Forest fire early-stage monitoring and early-warning method and system

Abstract

The invention provides a forest fire early-stage monitoring and early-warning method and system, and belongs to the technical field of monitoring and early-warning, and the method specifically comprises the steps: dividing a target forest region into a plurality of sub-regions according to a preset area, and determining the wind direction of the target forest region based on the weather data of the current date, according to the wind direction, determining influence adjacent areas when a fire occurs in the target forest area, determining influence conditions of different influence adjacent areas on Internet of Things equipment in other areas, and combining spacing distances between different sub-areas and the influence adjacent areas and distribution data of Internet of Things gateway equipment; the fire monitoring and early warning method of the target forest area is determined, and the efficiency of fire monitoring and early warning processing is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of monitoring and early warning, and in particular relates to a forest fire early monitoring and early warning method and system. Background Art

[0002] In order to achieve early monitoring and early warning of forest fires, the invention patent application CN202510287490.6 "A forest fire prevention method based on smoke recognition" automatically learns and extracts key features such as smoke color, texture, and shape from pre-processed image data to obtain feature representation. When the fire level judgment result meets the warning conditions, the warning signal is automatically triggered, and early warning of the fire is achieved through message push, sound and light alarm, etc. However, there are the following technical problems: The existing technical solutions use IoT devices, gateways, etc. to monitor and warn of forest fires. However, once a fire occurs, it will inevitably cause damage to the IoT devices and gateways. As a result, the reliability of monitoring and early warning in some monitoring areas may be difficult to meet the requirements. Therefore, how to generate targeted monitoring and early warning processing strategies has become a technical problem that needs to be solved urgently.

[0003] In order to solve the above technical problems, the present application provides a forest fire early monitoring and warning method and system. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a forest fire early monitoring and early warning method, which specifically includes: S1 takes the IoT gateway device in the target forest area as the target gateway device, takes the IoT devices in other areas using the target gateway device as the influencing devices, and determines the influencing areas in other areas based on the installation distribution of the influencing devices in other areas; S2 proceeds to the next step when it is determined that the target forest area does not belong to the risk area based on the distribution of the affected area and the distribution data of the IoT devices in the affected area; S3: dividing the target forest area into a plurality of sub-areas according to a preset area, determining the wind direction of the target forest area based on weather data of the current date, and determining the adjacent area affected when a fire occurs in the target forest area according to the wind direction; S4 determines the impact of different IoT devices in the affected adjacent areas on other areas, and determines the fire monitoring and early warning method for the target forest area based on the interval distances between different sub-areas and the affected adjacent areas and the distribution data of IoT gateway devices.

[0005] The beneficial effects of the present invention are: Based on the distribution of the affected areas and the distribution data of the Internet of Things devices in the affected areas, it is determined whether the target forest area belongs to the risk area, realizing the screening of the risk area from the perspective of monitoring the impact on other areas when a fire occurs in the target forest area. Furthermore, it lays a foundation for generating differentiated fire monitoring and early warning strategies according to the differences in the impact situations, ensuring the timeliness and reliability of the fire monitoring and early warning.

[0006] A fire monitoring and early warning method for the target forest area is determined according to the impact on the Internet of Things devices in other areas in different affected adjacent areas, the interval distances between different sub-areas and the affected adjacent areas, and the distribution data of the Internet of Things gateway devices. It takes into account both the impact on adjacent areas and the impact of adjacent areas on other areas when a fire occurs, and also considers the differences in the impact degrees of adjacent areas due to the differences in the interval distances. Additionally, it further combines the impact on other areas when a fire occurs in itself caused by the distribution data of the Internet of Things gateway devices, realizing the determination of the differences in the fire monitoring and early warning method for the target forest area, ensuring the timeliness of the fire monitoring and early warning, and reducing the probability of impact on other areas.

[0007] A further technical solution is that the other areas are the areas located at the same forest location as the target forest area.

[0008] A further technical solution is that the Internet of Things devices are devices for fire risk monitoring.

[0009] A further technical solution is that the Internet of Things devices include monitoring devices, smoke sensing devices, and infrared monitoring devices.

