software combination.

[0104] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the monitoring method provided in the above embodiment are implemented.

[0105] An embodiment of the present application provides an electronic device; Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the monitoring method stored in the memory to implement the steps in the monitoring method provided in the above embodiment.

[0106] It should be noted here that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0107] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0108] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0110] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0111] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0112] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read On ly Memory), disks or optical disks, etc. Various media that can store program codes.

[0113] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0114] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for monitoring safety risk points of oil and gas surface engineering, characterized in that: include: Obtain video data from the construction site in real time; Splitting the video data to obtain a time image sequence; extracting corresponding target images from the time image sequence in sequence; Inputting the target image into a pre-built risk identification model and outputting a risk source identification result; If there is a risk source in the risk source identification result, analyzing the target image to determine whether there is a target person corresponding to the risk source in the target image; If there is no target person corresponding to the risk source in the target image, generating and sending first warning information based on the risk source identification result; If a target person corresponding to the risk source exists in the target image, the target person in the target image is cropped to obtain an image of the target person; Input the target person image into a pre-built face recognition model and output the face recognition result; A second warning message is generated and sent based on the risk source identification result and the face recognition result corresponding to the risk source identification result.

2. The method according to claim 1, characterized in that include: Preprocessing the target image, wherein the preprocessing includes: Remove noise from the image through Gaussian filtering algorithm; The contrast of the image is enhanced through histogram equalization and contrast stretching methods.

3. The method according to claim 1, characterized in that include: According to preset time intervals, corresponding target images are extracted from the time image sequence in sequence.

4. The method according to claim 3, characterized in that include: In the case that there is no target person corresponding to the risk source in the target image, acquiring sensitive images between the current target image and the previous target image in the time image sequence from back to front in time; Analyze the sensitive image to determine whether there is a target person corresponding to the risk source in the sensitive image; When it is determined that a target person corresponding to a risk source exists in a sensitive image, the target person in the sensitive image is cropped to obtain an image of the target person.

5. The method according to claim 1, characterized in that When there is a risk source in the risk source identification result, analyzing the target image to determine the target person corresponding to the risk source includes: Generate a risk source detection frame according to the risk source in the target image; Generating a person detection frame based on a person in a target image, wherein the target image includes at least one person; Acquire the first center coordinates of the risk source detection frame and the second center coordinates of the person detection frame; Based on the first center coordinates and the second center coordinates, a distance between the risk source detection frame and the person detection frame is calculated; The person corresponding to the person detection frame with the smallest distance in the target image is determined as the target person corresponding to the risk source.

6. The method according to claim 5, characterized in that The method further comprises: When there are multiple minimum distance person detection frames in the same sensitive image, the persons corresponding to all the person detection frames are determined as target persons corresponding to the risk source.

7. The method according to claim 5, characterized in that The method further comprises: When there are minimum distance person detection frames in different sensitive images, the person corresponding to the minimum distance person detection frame in the sensitive image that is located in the front in time sequence is determined as the target person corresponding to the risk source.

8. The method according to claim 1, characterized in that The method further comprises: In the case where there is a risk source in the target image, recording the start time corresponding to the risk source; When the corresponding risk source in the target image disappears, the end time corresponding to the risk source is recorded; Calculate the existence time of the risk source through the start time and the end time; When the existence time of the risk source exceeds a preset threshold, third warning information is generated and sent.

9. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 8 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method as claimed in any one of claims 1 to 8.

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