Artificial intelligence monitoring and analysis gas station operation management mode

Abstract

The invention discloses an artificial intelligence monitoring and analysis gas station operation management mode, which relates to the technical field of gas station artificial intelligence operation management systems, and comprises a data acquisition module, an intelligent video analysis system, an early warning and alarm module, a data storage and management module, a user interaction interface and an edge computing all-in-one machine, the data acquisition module is used for capturing a real-time video stream and transmitting the real-time video stream to the intelligent video analysis system, and the intelligent video analysis system is used for receiving the video stream of the data acquisition module and performing deep analysis and identification on video content by using an AI video analysis technology. The safety management level of the gas station is improved through artificial intelligence monitoring, real-time monitoring and early warning are carried out, real-time monitoring of personnel behaviors in the gas station can be achieved through the AI video analysis technology, safety accidents are effectively reduced, and the safety of the gas station is improved by formulating a standard operation process and deploying an AI intelligent algorithm in a gas unloading area. And the safety specification of the oil unloading process can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence management systems for gas stations, and specifically to an artificial intelligence monitoring and analysis management method for gas stations. Background Art

[0002] AI monitoring systems use machine learning and computer vision technology to analyze video streams and sensor data. They can identify and track objects in real time, detect abnormal behavior, and perform face and license plate recognition. AI monitoring systems usually integrate automatic alarms, data storage, and analysis functions to improve safety and operational efficiency. In gas station management systems, AI monitoring is usually combined with AI monitoring to achieve safety management of gas station sites.

[0003] The application of artificial intelligence monitoring in gas stations has brought many benefits, such as improving safety, reducing accidents, and optimizing operations management, but there are also some shortcomings. First, the cost of use is high. The deployment and maintenance of artificial intelligence monitoring artificial intelligence monitoring systems usually require high initial investment and ongoing maintenance costs. Second, the training data set may be biased, causing the AI ​​model to perform poorly when dealing with certain specific groups or scenarios. For example, facial recognition algorithms may have different recognition rates in people of different races, genders or ages. Third, privacy issues. The application of video AI may involve large-scale data collection and analysis, raising privacy concerns. Collecting and analyzing personal image data without consent may infringe on personal privacy rights. Fourth, technical limitations. Current AI models may encounter difficulties when dealing with complex scenes, low-light conditions, fast-moving objects or high-resolution videos, resulting in a decrease in recognition rate.

[0004] In summary, an artificial intelligence monitoring and analysis method for gas station operation and management is provided. Summary of the invention

[0005] The purpose of the present invention is to provide an artificial intelligence monitoring and analysis method for the operation and management of gas stations, which has the advantages of improving the safety management level of gas stations and real-time monitoring and early warning, and solves the problems of low safety efficiency and high deployment cost in the operation and management of gas stations.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an artificial intelligence monitoring and analysis of the operation and management of gas stations, including a data acquisition module, an intelligent video analysis system, an early warning and alarm module, a data storage and management module, a user interaction interface and an edge computing all-in-one machine, wherein the data acquisition module is used to capture real-time video streams and transmit them to the intelligent video analysis system, the intelligent video analysis system is used to receive the video streams from the data acquisition module, and use AI video analysis technology to perform in-depth analysis and recognition of the video content, the early warning and alarm module is used to trigger the early warning and alarm mechanism when the data acquisition module recognizes illegal behavior or abnormal conditions, the data storage and management module is used to store and manage all video streams and recognition results, and the user interaction interface is used to manage The management personnel view the monitoring screen, receive warning information and check the historical records. The edge computing all-in-one machine is used to reduce delays and improve data processing efficiency. The intelligent video analysis system includes a video processing module, a target detection module, a behavior recognition module, an anomaly detection module, a big data analysis module and an algorithm module. The video processing module uses an image processing algorithm to pre-process the video stream. The target detection module uses deep learning AI technology to perform real-time detection of targets in the video. The behavior recognition module analyzes the target's behavior pattern based on the target detection module. The anomaly detection module identifies abnormal situations by comparing historical data and preset rules. The big data analysis module uses an optimized algorithm model to deeply mine and analyze the collected video data and behavior data.

[0007] Preferably, the data acquisition module is a network camera, a spherical camera and a hemispherical camera, and the data acquisition module is installed in the refueling area, the oil unloading area, the convenience store and the oil tank area in the gas station.