[0010] A further technical solution is that the method for determining the affected areas in the other areas is as follows: Based on the installation distribution of the impact devices in the other areas, determine the number of impact devices in the other areas; According to the proportion of the number of impact devices in the other areas to the number of Internet of Things devices in the other areas, determine whether the other areas are affected areas.

[0011] A further technical solution is that when the proportion of the number of impact devices in the other areas to the number of Internet of Things devices in the other areas is greater than the preset proportion of the number of impact devices, it is determined that the other areas are affected areas.

[0012] A further technical solution is that the method for determining the fire monitoring and early warning method for the target forest area is as follows: Determine the number of Internet of Things devices in other sub-regions that adopt Internet of Things gateway devices in different adjacent regions according to the influence on Internet of Things devices in other regions in adjacent regions, and the proportion in the number in the other sub-regions. Determine the device influence area in the other regions based on the proportion. Determine the sub-regions with the interval distance between the sub-regions and the adjacent regions less than the preset distance threshold according to the interval distance between the sub-regions and the adjacent regions, and use them as adjacent sub-regions. According to the distribution data of Internet of Things gateway devices in the sub-regions, determine the sub-regions of other regions that utilize the Internet of Things gateway devices in the sub-regions, and use them as fault influence sub-regions. Determine the fire monitoring and early warning method for the target forest area according to the number of the device influence areas, the adjacent sub-regions, and the number of the fault influence sub-regions.

[0013] A further technical solution lies in that the device influence area is other regions with a proportion greater than the preset proportion threshold.

[0014] A further technical solution lies in that determining the fire monitoring and early warning method for the target forest area according to the number of the device influence areas, the adjacent sub-regions, and the number of the fault influence sub-regions specifically includes: When the number of device influence areas in the target forest area is greater than the preset device influence area number threshold, when any monitoring device in the target forest area monitors a fire signal, the target forest area outputs an early warning signal. When the number of device influence areas in the target forest area is not greater than the preset device influence area number threshold, determine the fire monitoring and early warning method for the target forest area according to the number of the adjacent sub-regions and the number of the fault influence sub-regions.

[0015] A further technical solution lies in that determining the fire monitoring and early warning method for the target forest area according to the number of the adjacent sub-regions and the number of the fault influence sub-regions specifically includes: When the sum of the number of adjacent sub-regions and the number of fault influence sub-regions in the target forest area accounts for a proportion greater than the preset influence sub-region number proportion in the sub-regions in the target forest area, when any monitoring device in the target forest area monitors a fire signal, the target forest area outputs an early warning signal. When the sum of the number of adjacent sub - regions and the number of failure - affected sub - regions in the target forest region accounts for no more than a preset proportion of the number of sub - regions in the target forest region: then when the monitoring device in the adjacent sub - region or the failure - affected sub - region in the target forest region monitors a fire signal or the number of sub - regions that monitor a fire signal is greater than a preset threshold of the number of sub - regions at fire risk, the target forest region outputs a warning signal.

[0016] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above - mentioned method for early monitoring and warning of forest fires.

[0017] Other features and advantages will be described in the subsequent description. The objectives and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the description and the drawings.

[0018] To make the above - mentioned objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. Description of the Drawings

[0019] By referring to the drawings and describing its exemplary embodiments in detail, the above - mentioned and other features and advantages of the present invention will become more obvious; Figure 1 is a flowchart of a method for early monitoring and warning of forest fires; Figure 2 is a flowchart of a method for determining the affected area in other regions; Figure 3 is a flowchart for determining that the target forest region does not belong to a risk area; Figure 4 is a flowchart of a method for determining the method of fire monitoring and warning for the target forest region; Figure 5 is a framework diagram of a computer system. Detailed Embodiments

[0020] In order to enable those skilled in the art of this technology to better understand the technical solutions in this specification, the following will, in conjunction with the drawings in the embodiments of this specification, clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the scope of protection of this specification.

[0021] In this application, by using the setting data of the Internet of Things (IoT) gateway devices in the target forest area, IoT devices using IoT gateway devices in other areas are determined, and a differentiated fire monitoring and early warning method is generated based on the number of IoT devices using IoT gateway devices in other areas, thereby avoiding the impact of fire on the monitoring reliability of other areas.

