A forest fire prevention early warning method and device

Abstract

The present application discloses a forest fire warning method and device, which are used to improve the accuracy and efficiency of forest fire warning, thereby more effectively avoiding the occurrence of forest fires and saving costs. The forest fire warning method provided by the present application includes: obtaining forest information; wherein the forest information is established by a plurality of sub-images obtained by patrolling the forest by a drone in advance, the plurality of sub-images constitute a complete image of the forest, and each of the sub-images corresponds to the establishment of information of an area of ​​the forest; when a preset trigger condition is met, the drone is triggered to perform a patrol mission according to the information of at least one area in the forest fire information.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a forest fire early warning method and device. Background Art

[0002] With the development of technology, drone platforms are becoming more and more mature. From small shuttles to large long-range drones, drones are appearing more and more frequently in all areas of life, and their savings in manpower and material resources are becoming more and more prominent. Among the many forest fire prevention solutions, most of them are manual inspections of forests, using sensors installed at detection points to detect and prevent fires, or using thermal imaging carried by drones to inspect forest farms. Summary of the invention

[0003] The embodiments of the present application provide a forest fire warning method and device to improve the accuracy and efficiency of forest fire warning, thereby more effectively avoiding forest fires and saving costs.

[0004] The present invention provides a forest fire early warning method, comprising:

[0005] Acquire forest information; wherein the forest information is established by obtaining multiple sub-images obtained by flying a drone over the forest in advance, the multiple sub-images constitute a complete image of the forest, and each sub-image corresponds to information about an area of ​​the forest;

[0006] When a preset trigger condition is met, the drone is triggered to perform a patrol mission based on information of at least one area in the forest fire prevention information.

[0007] The method is used to obtain forest information established by using a plurality of sub-images obtained in advance by a drone patrolling the forest, wherein the plurality of sub-images constitute a complete image of the forest, and each sub-image corresponds to information establishing an area of ​​the forest; when a preset trigger condition is met, the drone is triggered to perform a patrol mission based on information of at least one area in the forest fire prevention information, thereby improving the accuracy and efficiency of forest fire prevention warnings, more effectively avoiding the occurrence of forest fires, and saving costs. Targeted patrols are possible without the need for large-scale patrols.

[0008] In some embodiments, the information of the region includes a risk value of the region, and the risk value is determined by one or a combination of the following parameters:

[0009] The density of vegetation in the area, the type of vegetation, and the distance between the vegetation area and water sources.

[0010] In some embodiments, the patrol mission includes:

[0011] Full range patrol missions over the forest, and / or patrol missions over high risk areas of the forest;

[0012] The high-risk area is an area where the risk value is higher than a preset threshold.

[0013] In some embodiments, the trigger condition includes one or a combination of the following conditions:

[0014] The thunderstorm ended;

[0015] The humidity in any area is lower than the preset threshold and the duration exceeds the preset threshold;

[0016] Arrived at the preset time.

[0017] In some embodiments, the method further comprises:

[0018] The forest information is updated using the information obtained during the execution of the patrol mission.

[0019] In some embodiments, different trigger conditions correspond to different patrol missions; wherein, in different patrol missions, the patrol equipment carried by the UAV and / or the patrol range of the UAV are different.

[0020] In some embodiments, the method further comprises:

[0021] If an area with a temperature higher than a preset threshold is found during the execution of the patrol mission, an alarm signal is issued.

[0022] Another embodiment of the present application provides a forest fire warning device, which includes a memory and a processor, wherein the memory is used to store program instructions, and the processor is used to call the program instructions stored in the memory, and execute any of the above methods according to the obtained program.

[0023] Furthermore, according to an embodiment, for example, a computer program product for a computer is provided, which includes software code portions, and when the product is run on the computer, these software code portions are used to perform the steps of the method defined above. The computer program product may include a computer-readable medium having the software code portions stored thereon. Furthermore, the computer program product may be directly loaded into the internal memory of the computer and / or sent via a network through at least one of an upload process, a download process, and a push process.

[0024] Another embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable the computer to execute any of the above methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of 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.

[0026] Figure 1 A flowchart of a forest fire warning method provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of a drone collecting sub-images provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of forest information (forest farm data sandbox) corresponding to the entire forest image established based on multiple sub-images provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram for determining a high-risk area provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of the triggering conditions for the patrol mission provided in the embodiment of the present application;

[0031] Figure 6 A schematic structural diagram of a forest fire warning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] 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.

[0033] The embodiments of the present application provide a forest fire warning method and device to improve the accuracy and efficiency of forest fire warning, thereby more effectively avoiding forest fires and saving costs.

