Visual alarm system and alarm method for gas station, and storage medium

Abstract

The invention discloses a visual alarm system and alarm method for a gas station, and a storage medium, and relates to the technical field of industrial visual detection. The system comprises an image acquisition subsystem, an image analysis subsystem, an alarm data storage subsystem and a gas safety management platform, the image acquisition subsystem is configured to acquire video information of areas corresponding to the cameras by using the cameras; the image analysis subsystem is configured to analyze the video information by using a pre-trained artificial intelligence model, and if an abnormal behavior exists in the video information, the risk level of the abnormal behavior is determined and alarm information is generated; the alarm data storage subsystem is configured to store the alarm information to a database table corresponding to the risk level according to the risk level; and the gas safety management platform is configured to extract the alarm information from the database table corresponding to the risk level, and pops up and displays the alarm information on an operation interface. By adopting the system, the gas field station can be monitored around the clock, and the operation safety is ensured.

Description

Technical Field

[0001] The present application relates to the field of industrial visual detection technology, and in particular to a visual alarm system, an alarm method, and a storage medium for a gas station. Background Art

[0002] Gas stations are basic network units in the natural gas transmission system pipeline. They not only contain a large amount of flammable and explosive substances such as natural gas and hydrocarbons, but also contain gas industrial equipment such as filtering, pressure regulation, metering and gas storage. Once a natural gas leak occurs, it may cause a fire or even an explosion, seriously threatening personal and property safety. Therefore, the safe operation of gas stations is of great importance.

[0003] Currently, the relevant technology uses security personnel to patrol gas stations, but people tend to get sleepy at night and cannot concentrate on monitoring around the clock. In addition, there are many devices in gas stations, which are easy to miss, which can lead to inadequate inspections and bring safety hazards. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the related art, it is desired to provide a visual alarm system, an alarm method, and a storage medium for a gas station, which can monitor the gas station around the clock to ensure safe operation.

[0005] In a first aspect, the present application provides a visual alarm system for a gas station, the visual alarm system comprising an image acquisition subsystem, an image analysis subsystem, an alarm data storage subsystem and a gas safety management platform connected in sequence, the image acquisition subsystem comprising a router and cameras connected to the router and distributed in different areas of the gas station;

[0006] The image acquisition subsystem is configured to use each of the cameras to collect video information of the area corresponding to the camera, and send the video information to the image analysis subsystem through the router; the image analysis subsystem is configured to use a pre-trained artificial intelligence model to analyze the video information, and if there is abnormal behavior in the video information, determine the risk level of the abnormal behavior and generate alarm information of the abnormal behavior;

[0007] The alarm data storage subsystem is configured to store the alarm information of the abnormal behavior in a database table corresponding to the risk level of the abnormal behavior according to the risk level of the abnormal behavior; the gas safety management platform is configured to extract the alarm information of the abnormal behavior from the database table corresponding to the risk level, and pop up the alarm information on the operation interface for display.

[0008] Optionally, in some embodiments of the present application, the image analysis subsystem is specifically used to obtain the regional attributes contained in the video information, where the regional attributes are gas equipment areas, personnel gathering areas or fire protection facilities areas, and call the artificial intelligence model corresponding to the regional attributes to analyze the video information.

[0009] Optionally, the artificial intelligence model corresponding to the gas equipment area in some embodiments of the present application is pre-trained through the following steps:

[0010] Constructing multiple viewing angle characteristic diagrams of the gas valve in different working states, and marking the working state corresponding to each viewing angle characteristic diagram, where the working state corresponding to the viewing angle characteristic diagram is open or closed;

[0011] Each of the viewing angle feature maps and the working status corresponding to the viewing angle feature map are input into a first artificial intelligence network structure for training to obtain an artificial intelligence model corresponding to the gas equipment area.

[0012] Optionally, the artificial intelligence model corresponding to the personnel gathering area in some embodiments of the present application is pre-trained through the following steps:

[0013] Acquire a set of historical clothing images of station personnel, and add a classification label to each historical clothing image in the set of historical clothing images of station personnel, wherein the classification label may represent standard clothing or non-standard clothing;

[0014] Each historical dress image in the historical dress image set of the station personnel is divided into a training set or a verification set, and the training set is used to train the second artificial intelligence network structure, and the verification set is used to verify the trained second artificial intelligence network structure, so as to obtain an artificial intelligence model corresponding to the personnel gathering area.