[0008] Preferably, the early warning and alarm module includes a sound alarm, a light signal, a mobile phone APP push and a computer terminal display, and the sound alarm is an IP speaker.

[0009] Preferably, the data storage and management module is a file server, a core business server and a database server. The data storage and management module adopts encrypted transmission. The data storage and management module adopts an API interface to connect documents. The illegal capture events are uploaded to the data business server via HTTP / HTTPS.

[0010] Preferably, the edge all-in-one machine is connected to the switch via a network cable, and the camera monitoring line is configured by logging into the device system client. After intelligent analysis of the data collected by the camera, the data of the relevant captured events is transmitted to the user business server in HTTP / HTTPS mode.

[0011] Preferably, the targets in the target detection module are people, vehicles and objects, which are used to identify the category, location and size of the targets. The behavior recognition module identifies whether the personnel are wearing safety protection equipment, whether they are making phone calls or smoking violations, whether the vehicles are parked illegally, and whether the tanker trucks are unloading oil according to regulations.

[0012] Preferably, the intelligent video analysis module uses feature extraction and selection, deep learning model, and data fusion algorithm to perform image processing, and the data acquisition module supports human structured analysis, vehicle structured analysis, general behavior analysis, and supports single-line multi-capability configuration.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] The present invention improves the safety management level of gas stations through artificial intelligence monitoring, conducts real-time monitoring and early warning, and through AI video analysis technology, can realize real-time monitoring of the behavior of personnel in gas stations, effectively reducing the occurrence of safety accidents. By formulating standardized operating procedures and deploying AI intelligent algorithms in the oil unloading area, it can ensure the safety and standardization of the oil unloading process and avoid safety accidents caused by improper operation. By enhancing emergency response capabilities, AI video analysis technology can quickly locate the source of the accident, provide strong support for emergency response, and reduce accident losses. By improving the operating efficiency of gas stations and using automated management systems, AI video analysis technology can automatically identify vehicle entry and exit, oil loading and unloading scenes, reduce manual intervention, and improve the operating efficiency of gas stations. By using data analysis and optimization to analyze video data, the operating status of the gas station can be understood, providing data support for the optimized layout and operating strategy of the gas station, reducing labor costs, and reducing the need for manual monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the management process of the present invention;

[0016] Figure 2 This is a schematic diagram of the deployment of a single device of the present invention;

[0017] Figure 3 It is a schematic diagram of the deployment of multiple devices of the present invention;

[0018] Figure 4 It is a schematic diagram of the deployment mode flow of the present invention;

[0019] Figure 5 It is a standard operation flow chart of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1

[0022] like Figure 1-5 As shown, it is the first embodiment of the present invention, which provides an artificial intelligence monitoring and analysis of the operation and management method of the gas station, including a data acquisition module, an intelligent video analysis system, an early warning and alarm module, a data storage and management module, a user interaction interface and an edge computing all-in-one machine. The data acquisition module is used to capture real-time video streams and transmit them to the intelligent video analysis system. The intelligent video analysis system is used to receive the video stream of the data acquisition module and use AI video analysis technology to perform in-depth analysis and recognition of the video content. The early warning and alarm module is used to trigger the early warning and alarm mechanism when the data acquisition module recognizes illegal behavior or abnormal conditions. The data storage and management module is used to store and manage all video streams and recognition results. The user interaction interface It is used for management personnel to view monitoring images, receive warning information and review historical records. The edge computing all-in-one machine is used to reduce delays and improve data processing efficiency. The intelligent video analysis system includes a video processing module, a target detection module, a behavior recognition module, an anomaly detection module, a big data analysis module and an algorithm module. The video processing module uses an image processing algorithm to pre-process the video stream. The target detection module uses deep learning AI technology to perform real-time detection of targets in the video. The behavior recognition module analyzes the target's behavior pattern based on the target detection module. The anomaly detection module identifies anomalies by comparing historical data and preset rules. The big data analysis module uses an optimized algorithm model to deeply mine and analyze the collected video data and behavior data.

