Method, system and device for prompting violation of construction site around aviation oil pipeline

Abstract

The invention provides a violation prompting method, system and device for a construction site around an aviation oil pipeline. The violation prompting method for the construction site around the aviation oil pipeline comprises the following steps: receiving a real-time picture of the construction site around the aviation oil pipeline collected by a camera on a solar mobile sentry; the real-time picture is used for representing the construction condition; performing image recognition on the construction condition in the real-time picture to obtain an image recognition result; judging whether the image recognition result belongs to a violation scene or not; and if the image recognition result belongs to the violation scene, sending out a violation prompt. According to the application, the construction site around the aviation oil pipeline can be monitored and managed, violation prompts can be sent out in time when violation occurs in construction, safety events can be found and handled in time, and the safety risk of the construction site is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of construction site management around aviation fuel pipelines, and in particular to a method, system, and device for warning against violations at construction sites around aviation fuel pipelines. Background Art

[0002] As a key infrastructure in the field of aviation transportation, the safety of aviation fuel pipelines is directly related to the normal operation of aircraft and public safety. With the acceleration of urbanization and the increase in various construction projects, the areas around aviation fuel pipelines are often faced with challenges from construction activities. These construction activities, such as excavation, piling, and heavy vehicle traffic, can easily cause physical damage or safety threats to aviation fuel pipelines if they are not strictly planned and monitored, leading to serious consequences such as fuel leakage, fire, and even explosion, which not only affects the normal order of aviation transportation, but may also pose a major threat to the safety of life and property of surrounding residents.

[0003] Traditionally, the management of construction sites around aviation fuel pipelines mainly relies on manual inspections, which has the disadvantages of low efficiency, limited coverage, and slow response. Especially in complex and changing construction environments, it is difficult for manual inspections to achieve all-weather and all-around monitoring, and it is easy to miss potential safety hazards. In addition, manual inspections also rely on the professional quality and sense of responsibility of the inspectors, which are subjective and difficult to ensure the consistency and effectiveness of management.

[0004] In recent years, with the rapid development of video surveillance technology and artificial intelligence algorithms, intelligent monitoring systems have been widely used in various industries, demonstrating efficient, accurate, and real-time monitoring capabilities. However, in the field of monitoring construction sites around aviation fuel pipelines, although attempts have been made to introduce video surveillance technology, most of them are still at the stage of simple image recording and playback, lacking intelligent image recognition and analysis functions, and unable to automatically identify and warn of illegal construction behaviors, resulting in delayed detection of safety incidents and inefficient handling. The safety of construction sites around aviation fuel pipelines needs to be improved. Summary of the invention

[0005] The present application provides a violation warning method, system, and device for construction sites around aviation fuel pipelines that can improve safety.

[0006] This application provides a method for warning of violations at construction sites around aviation fuel pipelines, including:

[0007] Receiving real-time images of the construction site around the aviation fuel pipeline collected by the camera on the solar mobile sentinel; the real-time images are used to represent the construction situation;

[0008] Performing image recognition on the construction situation in the real-time picture to obtain an image recognition result;

[0009] Determining whether the image recognition result belongs to a violation scenario;

[0010] If the image recognition result belongs to a violation scenario, a violation prompt is issued.

[0011] Optionally, performing image recognition on the construction situation in the real-time picture to obtain an image recognition result includes:

[0012] The real-time image is input into a recognition model to obtain an image recognition result; the recognition model is generated by the following steps:

[0013] The data belonging to the violation scenario and the data belonging to the non-violation scenario are collected to obtain a data set; and the model is trained using the data set to obtain the recognition model.

[0014] Optionally, the using the data set to train a model to obtain the recognition model includes:

[0015] Adding the data of the real-time picture to the data set to obtain an updated data set;

[0016] The model is trained using the updated data set to obtain an updated recognition model.

[0017] Optionally, the violation prompt method further includes:

[0018] If the image recognition result belongs to a violation scenario, compare the facial information and construction personnel information in the image recognition result; the construction personnel information is the facial information of the construction personnel at the construction site collected in advance;

[0019] If the facial information in the image recognition result belongs to the construction worker information, a violation prompt is issued to the construction worker.

[0020] Optionally, the construction site includes construction equipment, and the violation prompt method further includes:

[0021] If the facial information in the image recognition result belongs to the construction worker information, it is determined whether to shut down the construction equipment according to the distance between the construction worker and the construction equipment.