[0034] Among them, the method and the device are based on the same application concept. Since the method and the device solve the problem in a similar principle, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0035] The terms "first", "second", etc. (if any) in the specification and claims of the embodiments of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] The following examples and embodiments are to be understood as illustrative examples only. Although this specification may refer to "one", "an" or "some" examples or embodiments in several places, this does not mean that each such reference relates to the same example or embodiment, nor does it mean that the feature applies only to a single example or embodiment. Individual features of different embodiments may also be combined to provide other embodiments. In addition, terms such as "including" and "comprising" should be understood as not limiting the described embodiments to only those features that have been mentioned; such examples and embodiments may also include features, structures, units, modules, etc. that are not specifically mentioned.

[0037] The system for realizing communication with terminals such as drones in the technical solution provided in the embodiment of the present application can be applicable to a variety of systems, especially 5G systems. For example, the applicable system can be a global system of mobile communication (GSM) system, a code division multiple access (CDMA) system, a wideband code division multiple access (WCDMA) general packet radio service (GPRS) system, a long term evolution (LTE) system, a LTE frequency division duplex (FDD) system, a LTE time division duplex (TDD) system, a universal mobile telecommunication system (UMTS), a world-wide interoperability for Microwave Access (WiMAX) system, a 5G system, and a 5G NR system. These various systems include terminal equipment and network equipment.

[0038] The terminal device involved in the embodiments of the present application may be a device that provides voice and / or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem, such as a drone.

[0039] The network device involved in the embodiments of the present application may be a base station, a server or other device, which is not limited in the embodiments of the present application.

[0040] The following is a detailed description of each embodiment of the present application in conjunction with the accompanying drawings. It should be noted that the display order of the embodiments of the present application only represents the order of the embodiments, and does not represent the advantages and disadvantages of the technical solutions provided by the embodiments.

[0041] The technical solution provided in the embodiment of this application involves a solution for long-term inspections by drones in a large area, combining big data and image processing to automatically calculate risk weights and plan inspection routes; periodic full-area inspections and specific inspections triggered by specific conditions. Combined with the weather conditions in the current area obtained in real time by the console, combined with big data, it automatically plans whether to inspect and what kind of drone to send, and combines previous data analysis to plan targeted inspection routes.

[0042] Fires often do not occur when drones are flown. The technical solution provided in the embodiments of the present application focuses on the prevention of potential fire hazards, and provides timely feedback and disposal when no fire occurs or a fire is discovered, thereby reducing the possibility of fire.

[0043] See also Figure 1 , a forest fire early warning method provided by an embodiment of the present application includes:

[0044] S101, obtaining forest information; wherein the forest information is established by obtaining a plurality of sub-images in advance by using a drone to patrol the forest, the plurality of sub-images forming a complete image of the forest, and each sub-image corresponding to information of an area of ​​the forest;

[0045] That is to say, the forest information described in the embodiment of the present application includes information of multiple areas, and the information of each area is determined by a sub-image obtained by flying a drone over the forest, and various information can be obtained by identifying the image. Specific information, for example: what types of vegetation are included in the area (whether it contains very green leafy plants, oily vegetation, etc.), whether it contains vegetation areas far away from water sources, the temperature and humidity of the area, the risk value of the forest area, whether there are peaks in the area, whether there is a mountain effect, etc.

[0046] S102: When a preset trigger condition is met, trigger the drone to perform a patrol mission based on information of at least one area in the forest fire prevention information.

[0047] The patrol mission can be triggered manually, that is, the user issues a command to trigger the patrol mission; or the patrol mission can be automatically triggered based on information of at least one area in the forest fire prevention information, or time and other information.

[0048] The patrol mission includes multiple contents, such as patrol equipment, patrol plan (i.e. patrol range or patrol route), processing plan, etc. Among them, the processing plan, for example, performs corresponding processing according to the parameter information obtained during the patrol process, such as updating forest information, issuing alarm signals, automatic fire extinguishing processing, etc.

[0049] The technical solution provided in the embodiment of the present application automatically plans whether to conduct an inspection and plans an inspection plan based on meteorological conditions such as the fire season with a high incidence rate, the temperature of the day and / or the next day, the humidity of the day and / or the next day, the wind speed of the day and / or the next day, and the lightning of the day.

[0050] Specifically, for example, they include:

[0051] 1. Initialize the forest data sandbox, that is, use multiple sub-images obtained by drones patrolling the forest to establish forest information:

[0052] ①. Fly the drone over the forest to acquire images, and perform image analysis to obtain corresponding data information.