[0022] Embodiment 1 is as Figure 1 shown. This application provides a method for early monitoring and early warning of forest fires, which specifically includes: S1: Use the IoT gateway devices in the target forest area as target gateway devices, and use the IoT devices using the target gateway devices in other areas as influencing devices. Based on the installation and distribution of the influencing devices in other areas, determine the influencing areas in other areas; The influencing area is an area in which the proportion of the number of influencing devices among IoT devices is greater than 0.2.

[0023] S2: Based on the distribution of the influencing areas and the distribution data of the IoT devices in the influencing areas, when it is determined that the target forest area does not belong to the risk area, proceed to the next step; Regard the influencing areas where the number of IoT devices in the influencing areas is within a preset range of the number of IoT devices as monitoring deviation areas. When the number of monitoring deviation areas is more than 3, it is determined that the target forest area is a risk area.

[0024] S3: Divide the target forest area into multiple sub-areas according to a preset area. Determine the wind direction of the target forest area based on the weather data of the current date, and determine the affected adjacent areas when a fire occurs in the target forest area according to the wind direction; The affected adjacent area is an area that is downwind of the target forest area and has a distance from the target forest area less than a preset distance threshold.

[0025] S4: Determine the impact on the IoT devices in other areas in different affected adjacent areas, and combine the interval distance between different sub-areas and the affected adjacent areas and the distribution data of the IoT gateway devices to determine the fire monitoring and early warning method for the target forest area.

[0026] When the number of IoT gateway devices used by the IoT devices in other areas in the affected adjacent area is greater than the gateway device number threshold, when any monitoring device in the target forest area detects a fire signal, the target forest area outputs an early warning signal; When the number of IoT gateway devices used by IoT devices in other areas that affect adjacent areas is not greater than the gateway device number threshold, then when the sub-areas with a distance less than the preset distance threshold from the affected adjacent areas or the sub-areas where the IoT gateway devices used in other areas detect a fire signal or the number of sub-areas that detect a fire signal is more than 5, the target forest area outputs a warning signal.

[0027] Further, the other areas are areas located at the same forest site as the target forest area.

[0028] Specifically, the IoT device is a device for fire risk monitoring.

[0029] It should be noted that the IoT devices include monitoring devices, smoke sensing devices, and infrared monitoring devices.

[0030] Specifically, as Figure 2 shown, the method for determining the affected area in the other areas is as follows: Based on the installation distribution of the affected devices in the other areas, determine the number of affected devices in the other areas; According to the proportion of the number of affected devices in the IoT devices in the other areas in the other areas, determine whether the other areas are affected areas.

[0031] Further, when the proportion of the number of affected devices in the IoT devices in the other areas in the other areas is greater than the preset proportion of the number of affected devices, determine that the other areas are affected areas.

[0032] In another possible embodiment, the method for determining the affected area in the other areas is as follows: Based on the installation distribution of the affected devices in the other areas, determine the number of affected devices in the other areas; Use a preset area to divide the other areas into multiple sub-areas, and determine the monitoring influence coefficients in different sub-areas according to the proportion of the number of affected devices in the IoT devices in different sub-areas; Determine whether the other areas are affected areas according to the monitoring influence coefficients in different sub-areas.

[0033] Further, when there are sub-areas with a monitoring influence coefficient greater than the preset monitoring influence coefficient threshold, determine that the other areas are affected areas.

[0034] It can be understood that when the monitoring influence value of the other areas is greater than the preset monitoring influence threshold, determine that the other areas are affected areas.

[0035] It should be noted that the distribution of the affected areas includes the number of affected areas and their distribution locations in the forest sites.

[0036] Specifically, the distribution data of the Internet of Things devices is determined according to the installation locations of the Internet of Things devices in the affected areas.