[0022] Optionally, if the image recognition result belongs to a violation scenario, issuing a violation prompt includes: comparing the image recognition result with a preset violation category to determine a violation level; and issuing a violation prompt corresponding to the violation level according to a corresponding relationship between the violation level and the violation prompt;

[0023] Optionally, if the image recognition result belongs to a violation scenario, issuing a violation prompt includes:

[0024] The construction site includes construction equipment; the violation scenario includes the movement of the construction equipment that creates potential danger to the aviation fuel pipeline; if the image recognition result belongs to a violation scenario, issuing a violation prompt, including: when the camera monitors that the construction equipment is moving toward the aviation fuel pipeline, tracking the construction equipment; when the camera monitors that the distance between the construction equipment and the aviation fuel pipeline is reduced and tends to the minimum dangerous distance, issuing a violation prompt to the construction equipment to stay away from the aviation fuel pipeline.

[0025] The present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for prompting violations at a construction site around an aviation fuel pipeline described in any one of the above items is implemented.

[0026] The present application provides a violation prompting system for a construction site around an aviation fuel pipeline, comprising one or more processors for implementing a violation prompting method for a construction site around an aviation fuel pipeline as described in any one of the above items.

[0027] The present application provides a violation reminder device for a construction site around an aviation fuel pipeline, comprising:

[0028] An image acquisition terminal is arranged at the construction site;

[0029] An outdoor network cabinet electrically connected to the image acquisition terminal; and

[0030] The violation reminder system at the construction site around the aviation fuel pipeline as described above is electrically connected to the outdoor network cabinet.

[0031] Optionally, the outdoor network cabinet includes a cabinet, a network cable, a network receiving terminal arranged outside the cabinet, and a router, an algorithm box, a video recorder, a switch and a battery respectively arranged inside the cabinet;

[0032] The network receiving end and the switch, the router and the switch, the algorithm box and the switch, and the video recorder and the switch are electrically connected via the network cable respectively; the algorithm box is electrically connected to the violation reminder system at the construction site around the aviation fuel pipeline.

[0033] In some embodiments, by receiving real-time images of the construction situation at the construction site around the aviation fuel pipeline collected by the camera on the solar-powered mobile sentinel, image recognition is performed on the construction situation in the real-time image, and it is determined whether the image recognition result belongs to a violation scene. When the image recognition result belongs to a violation scene, a violation prompt is issued. The construction site around the aviation fuel pipeline can be monitored and managed in real time, and a violation prompt is issued in time when violations occur in the construction. Security incidents can be discovered and handled in time, reducing safety risks at the construction site.

[0034] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0036] Figure 1 Shown is a schematic structural diagram of an embodiment of an outdoor network cabinet of the present application.

[0037] Figure 2 Shown is a flow chart of an embodiment of a method for indicating violations at construction sites around aviation fuel pipelines of the present application.

[0038] Figure 3 Shown is a structural block diagram of an embodiment of a violation reminder system for a construction site around an aviation fuel pipeline of the present application. DETAILED DESCRIPTION

[0039] The present application provides a method, system, and device for notifying construction sites of violations around aviation fuel pipelines. The method, system, and device for notifying construction sites of violations around aviation fuel pipelines of the present application are described in detail below in conjunction with the accompanying drawings. The features of the following embodiments and implementations may be combined with each other without conflict.

[0040] The present application provides a violation reminder device for construction sites around aviation fuel pipelines, comprising: an image acquisition terminal, an outdoor network cabinet, and a violation reminder system for construction sites around aviation fuel pipelines provided by the present application.

[0041] The image acquisition terminal is set up at the construction site. The image acquisition terminal includes the camera on the solar mobile sentinel. The solar mobile sentinel is equipped with rollers and solar panels, solar controllers and batteries, and uses solar power to power the camera. The solar mobile sentinel can be flexibly deployed at various key locations around the aviation fuel pipeline to achieve all-round monitoring coverage.

[0042] The outdoor network cabinet is electrically connected to the image acquisition terminal and is used to receive data collected by the image acquisition terminal and transmit it.

[0043] The violation reminder system at the construction site around the aviation fuel pipeline is electrically connected to the outdoor network cabinet to receive data transmitted by the outdoor network cabinet.

[0044] Figure 1 FIG. 1 is a schematic diagram of the structure of an embodiment of an outdoor network cabinet 100 of the present application. Figure 1As shown, the outdoor network cabinet 100 includes a cabinet 110, a network cable, a network receiving terminal 170 disposed outside the cabinet 110, and a router 120, an algorithm box 130, a video recorder 140, a switch 150 and a battery 160 respectively disposed in the cabinet 110.