[0023] like Figure 1-5As shown, through the edge computing all-in-one machine, plus system firmware + driver, it integrates VPU, NPU, GPU, FUMPEG, and embeds Ubuntu-aarch64-20.04 system. The kernel includes video encoding and decoding, color channel conversion, frame extraction strategy, AI capability engine, streaming service, version upgrade service, and AI capability + service is based on video analysis, interface service, capability configuration, log service, status check, and uses AI capability to automatically inspect all cameras on the site. When an abnormal situation occurs, the stationmaster is notified through the watch to reduce the burden on the site. The reminder content includes: camera name, monitoring area, occurrence time, and problem type. At the same time, AI capability is used to judge the site inspection behavior, automatically generate inspection records, reduce subsequent manual table operations, and realize online viewing of equipment inspections. First, the video stream will be input, and the gas station The high-definition cameras in the gas station transmit real-time video streams to the intelligent video analysis system, which then performs intelligent analysis. The system uses AI video analysis technology to perform intelligent analysis on the video stream, identify various behaviors and abnormal situations, and then trigger an early warning. When violations or abnormal situations are identified, the system will trigger the early warning mechanism according to the preset rules. Information will be conveyed during this process, and the early warning information will be conveyed to the gas station managers through preset channels, such as mobile phone APP push and computer terminal display. In emergencies, there will also be corresponding emergency handling functions. Managers will take timely measures based on the early warning information to stop violations and deal with abnormal situations. The entire process will be recorded and reviewed after the technology is completed. The system records the occurrence of violations or abnormal situations and generates a chain of evidence file. Managers can review and provide feedback on these records to improve the safety management system of the gas station.

[0024] Example 2

[0025] Reference Figure 2-5 , which is the second embodiment of the present invention, and this embodiment is based on the previous embodiment.

[0026] In this embodiment, the data acquisition module is a network camera, a spherical camera and a hemispherical camera, and the data acquisition module is installed in the refueling area, the oil unloading area, the convenience store and the oil tank area in the gas station.

[0027] The early warning and alarm module includes sound alarm, light signal, mobile phone APP push and computer terminal display. The sound alarm is an IP speaker.

[0028] The data storage and management module consists of a file server, a core business server and a database server. The data storage and management module uses encrypted transmission. The data storage and management module uses an API interface to connect to documents. Illegal capture events are uploaded to the data business server via HTTP / HTTPS.

[0029] The edge all-in-one machine is connected to the switch via a network cable. Log in to the device system client to configure the camera monitoring line. After intelligent analysis of the data collected by the camera, the data of the relevant captured events is transmitted to the user business server in HTTP / HTTPS mode.

[0030] The targets in the target detection module are people, vehicles and objects, which are used to identify the category, location and size of the targets. The behavior recognition module identifies whether the personnel are wearing safety protection equipment, whether they are making phone calls or smoking violations, whether the vehicles are parked illegally, and whether the tanker trucks are unloading oil according to regulations.

[0031] The intelligent video analysis module uses feature extraction and selection, deep learning models, and data fusion algorithms for image processing. The data acquisition module supports human structured analysis, vehicle structured analysis, general behavior analysis, and supports single-line multi-capability configuration.

[0032] like Figure 2-5 As shown, the deployment of a single device is to connect multiple network cameras to the switch, and then connect to a single edge computing all-in-one machine. The deployment of multiple devices is to connect multiple network cameras to the switch, and then connect to multiple edge computing all-in-one machines, and connect to a central service node at the same time. First of all, mobile phones cannot be used during the whole monitoring process, and smoking is not allowed. The system will monitor and query by itself. At the same time, an alarm will be issued when the oil stabilization time is insufficient. The specific standard process standards are as follows. First, the vehicle is heated, and then the vehicle blocker will be placed. If the vehicle blocker is not placed, a reminder will be issued. After the standard placement, the next step will be to connect the anti-static equipment. If the anti-electric equipment is not connected, a reminder will be issued. The standard connection will proceed to the next step of placing fire-fighting equipment and signs. If the fire-fighting equipment and signs are not placed, a reminder will be issued. The standard placement will proceed to the next step of oil pipe docking. At the same time, double monitoring will be carried out on duty. If the personnel exceed five minutes, there will be a warning of leaving the post. If there is no reminder, the next oil pipe and electrostatic line will be disassembled. The disassembly sequence will be monitored during disassembly, and a reminder will be issued if there is a misunderstanding in the sequence. Finally, the items are returned. All items are placed in the specified position without triggering a reminder that the items are not returned. In this way, the standard operation process is realized. By monitoring and alarming all processes, no mistakes will occur during the operation.