[0015] Optionally, the artificial intelligence model corresponding to the fire protection facility area in some embodiments of the present application is pre-trained by the following steps:

[0016] Acquire a flame image set, wherein each flame image in the flame image set has different colors and sizes;

[0017] Each flame image in the flame image set is input into a third artificial intelligence network structure for training to obtain an artificial intelligence model corresponding to the firefighting facility area.

[0018] Optionally, in some embodiments of the present application, the gas safety management platform is specifically used to detect the risk level. If the risk level is general, the alarm information is displayed in a yellow pop-up box on the operation interface; if the risk level is serious, the alarm information is displayed in a red pop-up box on the operation interface, and the alarm information is broadcast to the outside.

[0019] Optionally, in some embodiments of the present application, the gas safety management platform is further specifically used to export the alarm information of the abnormal behavior from the database table corresponding to the risk level.

[0020] In a second aspect, the present application provides a visual alarm method for a gas station, which can be used for the visual alarm system described in any one of the first aspects, and the visual alarm method includes:

[0021] Collect video information from different areas of the gas station;

[0022] Analyze the video information using a pre-trained artificial intelligence model, and if there is abnormal behavior in the video information, determine the risk level of the abnormal behavior and generate alarm information of the abnormal behavior;

[0023] According to the risk level of the abnormal behavior, the alarm information of the abnormal behavior is stored in a database table corresponding to the risk level;

[0024] The alarm information of the abnormal behavior is extracted from the database table corresponding to the risk level, and the alarm information is popped up and displayed on the operation interface.

[0025] Optionally, in some embodiments of the present application, analyzing the video information using a pre-trained artificial intelligence model includes:

[0026] The regional attributes contained in the video information are obtained, where the regional attributes are a gas equipment area, a personnel gathering area or a fire-fighting facility area, and an artificial intelligence model corresponding to the regional attributes is called to analyze the video information.

[0027] In a third aspect, the present application provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the visual alarm method described in any one of the second aspects.

[0028] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0029] The embodiments of the present application provide a visual alarm system, an alarm method, and a storage medium for a gas station. Video information is collected around the clock and without blind spots through cameras distributed in different areas of the gas station without human intervention. A pre-trained artificial intelligence model is then used to quickly analyze the video information in real time. If there is abnormal behavior in the video information, the risk level of the abnormal behavior can be determined and alarm information of the abnormal behavior can be generated. The alarm information of the abnormal behavior can then be extracted from a database table corresponding to the risk level, and the alarm information can be popped up on an operation interface for display, which is convenient, intuitive, and clear at a glance, ensuring the safe operation and high reliability of the gas station. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A structural block diagram of a visual alarm system provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of a partial structure of a visual alarm system provided in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of a partial structure of another visual alarm system provided in an embodiment of the present application;

[0034] Figure 4 A flow chart of a visual alarm method for a gas station provided in an embodiment of the present application.

[0035] Reference numerals:

[0036] 100- visual alarm system, 101- image acquisition subsystem, 1011- router, 1012- camera, 102- image analysis subsystem, 103- alarm data storage subsystem, 104- gas safety management platform. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. Figures 1 to 4 The visual alarm system, alarm method and storage medium for a gas station provided in the embodiments of the present application are described in detail.

[0040] Please refer to Figure 1 , which is a structural block diagram of a visual alarm system provided in an embodiment of the present application, the visual alarm system 100 includes an image acquisition subsystem 101, an image analysis subsystem 102, an alarm data storage subsystem 103 and a gas safety management platform 104 connected in sequence, and as Figure 2 As shown, the image acquisition subsystem 101 includes a router 1011 and cameras 1012 connected to the router 1011 and distributed in different areas of the gas station, wherein the different areas include but are not limited to gas equipment areas, personnel gathering areas, and fire protection facilities areas, etc., so that the cameras 1012 can be used to collect video information in real time around the clock.