[0045] The network receiving end 170 is electrically connected to the switch 150, the router 120 is electrically connected to the switch 150, the algorithm box 130 is electrically connected to the switch 150, and the video recorder 140 is electrically connected to the switch 150. The algorithm box 130 is electrically connected to the violation reminder system at the construction site around the aviation fuel pipeline.

[0046] The cabinet 110 includes a cavity 111. The router 120, the algorithm box 130, the video recorder 140, the switch 150 and the battery 160 are arranged in the cavity 111. Optionally, the network receiving end 170 can be a wireless network receiving end.

[0047] The cabinet 110 is used to protect the router 120, algorithm box 130, video recorder 140, switch 150 and battery 160 arranged inside the cabinet 110. The network receiving terminal 170 is arranged outside the cabinet 110, which is conducive to the collection of monitoring data by wireless transmission. The switch 150 collects the data received by the network receiving terminal 170 through the network cable and summarizes the data into the video recorder 140. The algorithm box 130 is used to analyze the data, and the algorithm box 130 can send the statistical data to the external subsequent equipment through the router 120. In the present application, the algorithm box 130 can send the data collected by the image acquisition terminal to the violation prompt system at the construction site around the aviation fuel pipeline.

[0048] Figure 2 Shown is a flow chart of an embodiment of a method 200 for notifying a violation of a construction site around an aviation fuel pipeline of the present application.

[0049] The violation reminder method 200 for the construction site around the aviation fuel pipeline includes: steps 210 to 240.

[0050] Step 210: receiving a real-time picture of the construction site around the aviation fuel pipeline collected by a camera on a solar-powered mobile sentinel. The real-time picture is used to represent the construction situation.

[0051] The Solar Mobile Sentinel is a monitoring device that integrates a solar power supply system and a high-definition camera. It can operate autonomously and does not rely on external power sources. It is particularly suitable for monitoring scenarios such as around aviation fuel pipelines where there may be a lack of stable power supply.

[0052] The camera is responsible for capturing real-time images of the construction site, and the real-time images are transmitted to the outdoor network cabinet 100 via a wireless network (such as 4G / 5G, Wi-Fi, etc.) or a wired network.

[0053] The real-time images clearly show the actual situation at the construction site, including the activities of construction workers, the operation of construction equipment, the conditions of the on-site environment, etc. The real-time images provide basic data for subsequent image recognition and are an important basis for judging whether the construction situation is in violation of regulations.

[0054] Step 220, performing image recognition on the construction situation in the real-time picture to obtain an image recognition result.

[0055] The real-time images are processed and analyzed through image recognition algorithms to identify key elements in the images, such as construction personnel, construction equipment, obstacles, etc.

[0056] Step 230, determining whether the image recognition result belongs to a violation scenario.

[0057] Based on the image recognition results and combined with the preset violation judgment criteria, a comprehensive assessment of the construction situation is conducted.

[0058] If the construction situation matches one or more items in the violation scenario template or rule base, the construction situation is determined to be a violation.

[0059] Step 240: If the image recognition result belongs to a violation scenario, a violation prompt is issued.

[0060] Once the construction situation is determined to be in violation of regulations, a violation reminder will be issued immediately. Violation reminders can be in the form of sound alarms, flashing lights, text message notifications, email reminders, etc. Violation reminders can attract the attention of relevant personnel in a timely manner, prompting them to take immediate measures to correct violations and prevent potential safety accidents.

[0061] In some embodiments, by receiving real-time images of the construction situation at the construction site around the aviation fuel pipeline collected by the camera on the solar-powered mobile sentinel, image recognition is performed on the construction situation in the real-time image, and it is determined whether the image recognition result belongs to a violation scene. When the image recognition result belongs to a violation scene, a violation prompt is issued. The construction site around the aviation fuel pipeline can be monitored and managed in real time, and a violation prompt is issued in time when violations occur in the construction. Security incidents can be discovered and handled in time, reducing safety risks at the construction site.

[0062] In some embodiments, step 220 includes:

[0063] The real-time image is input into the recognition model to obtain the image recognition result; the recognition model is generated through the following steps:

[0064] Data belonging to violation scenarios and data belonging to non-violation scenarios are collected to obtain a data set; a model is trained using the data set to obtain a recognition model.