[0033] When in use, the high-definition cameras installed in the gas station capture the video images of the gas station in real time, and transmit these video streams to the intelligent video analysis system, followed by video analysis. The intelligent video analysis system uses AI video analysis technology to conduct in-depth analysis and identification of the front-end real-time video stream. Through video construction, big data mining, portrait image comparison and vehicle identification, it can identify various information such as personnel behavior, vehicle information and safety hazards. There will be behavior recognition in the process. The system can identify various behaviors in the gas station, including personnel not wearing work clothes, not wearing work hats, smoking, and making phone calls, as well as fire, smoke, channel stacking, abnormal oil loading and unloading operations and other on-site abnormal situations. At the same time, it also has early warning and alarm functions. When the system identifies violations or abnormal situations, it will immediately trigger the early warning and alarm mechanism. The early warning information can be conveyed to the gas station managers in a variety of ways so that timely measures can be taken. Finally, it will be recorded and feedback. The intelligent video analysis system will also automatically generate a chain of evidence file to record the time, location, and personnel information of the violations or abnormal situations, and store and manage them through the data storage and management module.

[0034] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence monitoring and analysis of gas station management and operation method, including a data acquisition module, an intelligent video analysis system, an early warning and alarm module, a data storage and management module, a user interaction interface and an edge computing all-in-one machine, characterized in that: The data acquisition module is used to capture real-time video streams and transmit them to the intelligent video analysis system. The intelligent video analysis system is used to receive the video streams of the data acquisition module and use AI video analysis technology to perform in-depth analysis and identification of the video content. The early warning and alarm module is used to trigger the early warning and alarm mechanism when the data acquisition module identifies illegal behavior or abnormal situations. The data storage and management module is used to store and manage all video streams and identification results. The user interaction interface is used for managers to view monitoring images, receive early warning information and review historical records. The edge computing all-in-one machine is used to reduce delays and improve data processing efficiency. The intelligent video analysis system includes a video processing module, a target detection module, a behavior recognition module, an anomaly detection module, a big data analysis module and an algorithm module. The video processing module uses an image processing algorithm to pre-process the video stream. The target detection module uses deep learning AI technology to perform real-time detection of targets in the video. The behavior recognition module analyzes the target's behavior pattern based on the target detection module. The anomaly detection module identifies abnormal situations by comparing historical data and preset rules. The big data analysis module uses an optimized algorithm model to deeply mine and analyze the collected video data and behavior data.

2. According to claim 1, an artificial intelligence monitoring and analysis of gas station management and operation method is characterized by: The data acquisition module is a network camera, a spherical camera and a hemispherical camera, and the data acquisition module is installed in the refueling area, the oil unloading area, the convenience store and the oil tank area in the gas station.

3. According to claim 1, an artificial intelligence monitoring and analysis of gas station operation and management method is characterized by: The early warning and alarm module includes sound alarm, light signal, mobile phone APP push and computer terminal display, and the sound alarm is an IP speaker.

4. According to claim 1, an artificial intelligence monitoring and analysis of gas station management and operation method is characterized by: The data storage and management module is a file server, a core business server and a database server. The data storage and management module adopts encrypted transmission. The data storage and management module adopts an API interface to connect documents. Illegal capture events are uploaded to the data business server via HTTP / HTTPS.

5. According to claim 1, an artificial intelligence monitoring and analysis of gas station management and operation method is characterized by: The edge all-in-one machine is connected to the switch via a network cable, and the device system client is logged in to configure the camera monitoring line. After intelligent analysis of the data collected by the camera, the data of the relevant captured events is transmitted to the user business server in HTTP / HTTPS mode.

6. The method of artificial intelligence monitoring and analyzing the operation and management of a gas station according to claim 1, characterized in that: The targets in the target detection module are people, vehicles and objects, which are used to identify the category, location and size of the targets. The behavior recognition module identifies whether the personnel are wearing safety protection equipment, whether they have illegal behaviors such as making phone calls and smoking, whether the vehicles are parked illegally, and whether the tanker trucks are unloading oil according to regulations.

7. The method of artificial intelligence monitoring and analyzing the operation and management of a gas station according to claim 1, characterized in that: The intelligent video analysis module uses feature extraction and selection, deep learning models, and data fusion algorithms to perform image processing. The data acquisition module supports human structured analysis, vehicle structured analysis, general behavior analysis, and supports single-line multi-capability configuration.

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