[0041] In actual use, the image acquisition subsystem 101 can use each camera 1012 to collect video information of the area corresponding to the camera, and send the video information to the image analysis subsystem 102 through the router 1011. Then, the image analysis subsystem 102 can use the pre-trained artificial intelligence (AI) model to analyze the video information. If there is abnormal behavior in the video information, the risk level of the abnormal behavior is determined and the alarm information of the abnormal behavior is generated. If there is no abnormal behavior in the video information, the video information is discarded and the storage resources are released. For example, the image analysis subsystem 102 can specifically obtain the regional attributes contained in the video information, which are the gas equipment area, the personnel gathering area or the fire protection facility area, and call the artificial intelligence model corresponding to the regional attribute to analyze the video information. Among them, when pre-training the artificial intelligence model corresponding to the gas equipment area, some embodiments of the present application can manually construct multiple viewing angle feature maps of the gas valve in different working states through computer vision technology, and mark the working state corresponding to each viewing angle feature map. For example, the working state corresponding to the viewing angle feature map is open or closed. Multiple viewing angle feature maps include but are not limited to the main view, side view and top view, etc. Then, each viewing angle feature map and the working state corresponding to the viewing angle feature map are input into the first artificial intelligence network structure for efficient training to obtain the artificial intelligence model corresponding to the gas equipment area, thereby improving the model recognition ability. At this time, the abnormal behavior in the video information is that the gas valve is open, which indicates that there is a hidden danger of gas leakage. The risk level of the abnormal behavior is serious, and the alarm information of the abnormal behavior is that the gas valve in the gas equipment area corresponding to the video information is open.

[0042] When pre-training the artificial intelligence model corresponding to the personnel gathering area, some embodiments of the present application can obtain a set of historical dress images of station personnel, and add classification labels to each historical dress image in the set of historical dress images of station personnel. The classification label can represent standard dress or non-standard dress, for example, using 0 to represent standard dress and 1 to represent non-standard dress. Of course, it can also be directly expressed in text. Then, each historical dress image in the set of historical dress images of station personnel is divided into a training set or a verification set, and the training set is used to train the second artificial intelligence network structure and the verification set is used to verify the trained second artificial intelligence network structure to obtain the artificial intelligence model corresponding to the personnel gathering area. The advantage of this setting is that the station personnel walk back and forth and wear various clothes. By verifying and dynamically adjusting the training parameters, it can ensure that the obtained artificial intelligence model is more accurate, and the stability and generalization ability of the model are improved. At this time, the abnormal behavior in the video information is non-standard dress, and the risk level of the abnormal behavior is general. The alarm information of the abnormal behavior is that the personnel in the personnel gathering area corresponding to the video information are not dressed in a standard manner. Also, when pre-training the artificial intelligence model corresponding to the firefighting facility area, some embodiments of the present application can directly obtain a set of flame images, each flame image in the flame image set has different colors and sizes, and input each flame image in the flame image set into a third artificial intelligence network structure for training to obtain an artificial intelligence model corresponding to the firefighting facility area. At this time, the abnormal behavior in the video information is the presence of flames, the risk level of the abnormal behavior is serious, and the alarm information of the abnormal behavior is that a fire occurs in the firefighting facility area corresponding to the video information. In addition, when pre-training the artificial intelligence model, some embodiments of the present application can also divide images such as historical clothing images and flame images into multiple different areas, remove noise and invalid data, ensure data quality, and increase data diversity through operations such as rotation, scaling, and cropping to improve the generalization ability of the model.

[0043] Furthermore, the alarm data storage subsystem 103 can store the alarm information of abnormal behavior in a database table corresponding to the risk level according to the risk level of the abnormal behavior, for example, a general risk level corresponds to a database table, and a serious risk level corresponds to a database table. For another example, the image analysis subsystem 102 and the alarm data storage subsystem 103 are distributedly deployed in two server hosts. Figure 3As shown, the gas safety management platform 104 can extract the alarm information of abnormal behavior from the database table corresponding to the risk level, and display the alarm information in the operation interface to realize human-computer interaction, thereby clarifying the priority of on-site hidden danger handling through alarm classification, which is more intelligent. For example, the gas safety management platform 104 includes but is not limited to computer equipment and tablet computers, etc. The gas safety management platform 104 can specifically detect the risk level. If the risk level is general, the alarm information is displayed in the yellow pop-up box of the operation interface, and if the risk level is serious, the alarm information is displayed in the red pop-up box of the operation interface, and the alarm information is broadcast to the outside, such as issuing a sharp alarm sound, sending text messages to important personnel such as the station manager, or directly dialing the fire alarm phone, etc. For another example, the gas safety management platform 104 can also specifically derive the alarm information of abnormal behavior from the database table corresponding to the risk level. At this time, the alarm information includes the image of the accident. The advantage of such a setting is to facilitate the summary of experience and accountability, and ensure the safety of subsequent gas station operations.