[0065] Violation scenarios include: not wearing a safety helmet, wandering around, construction workers falling to the ground, making phone calls in oil and gas explosion-proof areas, smoking in dangerous areas, sleeping on duty, identifying backfill warning tapes, and intentionally blocking monitoring equipment.

[0066] Collect a large amount of data on illegal and non-illegal scenarios through various channels (such as surveillance videos, social media, public databases, etc.). This data can be in image or video format for subsequent processing and analysis.

[0067] The collected data needs to be labeled, that is, which are violation scenarios and which are non-violation scenarios. The labeled data is used as a training set to train the recognition model.

[0068] For image recognition tasks, convolutional neural network (CNN) and residual network (ResNet) can be selected as training models.

[0069] Use the labeled dataset to train the model. During the training process, the model will learn how to extract features from the input image and determine whether the image belongs to a violation scene based on these features.

[0070] When the model training is completed and real-time image input is received, the recognition model will quickly analyze the image content and output the image recognition result, that is, determine whether the image belongs to a violation scene.

[0071] In some embodiments, the use of a data set to train a model to obtain a recognition model includes: adding data of a real-time image to a data set to obtain an updated data set; and using the updated data set to train a model to obtain an updated recognition model.

[0072] As the system runs, new real-time image data will be continuously generated. These real-time data contain new scenes, new violations or non-violations, and are resources for continuous learning and optimization of the model.

[0073] The annotated and verified real-time data is added to the original data set to form an updated data set. The process of updating the data set can be periodic or real-time.

[0074] New data is fed into the model training framework to allow the recognition model to learn new features and patterns.

[0075] By continually incorporating new real-time data and retraining the model, the recognition model can continuously learn new features and patterns, thereby improving its recognition ability and accuracy.

[0076] In some embodiments, the violation prompt method 200 also includes: if the image recognition result belongs to a violation scenario, comparing the facial information and construction worker information in the image recognition result; the construction worker information is the facial information of the construction workers at the construction site collected in advance; if the facial information in the image recognition result belongs to the construction worker information, a violation prompt is issued to the construction worker.

[0077] If the recognition result confirms that the screen content belongs to an illegal scene, the facial information is extracted from the recognition result. This is usually achieved through a face recognition algorithm, which can accurately identify and extract facial features from a complex background.

[0078] The construction personnel information database stores the facial information of all construction personnel at the construction site. The facial information of construction personnel is collected and entered by facial recognition equipment before the construction personnel enter the construction site.

[0079] Using the face matching algorithm, the extracted facial information in the violation scene is compared with the information in the construction personnel information database. The matching algorithm calculates the similarity between facial features and sets a threshold to determine whether there is a match. If the similarity exceeds the threshold, the facial information is considered to be matched successfully.

[0080] If the facial information in the violation scene successfully matches a record in the construction worker information database, the construction worker is determined to be the executor of the violation.

[0081] At this time, a violation reminder is issued to the construction worker. The reminder can be in the form of on-site voice broadcast, SMS notification, email notification, etc. If the construction worker is riding in a construction vehicle, a violation reminder can also be sent to the construction vehicle. For example, a voice or image reminder is sent to the central control screen of the construction vehicle.

[0082] In addition to issuing violation reminders, violation information can also be recorded, such as violation time, violation location, violation type, etc., for subsequent statistical analysis or as a basis for punishment.

[0083] Through the automated recognition and comparison process, the perpetrators of violations can be identified quickly and accurately, and violation prompts can be issued, greatly improving the management efficiency of the construction site.

[0084] At the same time, recording and counting violation information can provide data support for construction site managers, helping them better understand the safety conditions of the construction site and optimize management processes.

[0085] In some embodiments, the construction site includes construction equipment, and the violation prompt method 200 further includes: if the facial information in the image recognition result belongs to the construction personnel information, determining whether to shut down the construction equipment according to the distance between the construction personnel and the construction equipment.

[0086] The distance between construction workers and construction equipment is detected through image recognition technology or additional sensors (such as radar, infrared sensors, etc.).

[0087] Distance detection can be based on pixel distance in an image, physical distance in three-dimensional space, or a comprehensive analysis combining multiple data sources.

[0088] A safety distance threshold is preset, and the safety distance threshold is determined according to the type of construction equipment, the actual situation of the construction site and safety regulations.

[0089] The setting of the safety distance threshold is intended to ensure that construction workers maintain a sufficient safety distance from construction equipment to prevent accidents.