[0053] Among them, Figure 2 As shown in the figure, the area viewed by the drone is defined as an independent grid, so the patrol forest farm is divided into a data sandbox consisting of many grids, such as Figure 3 As shown, each grid corresponds to a sub-image area of ​​the forest. Each sub-image is used to establish information of a region of the forest, so that the information of the regions corresponding to all sub-images constitutes the information of the entire forest.

[0054] ②. Through image recognition and other technologies, high-risk sub-image areas (i.e. high-risk grids) are marked on the forest image.

[0055] For example, the image transmission data and big data artificial intelligence can be combined to analyze the density of trees, tree types, surrounding environment, etc. in each sub-image area. Grids with high density, grids containing very green leafy plants, grids containing oily vegetation types, grids containing vegetation areas far from water sources, etc. are marked as high-risk grids. Figure 4 As shown, one or more factors such as vegetation density, very green leafy plants, oily plants, and no water source near plants can be comprehensively considered to determine whether any grid area is a high-risk area. For example, if any of the above factors meets the conditions, the risk value is increased by 1. If the total risk value of the grid reaches the preset threshold, the grid is identified as a high-risk grid.

[0056] Among them, for vegetation density: for example, using existing computer vision matlab image segmentation technology, a vegetation scatter plot in the grid is obtained for density judgment. For example, if the number of points in the vegetation scatter plot is greater than a preset threshold, it is considered to be a grid with high density, that is, marked as a high-risk grid;

[0057] For vegetation types: Model training is performed based on different plant features, such as leaf shape, trunk size and other feature points, so that the trained model can be used to identify the images in the grid to determine whether the grid includes vegetation types such as very green leaf plants and oily plants.

[0058] Whether a grid contains vegetation areas far away from water sources can be determined by, for example, performing image recognition on multiple grid images to determine the distance between the vegetation area in the grid and the nearest water source area. If the distance is greater than a preset threshold, the grid is considered to contain vegetation areas far away from water sources, and the grid is marked as a high-risk grid.

[0059] 2. Patrol flight plan:

[0060] Method 1: Manually set the patrol plan:

[0061] Manually set up scheduled patrol missions:

[0062] (1) Full-range patrol: the staff triggers the drone to patrol the entire forest farm;

[0063] (2) Intelligent patrol: This means that staff trigger drones to automatically patrol high-risk grid areas in the forest farm based on factors such as the weather conditions of the day.

[0064] Processing: After each patrol, or during the patrol, the corresponding grid data in the data sandbox can be updated according to the latest collected data, for example: what types of vegetation are included in the forest area corresponding to the grid (whether it contains very green leaf plants, oily vegetation, etc.), whether it contains vegetation areas far away from water sources, the temperature and humidity of the forest area corresponding to the grid, the risk value of the forest area corresponding to the grid, etc.

[0065] Method 2: Automatically trigger the patrol plan:

[0066] For example, by obtaining weather conditions from meteorological platforms and combining them with solar terms and seasons, patrol plans can be automatically planned and executed without manual triggering.

[0067] Then the patrol planning is triggered when, for example, a fire hazard condition is constituted (i.e., an event trigger condition is met) and / or when a preset patrol plan date is reached (i.e., a time trigger condition is met).

[0068] In some embodiments, based on the weather conditions of the day, air humidity, etc., combined with big data analysis of previous wildfire occurrences, it is possible to calculate whether the risk of each current grid has risen above a threshold, thereby triggering a patrol.

[0069] For specific patrol triggering conditions, patrol equipment, patrol plans and corresponding follow-up processing, see Figure 5 , for example:

[0070] (1) End of thunderstorm weather:

[0071] Triggering patrol conditions: According to the current weather forecast information, after thunderstorms occur in the control area, automatic patrols are carried out on the grids with mountain peaks;

[0072] Patrol equipment: The drone can carry image sensors (referred to as image transmission) and thermal imaging devices; further, it can also carry fire extinguishing bombs;

[0073] Patrol plan: Patrol the grid area where there are mountains;

[0074] Processing: Based on the data obtained by the drone during the patrol process, the grid data corresponding to the grid area patrolled in the data sandbox is updated. If obvious high-temperature spots or open flames are found after the update, an alarm is triggered and the drone is handed over to the staff.