[0044] The visual alarm system for a gas station provided in the embodiment of the present application collects video information around the clock and without blind spots through cameras distributed in different areas of the gas station, without the need for human intervention, and then uses a pre-trained artificial intelligence model to perform real-time and rapid analysis of the video information. If there is abnormal behavior in the video information, the risk level of the abnormal behavior can be determined and alarm information of the abnormal behavior can be generated. The alarm information of the abnormal behavior can then be extracted from a database table corresponding to the risk level, and the alarm information can be popped up and displayed on the operation interface, which is convenient, intuitive, and clear at a glance, ensuring the safe operation and high reliability of the gas station.

[0045] Based on the above embodiments, the present application provides a visual alarm method for a gas station, which can be used to Figures 1 to 3 The visual warning system 100 of the corresponding embodiment. Figure 4 , which is a flow chart of a visual alarm method for a gas station provided in an embodiment of the present application, and the visual alarm method specifically comprises the following steps:

[0046] S101, collecting video information of different areas of the gas station.

[0047] Exemplarily, the embodiment of the present application can utilize each camera 1012 of the image acquisition subsystem 101 in the visual alarm system 100 to collect video information of the area corresponding to the camera, where the area corresponding to the camera is a gas equipment area, a personnel gathering area, or a fire protection facility area, and then send the video information to the image analysis subsystem 102 through the router 1011.

[0048] S102, using a pre-trained artificial intelligence model to analyze the video information, if there is abnormal behavior in the video information, determine the risk level of the abnormal behavior, and generate alarm information for the abnormal behavior.

[0049] Exemplarily, the embodiment of the present application can use the image analysis subsystem 102 in the visual alarm system 100 to obtain the regional attributes contained in the video information, and the regional attributes are the gas equipment area, the personnel gathering area or the fire protection facility area, and call the artificial intelligence model corresponding to the regional attributes to analyze the video information. The abnormal behavior in the video information can be the opening of the gas valve. At this time, the risk level of the abnormal behavior is serious, and the alarm information of the abnormal behavior is that the gas valve in the gas equipment area corresponding to the video information is opened; the abnormal behavior in the video information can also be non-standard dress, at this time, the risk level of the abnormal behavior is general, and the alarm information of the abnormal behavior is that the personnel in the personnel gathering area corresponding to the video information are not dressed in a standard manner; and the abnormal behavior in the video information can also be the presence of flames. At this time, the risk level of the abnormal behavior is serious, and the alarm information of the abnormal behavior is that a fire occurs in the fire protection facility area corresponding to the video information. Further, the image analysis subsystem 102 sends the alarm information to the alarm data storage subsystem 103 through the database configuration tool.

[0050] S103, according to the risk level of the abnormal behavior, storing the alarm information of the abnormal behavior in a database table corresponding to the risk level.

[0051] Exemplarily, the risk levels of abnormal behaviors in the embodiments of the present application include but are not limited to general and severe, and the general risk level corresponds to a database table, and the severe risk level corresponds to a database table.

[0052] S104, extracting alarm information of abnormal behavior from a database table corresponding to the risk level, and popping up the alarm information on the operation interface.

[0053] Exemplarily, the embodiment of the present application can utilize the gas safety management platform 104 in the visual alarm system 100 to detect the risk level. If the risk level is general, the alarm information is displayed in a yellow pop-up box on the operation interface. If the risk level is serious, the alarm information is displayed in a red pop-up box on the operation interface, and the alarm information is broadcast to the outside, such as by issuing a sharp alarm sound, sending text messages to important personnel such as the plant manager, or directly calling the fire alarm number.

[0054] It should be noted that, for the description of the same steps and the same contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0055] The visual alarm method for a gas station provided in the embodiment of the present application collects video information around the clock and without blind spots through cameras distributed in different areas of the gas station, without the need for human intervention, and then uses a pre-trained artificial intelligence model to quickly analyze the video information in real time. If there is abnormal behavior in the video information, the risk level of the abnormal behavior can be determined and alarm information of the abnormal behavior can be generated. The alarm information of the abnormal behavior can then be extracted from a database table corresponding to the risk level, and the alarm information can be popped up on the operation interface for display, which is convenient, intuitive, and clear at a glance, ensuring the safe operation and high reliability of the gas station.

[0056] As another aspect, the present application provides a computer-readable storage medium for storing program code, the program code for executing the aforementioned Figure 4 The steps of the visual alarm method of the corresponding embodiment.