[0090] The detected distance between the construction personnel and the construction equipment is compared with the safety distance threshold. If the detected distance is less than the safety distance threshold, that is, the distance between the construction personnel and the construction equipment is too close, there is a safety hazard, and the mechanism of automatically shutting down the construction equipment is triggered.

[0091] The operation of shutting down the construction equipment can be achieved by sending a control signal to the control system of the construction equipment, ensuring that the construction equipment can stop running quickly and safely.

[0092] While shutting down construction equipment, safety reminders can also be issued to construction workers, reminding them to maintain a safe distance.

[0093] In addition, relevant information of the shutdown incident, such as time, location, construction personnel and equipment involved, will also be recorded for subsequent safety analysis and improvements.

[0094] By monitoring the distance between construction workers and construction equipment in real time and automatically shutting down construction equipment when necessary, the system can significantly reduce safety risks at construction sites.

[0095] In some embodiments, step 240 includes: comparing the image recognition result with a preset violation category to determine the violation level; and issuing a violation prompt corresponding to the violation level according to the corresponding relationship between the violation level and the violation prompt.

[0096] Violation scenarios such as not wearing a helmet, wandering, construction workers falling to the ground, making phone calls in oil and gas explosion-proof places, smoking in dangerous areas, face recognition, sleeping on the job, backfill warning tape recognition, and deliberately blocking the alarm of monitoring equipment can be divided into different violation categories. According to the severity and potential consequences of the violation category, the violation is divided into different levels, such as minor violations, general violations, and serious violations. The correspondence between violation levels and violation prompts is established in advance. For example, different violation levels correspond to different prompt methods (such as sound, light, text information, etc.), prompt content (such as description of the violation, consequence reminder, etc.), and prompt frequency.

[0097] Compare the image recognition result with the preset violation category to determine whether the recognition result belongs to a certain violation category.

[0098] If the recognition result meets the description of a certain violation category, the recognition result is classified into the violation category. If the recognition result is classified into a certain violation category, the specific violation level is determined according to the violation level classification of the category. According to the determined correspondence between the violation level and the violation prompt, the violation prompt corresponding to the violation level is issued to the relevant personnel through appropriate prompting methods. In this way, the relevant personnel can be reminded of the violation in a timely manner and take necessary corrective measures to avoid potential safety risks or consequences.

[0099] In some embodiments, the violation scenario includes the movement of construction equipment that creates a potential danger to the aviation fuel pipeline; step 240 includes: when the camera monitors that the construction equipment is moving toward the aviation fuel pipeline, tracking the construction equipment; when the camera monitors that the distance between the construction equipment and the aviation fuel pipeline is reduced and tends to the minimum dangerous distance, issuing a violation prompt to the construction equipment to stay away from the aviation fuel pipeline.

[0100] Use image recognition or object tracking technology to identify and track construction equipment in images captured by the camera. Tracking technology can identify construction equipment based on characteristics such as color, shape, size, and track its position and movement trajectory in real time. Use image analysis or additional sensors (such as radar, laser rangefinder, etc.) to detect the distance between construction equipment and aviation fuel pipelines. The minimum danger distance is determined based on the safety regulations of the aviation fuel pipeline, the size and type of construction equipment, and the potential risk assessment.

[0101] The minimum danger distance is designed to ensure that the construction equipment maintains a sufficient safety distance from the aviation fuel pipeline to prevent safety accidents caused by improper operation or accidents of the construction equipment. When it is detected that the distance between the construction equipment and the aviation fuel pipeline is reduced and tends to the minimum danger distance, the violation prompt mechanism will be triggered to issue a violation prompt to the construction equipment to stay away from the aviation fuel pipeline. By monitoring the dynamics of the construction equipment in real time and issuing a violation prompt when it approaches the aviation fuel pipeline to a dangerous distance, the risk of safety accidents can be significantly reduced.

[0102] Figure 3 Shown is a structural block diagram of an embodiment of a violation reminder system for a construction site around an aviation fuel pipeline of the present application.

[0103] like Figure 3 As shown, the violation prompting system for the construction site around the aviation fuel pipeline includes one or more processors 31 for implementing the violation prompting method 200 for the construction site around the aviation fuel pipeline as described above.

[0104] In some embodiments, the violation reminder system for the construction site around the aviation fuel pipeline may include a computer-readable storage medium 32, which may store a program that can be called by the processor 31, and may include a non-volatile storage medium. In some embodiments, the violation reminder system for the construction site around the aviation fuel pipeline may include a memory 33 and an interface 34. In some embodiments, the violation reminder system for the construction site around the aviation fuel pipeline may also include other hardware according to actual applications.