[0075] (2) Dry and low temperature:

[0076] Triggering patrol conditions: In autumn and winter, continuous dryness (humidity is lower than the preset threshold), which has a significant impact on the forest environment, a large number of fallen leaves pose a fire risk, long-term dryness (dryness duration exceeds the preset threshold) or short-term high temperature (i.e. the temperature is higher than the preset threshold, but the duration does not exceed the preset threshold), then the patrol is triggered;

[0077] Patrol equipment: The drone is equipped with image transmission and thermal imaging devices;

[0078] Patrol plan: patrol the grids where mountain effect exists; the mountain effect mainly refers to the thermal effect of the raised land, that is, there may be long-term dryness (dryness duration exceeds the preset threshold) and short-term high temperature (temperature is higher than the preset threshold) areas, which need to be patrolled.

[0079] Processing: Update the collected data and risk weights corresponding to the grids that have completed the patrol in the data sandbox. For example, if the data obtained during the drone patrol shows that there are a large number of fallen leaves, the risk weight of the grid will be increased. If there are obvious high-temperature spots or open flames, an alarm will be triggered.

[0080] (3) High temperature and dryness:

[0081] Trigger patrol conditions: continuous high temperature and low humidity, that is, the temperature is higher than the preset threshold, and the humidity is lower than the preset threshold, and the duration exceeds the preset threshold, then automatic patrol is performed;

[0082] Patrol equipment: The drone should be equipped with image transmission, thermal imaging and other equipment, and can also carry fire extinguishing bombs;

[0083] Patrol plan: Patrol the current high-risk grids and grids with mountain effects;

[0084] Processing: Update the collected data and risk weights in the table in the data sandbox. If there are obvious high-temperature points or open flames, an alarm will be triggered.

[0085] (4) In addition, during special holidays, when the risk of fire is high, full-range patrols can be conducted.

[0086] In some embodiments, if the above conditions for triggering patrol are met, but the conditions for take-off are not met (for example, strong winds prevent the drone from flying normally), an alarm is sounded to inform the supervisor to take corresponding measures.

[0087] In summary, in the technical solution provided in the embodiment of the present application, the patrol route can be calculated based on the fire hazard of the day and the grid risk weight. When the fire hazard value is low, patrols can be conducted only for high-weight grids. When the fire risk is high, such as after a thunderstorm, in the summer when the temperature is high, and in the dry autumn and winter, large-scale patrols can be conducted. In addition, data such as the risk weight of the grid is updated based on the data obtained from the patrol. If the grid risk weight increases, it means that the fire hazard in the grid area is increasing, and an alarm is issued to notify the ranger to go and deal with it. If an obvious high temperature point or fire point is found during the patrol, an alarm is issued to notify the ranger to go and deal with it, and the image information is transmitted back.

[0088] In summary, the technical solution provided by the embodiment of the present application constructs forest area weighted map data, focuses on possible fire points for prevention, and automatically allocates and triggers different inspection plans through algorithms to improve the efficiency and accuracy of fire prevention warnings and minimize costs. It can achieve early prevention of forest fires and automatic patrols during high-incidence periods of fires.

[0089] The following is an introduction to the equipment or device provided in the embodiments of the present application, in which the explanations or examples of technical features that are the same or corresponding to those described in the above method will not be repeated later.

[0090] See also Figure 6 The embodiment of the present application provides a forest fire warning device (which may be a device independent of a drone or the drone itself), including:

[0091] The processor 600 is used to read the program in the memory 620 and execute the following process:

[0092] Acquire forest information; wherein the forest information is established by obtaining multiple sub-images obtained by flying a drone over the forest in advance, the multiple sub-images constitute a complete image of the forest, and each sub-image corresponds to information about an area of ​​the forest;

[0093] When a preset trigger condition is met, the drone is triggered to perform a patrol mission based on information of at least one area in the forest fire prevention information.

[0094] In some embodiments, the information of the region includes a risk value of the region, and the risk value is determined by one or a combination of the following parameters:

[0095] The density of vegetation in the area, the type of vegetation, and the distance between the vegetation area and water sources.

[0096] In some embodiments, the patrol mission includes:

[0097] Full range patrol missions over the forest, and / or patrol missions over high risk areas of the forest;

[0098] The high-risk area is an area where the risk value is higher than a preset threshold.

[0099] In some embodiments, the trigger condition includes one or a combination of the following conditions:

[0100] The thunderstorm ended;

[0101] If the humidity in any area is lower than the preset threshold and lasts longer than the preset threshold, i.e. long-term dryness, regardless of low or high temperatures, patrols can be triggered;

[0102] Arrival at a preset time, for example, can be a periodically set patrol time, or a holiday period, etc.

[0103] In some embodiments, the processor 600 is further configured to read a program in the memory 620 and execute the following process:

[0104] The forest information is updated using the information obtained during the execution of the patrol mission.