[0037] Specifically, as Figure 3 shown, determining that the target forest area does not belong to the risk area specifically includes: Based on the distribution of the affected areas, determine the proportion of the number of affected areas in the forest areas of the forest site, and use it as the proportion of the number of affected areas; According to the distribution data of the Internet of Things devices in different affected areas, determine the proportion of the number of affected devices in the Internet of Things devices in the affected areas, and use it as the regional impact coefficient; Based on the proportion of the number of affected areas and the regional impact coefficients of different affected areas, determine the regional impact risk coefficient of the target forest area, and determine whether the target forest area belongs to the risk area based on the regional impact risk coefficient.

[0038] Furthermore, the regional impact risk coefficient of the target forest area is determined according to the product of the proportion of the number of affected areas and the average value of the regional impact coefficients of different affected areas.

[0039] It can be understood that the value range of the regional impact risk coefficient is between 0 and 1. When the regional impact risk coefficient is greater than the preset regional impact risk coefficient threshold, it is determined that the target forest area belongs to the risk area.

[0040] Furthermore, when the target forest area belongs to the risk area, when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal.

[0041] Specifically, the fire signals include smoke, flames, and smoldering.

[0042] In another possible embodiment, determining that the target forest area does not belong to the risk area specifically includes: Based on the distribution of the affected areas, determine the number of affected areas in the forest site; According to the distribution data of the Internet of Things devices in different affected areas, determine the affected areas where the number of Internet of Things devices is less than the preset device number threshold, and use them as the monitoring deviation affected areas; Based on the number of the monitoring deviation affected areas, determine whether the target forest area belongs to the risk area.

[0043] Further, when the number of monitored deviation influence areas is greater than the preset monitored deviation influence area number threshold, the target forest area is determined as a risk area.

[0044] It can be understood that the adjacent areas affected when a fire occurs in the target forest area are other areas whose distance from the target forest area in the wind direction is less than the preset distance threshold.

[0045] Specifically, as Figure 4 shown, the method for determining the fire monitoring and early warning method of the target forest area is as follows: Based on the influence on the Internet of Things devices in other areas in different adjacent areas affected, determine the number of Internet of Things devices using the Internet of Things gateway devices in different adjacent areas in other sub-areas, the proportion in the number in the other sub-areas, and determine the device influence area in the other areas based on the proportion; Based on the distance between the sub-areas and the adjacent areas affected, determine the sub-areas with a distance less than the preset distance threshold and use them as adjacent sub-areas; According to the distribution data of the Internet of Things gateway devices in the sub-areas, determine the sub-areas of other areas that utilize the Internet of Things gateway devices in the sub-areas and use them as fault influence sub-areas, and determine the fire monitoring and early warning method of the target forest area according to the number of the device influence areas, the adjacent sub-areas, and the number of the fault influence sub-areas.

[0046] Further, the device influence area is other areas with a proportion greater than the preset proportion threshold.

[0047] It can be understood that determining the fire monitoring and early warning method of the target forest area according to the number of the device influence areas, the adjacent sub-areas, and the number of the fault influence sub-areas specifically includes: When the number of device influence areas in the target forest area is greater than the preset device influence area number threshold, when any monitoring device in the target forest area monitors a fire signal, the target forest area outputs a warning signal; When the number of device influence areas in the target forest area is not greater than the preset device influence area number threshold, determine the fire monitoring and early warning method of the target forest area according to the number of the adjacent sub-areas and the fault influence sub-areas.

[0048] Specifically, determining the fire monitoring and early warning method of the target forest area according to the number of the adjacent sub-areas and the fault influence sub-areas specifically includes: When the sum of the number of adjacent sub - regions and fault - affected sub - regions in the target forest area accounts for a proportion greater than the preset proportion of the number of affected sub - regions in the number of sub - regions in the target forest area, then when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal; When the sum of the number of adjacent sub - regions and fault - affected sub - regions in the target forest area accounts for a proportion not greater than the preset proportion of the number of affected sub - regions: then when the monitoring device of the adjacent sub - region or the fault - affected sub - region in the target forest area detects a fire signal or the number of sub - regions where the fire signal is detected is greater than the preset threshold of the number of fire - risk sub - regions, the target forest area outputs a warning signal.