[0105] The computer-readable storage medium 32 of the embodiment of the present application stores a program thereon, and when the program is executed by the processor 31, it is used to implement the violation prompt method 200 for the construction site around the aviation fuel pipeline as described above.

[0106] The present application may take the form of a computer program product implemented on one or more computer-readable storage media 32 (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. The computer-readable storage medium 32 includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be a computer-readable instruction, a data structure, a module of a program, or other data. Examples of computer-readable storage media 32 include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassette, tape disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

Claims

1. A method for notifying violations at construction sites around aviation fuel pipelines, characterized in that: include: Receive real-time images of the construction site around the aviation fuel pipeline collected by the camera on the solar-powered mobile sentinel; The real-time picture is used to show the construction situation; Performing image recognition on the construction situation in the real-time picture to obtain an image recognition result; Determining whether the image recognition result belongs to a violation scenario; If the image recognition result belongs to a violation scenario, a violation prompt is issued.

2. The method for notifying violations at construction sites around aviation fuel pipelines according to claim 1 is characterized in that: The performing image recognition on the construction situation in the real-time picture to obtain an image recognition result includes: The real-time image is input into a recognition model to obtain an image recognition result; the recognition model is generated by the following steps: The data belonging to the violation scenario and the data belonging to the non-violation scenario are collected to obtain a data set; and the model is trained using the data set to obtain the recognition model.

3. The method for notifying violations at construction sites around aviation fuel pipelines according to claim 2 is characterized in that: The step of using the data set to train a model to obtain the recognition model includes: Adding the data of the real-time picture to the data set to obtain an updated data set; The model is trained using the updated data set to obtain an updated recognition model.

4. The method for notifying violations at construction sites around aviation fuel pipelines according to claim 1 is characterized in that: The violation prompt method also includes: If the image recognition result belongs to a violation scenario, compare the facial information and construction personnel information in the image recognition result; the construction personnel information is the facial information of the construction personnel at the construction site collected in advance; If the facial information in the image recognition result belongs to the construction worker information, a violation prompt is issued to the construction worker.

5. The method for notifying violations at a construction site around an aviation fuel pipeline according to claim 4, wherein the construction site includes construction equipment, and is characterized in that: The violation prompt method also includes: If the facial information in the image recognition result belongs to the construction worker information, it is determined whether to shut down the construction equipment according to the distance between the construction worker and the construction equipment.

6. The method for notifying violations at construction sites around aviation fuel pipelines according to claim 1, characterized in that: If the image recognition result belongs to a violation scenario, issuing a violation prompt includes: comparing the image recognition result with a preset violation category to determine the violation level; and issuing a violation prompt corresponding to the violation level according to the corresponding relationship between the violation level and the violation prompt; and / or, The construction site includes construction equipment; the violation scenario includes the movement of the construction equipment that creates potential danger to the aviation fuel pipeline; if the image recognition result belongs to a violation scenario, issuing a violation prompt, including: when the camera monitors that the construction equipment is moving toward the aviation fuel pipeline, tracking the construction equipment; when the camera monitors that the distance between the construction equipment and the aviation fuel pipeline is reduced and tends to the minimum dangerous distance, issuing a violation prompt to the construction equipment to stay away from the aviation fuel pipeline.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the violation prompt method for a construction site around an aviation fuel pipeline according to any one of claims 1 to 6 is implemented.

8. A violation reminder system for construction sites around aviation fuel pipelines, characterized in that: The method comprises one or more processors for implementing the violation prompting method at a construction site around an aviation fuel pipeline as claimed in any one of claims 1 to 6.

9. A violation warning device for a construction site around an aviation fuel pipeline, characterized in that: include: An image acquisition terminal is arranged at the construction site; An outdoor network cabinet, electrically connected to the image acquisition terminal; and The violation reminder system for the construction site around the aviation fuel pipeline as claimed in claim 8 is electrically connected to the outdoor network cabinet.

10. The violation warning device for construction sites around aviation fuel pipelines according to claim 9, characterized in that: The outdoor network cabinet includes a cabinet, a network cable, a network receiving terminal arranged outside the cabinet, and a router, an algorithm box, a video recorder, a switch and a battery respectively arranged inside the cabinet; The network receiving end and the switch, the router and the switch, the algorithm box and the switch, and the video recorder and the switch are electrically connected via the network cable respectively; The algorithm box is electrically connected to a violation prompting system at a construction site around the aviation fuel pipeline.

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