[0057] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0058] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0059] In addition, each functional module in each embodiment of the present application may be integrated into a processing unit, or each module may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.

[0060] Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the visual alarm method of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0061] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A visual alarm system for a gas station, characterized in that: The visual alarm system includes an image acquisition subsystem, an image analysis subsystem, an alarm data storage subsystem and a gas safety management platform connected in sequence, and the image acquisition subsystem includes a router and cameras connected to the router and distributed in different areas of the gas station; The image acquisition subsystem is configured to use each of the cameras to collect video information of the area corresponding to the camera, and send the video information to the image analysis subsystem through the router; the image analysis subsystem is configured to use a pre-trained artificial intelligence model to analyze the video information, and if there is abnormal behavior in the video information, determine the risk level of the abnormal behavior and generate alarm information of the abnormal behavior; The alarm data storage subsystem is configured to store the alarm information of the abnormal behavior in a database table corresponding to the risk level of the abnormal behavior according to the risk level of the abnormal behavior; the gas safety management platform is configured to extract the alarm information of the abnormal behavior from the database table corresponding to the risk level, and pop up the alarm information on the operation interface for display.

2. The visual alarm system according to claim 1, characterized in that: The image analysis subsystem is specifically used to obtain the regional attributes contained in the video information, where the regional attributes are gas equipment areas, personnel gathering areas or fire protection facilities areas, and call the artificial intelligence model corresponding to the regional attributes to analyze the video information.

3. The visual alarm system according to claim 2, characterized in that: The artificial intelligence model corresponding to the gas equipment area is pre-trained through the following steps: Constructing multiple viewing angle characteristic diagrams of the gas valve in different working states, and marking the working state corresponding to each viewing angle characteristic diagram, where the working state corresponding to the viewing angle characteristic diagram is open or closed; Each of the viewing angle feature maps and the working status corresponding to the viewing angle feature map are input into a first artificial intelligence network structure for training to obtain an artificial intelligence model corresponding to the gas equipment area.

4. The visual alarm system according to claim 2, characterized in that: The artificial intelligence model corresponding to the personnel gathering area is pre-trained through the following steps: Acquire a set of historical clothing images of station personnel, and add a classification label to each historical clothing image in the set of historical clothing images of station personnel, wherein the classification label may represent standard clothing or non-standard clothing; Each historical dress image in the historical dress image set of the station personnel is divided into a training set or a verification set, and the training set is used to train the second artificial intelligence network structure, and the verification set is used to verify the trained second artificial intelligence network structure, so as to obtain an artificial intelligence model corresponding to the personnel gathering area.

5. The visual alarm system according to claim 2, characterized in that: The artificial intelligence model corresponding to the fire protection facility area is pre-trained through the following steps: Acquire a flame image set, wherein each flame image in the flame image set has different colors and sizes; Each flame image in the flame image set is input into a third artificial intelligence network structure for training to obtain an artificial intelligence model corresponding to the firefighting facility area.

6. The visual warning system according to any one of claims 1 to 5, characterized in that: The gas safety management platform is specifically used to detect the risk level. If the risk level is general, the alarm information is displayed in a yellow pop-up box on the operation interface. If the risk level is serious, the alarm information is displayed in a red pop-up box on the operation interface and the alarm information is broadcast to the outside.

7. The visual alarm system according to claim 6, characterized in that: The gas safety management platform is also specifically used to export the alarm information of the abnormal behavior from the database table corresponding to the risk level.

8. A visual alarm method for a gas station, characterized in that: The visual alarm method can be used in the visual alarm system according to any one of claims 1 to 7, and the visual alarm method comprises: Collect video information from different areas of the gas station; Analyze the video information using a pre-trained artificial intelligence model, and if there is abnormal behavior in the video information, determine the risk level of the abnormal behavior and generate alarm information of the abnormal behavior; According to the risk level of the abnormal behavior, the alarm information of the abnormal behavior is stored in a database table corresponding to the risk level; The alarm information of the abnormal behavior is extracted from the database table corresponding to the risk level, and the alarm information is popped up and displayed on the operation interface.

9. The visual alarm method according to claim 8, characterized in that: The analyzing the video information by using a pre-trained artificial intelligence model includes: The regional attributes contained in the video information are obtained, where the regional attributes are a gas equipment area, a personnel gathering area or a fire-fighting facility area, and an artificial intelligence model corresponding to the regional attributes is called to analyze the video information.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the visual alarm method according to any one of claims 8 to 9.

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