[0049] In another possible embodiment, the method for determining the fire monitoring and warning method of the target forest area is as follows: S41 Determine the number of Internet of Things devices in other sub - regions that use the Internet of Things gateway devices in different affected adjacent regions according to the influence of other regions on the Internet of Things devices in different affected adjacent regions, and the proportion of this number in the number of other sub - regions. Based on this proportion, determine the device - affected area in other regions. According to the number of affected adjacent regions and the number of device - affected areas, determine the spill - over risk coefficient of the target forest area; S42 Determine the sub - regions with an interval distance less than the preset distance threshold based on the interval distance between the sub - regions and the affected adjacent regions, and use them as adjacent sub - regions. According to the distribution data of the Internet of Things gateway devices in the sub - regions, determine the sub - regions in other regions that use the Internet of Things gateway devices in this sub - region, and use them as fault - affected sub - regions. According to the number of adjacent sub - regions and fault - affected sub - regions in the target forest area, and combined with the number of Internet of Things devices in other regions that use the Internet of Things gateway devices in different fault - affected sub - regions, determine the self - risk coefficient of the target forest area; S43 Based on the average value of the spill - over risk coefficient and the self - risk coefficient, determine the risk coefficient evaluation value of the target forest area. Based on the risk coefficient evaluation value, determine the fire monitoring and warning method of the target forest area.

[0050] Further, determining the fire monitoring and warning method of the target forest area based on the risk coefficient evaluation value specifically includes: When the risk coefficient evaluation value of the target forest area is greater than the preset risk evaluation value threshold, then when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal; When the risk coefficient evaluation value of the target forest area is not greater than the preset risk assessment threshold value, then when the monitoring devices in the adjacent sub-areas or the fault-affected sub-areas in the target forest area detect fire signals or the number of sub-areas detecting fire signals is greater than the preset fire risk sub-area number threshold value, the target forest area outputs a warning signal.

[0051] Embodiment 2 In another possible embodiment, determining that the target forest area does not belong to the risk area specifically includes: Based on the distribution of the affected areas, determine the number of affected areas in the target forest area. When the number of affected areas in the target forest area does not meet the requirements, it is determined that the target forest area belongs to the risk area; When the number of affected areas in the target forest area meets the requirements: Determine the proportion of the number of affected areas in the forest area of the forest location in the forest location, and use it as the proportion of the number of affected areas. Combine the number of affected areas in the target forest area and the interval distance to determine the monitoring influence coefficient of the target forest area. When the monitoring influence coefficient of the target forest area is less than the preset influence coefficient threshold value, it is determined that the target forest area does not belong to the risk area; When the monitoring influence coefficient of the target forest area is not less than the preset influence coefficient threshold value: According to the distribution data of the Internet of Things devices in different affected areas, determine the proportion of the number of affected devices in the Internet of Things devices in the affected areas. When the average value of the proportion of the number of affected devices in the Internet of Things devices in different affected areas does not meet the requirements, it is determined that the target forest area belongs to the risk area; When the average value of the proportion of the number of affected devices in the Internet of Things devices in different affected areas meets the requirements: Obtain the proportion of the number of affected devices in the Internet of Things devices in different affected areas, and combine the number of Internet of Things devices in different affected areas to determine the monitoring influence value of different affected areas. When there is an affected area where the monitoring influence value does not meet the requirements, it is determined that the target forest area belongs to the risk area; When there is no affected area where the monitoring influence value does not meet the requirements: Based on the proportion of the number of affected areas and the monitoring influence values of different affected areas, determine the regional influence risk coefficient of the target forest area, and determine whether the target forest area belongs to the risk area based on the regional influence risk coefficient.

[0052] Embodiment 3 Second aspect, as Figure 5As shown, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned method for early monitoring and warning of forest fires.

[0053] Optionally, the above step S41 includes the following content: S411 Determine the number of Internet of Things devices affected in the affected adjacent area based on the impact on other Internet of Things devices in other areas in the adjacent area and the number of Internet of Things devices using different Internet of Things gateway devices in other sub-areas. When the number of Internet of Things devices affected in the affected adjacent area does not meet the requirements, when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal. When the number of Internet of Things devices affected in the affected adjacent area meets the requirements, proceed to step S412; S412 When the number of Internet of Things devices affected in the affected adjacent area is less than the preset value of the number of affected devices, and the number of sub-areas where fire signals are detected is greater than the preset threshold of the number of fire risk sub-areas, the target forest area outputs a warning signal. When the number of Internet of Things devices affected in the affected adjacent area is not less than the preset value of the number of affected devices, proceed to step S413; S413 Determine the number of Internet of Things devices using different Internet of Things gateway devices in other sub-areas and their proportion in the number in other sub-areas. Based on the proportion, determine the device impact area in other areas. When the number of device impact areas does not meet the requirements, when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal. When the number of device impact areas meets the requirements, proceed to step S414; S414 Determine the spillover risk coefficient of the target forest area based on the number of affected adjacent areas and the number of device impact areas. When the spillover risk coefficient of the target forest area does not meet the requirements, when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal. When the spillover risk coefficient of the target forest area meets the requirements, proceed to step S42.

[0054] Optionally, the above step S42 includes the following content: S421 determines the sub-regions with the interval distance between the sub-regions and the adjacent regions less than the preset distance threshold, and takes them as adjacent sub-regions. According to the distribution data of the IoT gateway devices in the sub-regions, it determines the sub-regions of other regions that utilize the IoT gateway devices in the sub-regions, and takes them as fault-affected sub-regions; S422 When the sum of the number of adjacent sub-regions and fault-affected sub-regions in the target forest area does not meet the requirements, then when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal. When the sum of the number of adjacent sub-regions and fault-affected sub-regions in the target forest area meets the requirements, it proceeds to step S423; S423 determines the fault influence coefficient based on the number of fault-affected sub-regions and the number of IoT devices in other regions that utilize the IoT gateway devices in different fault-affected sub-regions. When the fault influence coefficient is greater than the preset fault influence coefficient threshold, it proceeds to step S424. When the fault influence coefficient is not greater than the preset fault influence coefficient threshold, it proceeds to step S425; S424 When the number of adjacent sub-regions is within the preset adjacent sub-region number range, then when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal. When the number of adjacent sub-regions is not within the preset adjacent sub-region number range, it proceeds to step S425; S425 determines the self-risk coefficient of the target forest area based on the number of adjacent sub-regions and fault-affected sub-regions in the target forest area, and combines the number of IoT devices in other regions that utilize the IoT gateway devices in different fault-affected sub-regions. When the self-risk coefficient of the target forest area does not meet the requirements, then when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal. When the self-risk coefficient of the target forest area meets the requirements, it proceeds to step S43.

[0055] The further technical solution of Embodiment 4 lies in that the method for determining the affected area in the other regions is as follows: Based on the installation distribution of the influencing devices in the other regions, it determines the number of influencing devices in the other regions. When either the number of influencing devices in the other regions or the proportion of the influencing devices in the other regions among the IoT devices in the other regions does not meet the requirements, it determines the other regions as the affected areas; When both the number of influencing devices in the other area and the proportion of the number of influencing devices in the other area among the Internet of Things devices in the other area meet the requirements: Divide the other area into multiple sub-areas using a preset area, and determine the monitoring influence coefficients of different sub-areas based on the proportion of the number of influencing devices in the sub-areas among the Internet of Things devices in the sub-areas. When there is a sub-area where the monitoring influence coefficient does not meet the requirements, then determine the other area as an influencing area; When there is no sub-area where the monitoring influence coefficient does not meet the requirements: Regard the sub-areas with monitoring influence coefficients within a preset influence coefficient range as influencing sub-areas. When either the number of influencing sub-areas in the other area or the proportion of the number of influencing sub-areas in the other area among the sub-areas in the other area does not meet the requirements, then determine the other area as an influencing area; When both the number of influencing sub-areas in the other area and the proportion of the number of influencing sub-areas in the other area among the sub-areas in the other area meet the requirements: Determine the distribution aggregation coefficient of the influencing sub-areas based on the interval distance between different influencing sub-areas and the number of influencing sub-areas. When the distribution aggregation coefficient of the influencing sub-areas in the other area does not meet the requirements, then determine the other area as an influencing area; When the distribution aggregation coefficient of the influencing sub-areas in the other area meets the requirements: Determine the monitoring influence value of the other area based on the monitoring influence coefficients of different sub-areas and in combination with the distribution aggregation coefficient of the influencing sub-areas in the other area, and determine whether the other area is an influencing area based on the monitoring influence value.

[0056] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0057] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The above are only one or more embodiments of this specification and are not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A forest fire early monitoring and early warning method, characterized in that: Specifically include: The IoT gateway device in the target forest area is used as the target gateway device, and the IoT devices in other areas using the target gateway device are used as influencing devices, and the influencing areas in other areas are determined based on the installation distribution of the influencing devices in other areas; When it is determined that the target forest area does not belong to the risk area based on the distribution of the affected area and the distribution data of the IoT devices in the affected area, proceed to the next step; Divide the target forest area into a plurality of sub-areas according to a preset area, determine the wind direction of the target forest area based on weather data of the current date, and determine the adjacent area affected when a fire occurs in the target forest area according to the wind direction; Determine the impact of different IoT devices in the affected adjacent areas on other areas, and determine the fire monitoring and early warning method for the target forest area based on the interval distance between different sub-areas and the affected adjacent areas and the distribution data of IoT gateway devices.

2. The forest fire early monitoring and early warning method according to claim 1, characterized in that: The other area is an area located in the same forest location as the target forest area.

3. The forest fire early monitoring and early warning method according to claim 1, characterized in that: The IoT device is a device used for fire risk monitoring.

4. The forest fire early monitoring and early warning method according to claim 1, characterized in that: The method for determining the influence area in the other areas is: Determine the number of influencing devices in the other areas based on the installation distribution of the influencing devices in the other areas; Whether the other area is an impact area is determined according to the proportion of the number of Internet of Things devices in the other area among the impact devices in the other area.

5. The forest fire early monitoring and early warning method according to claim 1, characterized in that: The distribution of the affected areas includes the number of affected areas and their distribution locations in the forest.

6. The forest fire early monitoring and early warning method according to claim 1, characterized in that: The distribution data of the Internet of Things devices is determined according to the installation locations of the Internet of Things devices in the impact area.

7. The forest fire early monitoring and early warning method according to claim 1, characterized in that: Determine that the target forest area does not belong to the risk area, including: Based on the distribution of the affected areas, determine the quantitative ratio of the affected areas in the forest location to the forest area in the forest location, and use it as the quantitative ratio of the affected areas; According to the distribution data of IoT devices in different impact areas, determine the proportion of the number of IoT devices in the impact area, and use it as the regional impact coefficient; Based on the proportion of the number of affected areas and the regional impact coefficients of different affected areas, the regional impact risk coefficient of the target forest area is determined, and based on the regional impact risk coefficient, it is determined whether the target forest area belongs to a risk area.

8. The forest fire early warning method according to claim 7, characterized in that: When the target forest area belongs to a risk area, when any monitoring device in the target forest area detects a fire signal, the target forest area outputs a warning signal.

9. The forest fire early monitoring and early warning method according to claim 1, characterized in that: The method for determining the fire monitoring and early warning method of the target forest area is: Determine the number of IoT devices in other sub-areas that use different IoT gateway devices that affect the adjacent areas based on the different impacts of the IoT devices in other areas, the proportion of the number in the other sub-areas, and determine the device impact area in the other area based on the proportion of the number; Based on the interval distance between the sub-region and the affected adjacent region, determine the sub-region whose interval distance is less than a preset distance threshold and take it as the adjacent sub-region; According to the distribution data of the Internet of Things gateway devices in the sub-area, the sub-areas of other areas where the Internet of Things gateway devices utilizing the sub-area exist are determined, and used as the fault-affected sub-areas. According to the number of the equipment-affected areas, the number of adjacent sub-areas and the number of fault-affected sub-areas, the fire monitoring and early warning method of the target forest area is determined.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes a forest fire early monitoring and early warning method as described in any one of claims 1-9 when running the computer program.

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