[0105] In some embodiments, different trigger conditions correspond to different patrol missions; wherein, in different patrol missions, the patrol equipment carried by the drone (such as image transmission, thermal imaging, fire extinguishing bombs, etc.), and / or the patrol range of the drone are different.

[0106] In some embodiments, the processor 600 is further configured to read a program in the memory 620 and execute the following process:

[0107] If an area with a temperature higher than a preset threshold (such as a high temperature point and / or a fire point) is found during the execution of the patrol mission, an alarm signal is issued.

[0108] In some embodiments, a forest fire warning device provided by an embodiment of the present application also includes a transceiver 610 for receiving and sending data under the control of a processor 600 .

[0109] Among them, Figure 6 In the embodiment of the present invention, the bus architecture may include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 600 and memory represented by memory 620. The bus architecture may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are not further described herein. The bus interface provides an interface. The transceiver 610 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium.

[0110] In some embodiments, a user interface 630 is also included. The user interface 630 may be an interface capable of externally or internally connecting required devices. The connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0111] The processor 600 is responsible for managing the bus architecture and general processing, and the memory 620 can store data used by the processor 600 when performing operations.

[0112] In some embodiments, the processor 600 may be a CPU (central processing unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array) or a CPLD (Complex Programmable Logic Device).

[0113] The embodiment of the present application provides a computing device, which may be a desktop computer, a portable computer, a smart phone, a tablet computer, a personal digital assistant (PDA), etc. The computing device may include a central processing unit (CPU), a memory, an input / output device, etc. The input device may include a keyboard, a mouse, a touch screen, etc. The output device may include a display device, such as a liquid crystal display (LCD), a cathode ray tube (CRT), etc.

[0114] The memory may include a read-only memory (ROM) and a random access memory (RAM), and provides the processor with program instructions and data stored in the memory. In an embodiment of the present application, the memory may be used to store the program of any of the methods provided in the embodiments of the present application.

[0115] The processor calls the program instructions stored in the memory, and the processor is used to execute any of the methods provided in the embodiments of the present application according to the obtained program instructions.

[0116] The present application embodiment also provides a computer program product or computer program, which includes a computer instruction, which is stored in a computer-readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction so that the computer device executes any of the methods described in the above embodiments. The program product can use any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0117] The embodiment of the present application provides a computer-readable storage medium for storing computer program instructions used by the apparatus provided in the embodiment of the present application, which includes a program for executing any of the methods provided in the embodiment of the present application. The computer-readable storage medium may be a non-transitory computer-readable medium.

[0118] The computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0119] It should be understood that:

[0120] The access technology through which entities in the communication network transmit traffic can be any suitable current or future technology, such as WLAN (Wireless Local Access Network), WiMAX (Worldwide Interoperability for Microwave Access), LTE, LTE-A, 5G, Bluetooth, infrared, etc.; in addition, the embodiments can also apply wired technology, for example, IP-based access technology, such as a wired network or a fixed line.

[0121] Embodiments suitable for being implemented as software code or a portion thereof and running using a processor or processing functionality are independent of the software code and may be specified using any known or future developed programming language, such as a high-level programming language such as objective-C, C, C++, C#, Java, Python, Javascript, other scripting languages, etc., or a low-level programming language such as machine language or assembler.

[0122] The implementation of the embodiments is hardware independent and may be implemented using any known or future developed hardware technology or any mixture thereof, such as a microprocessor or CPU (central processing unit), MOS (metal oxide semiconductor), CMOS (complementary MOS), BiMOS (bipolar MOS), BiCMOS (bipolar CMOS), ECL (emitter coupled logic) and / or TTL (transistor-transistor logic).

[0123] Embodiments may be implemented as separate devices, apparatuses, units, components, or functions, or in a distributed manner, for example, one or more processors or processing functions may be used or shared in a process, or one or more processing segments or processing portions may be used and shared in a process, wherein one physical processor or more than one physical processor may be used to implement one or more processing portions dedicated to a specific process as described.

[0124] The device may be implemented by a semiconductor chip, a chipset, or a (hardware) module including such a chip or chipset.

[0125] The embodiments may also be implemented as any combination of hardware and software, such as ASIC (Application Specific IC (Integrated Circuit)) components, FPGA (Field Programmable Gate Array) or CPLD (Complex Programmable Logic Device) components or DSP (Digital Signal Processor) components.

[0126] The embodiments may also be implemented as a computer program product including a computer usable medium having computer readable program code embodied therein, the computer readable program code being adapted to perform the processes as described in the embodiments, wherein the computer usable medium may be a non-transitory medium.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A forest fire early warning method, characterized in that: The method includes: Acquire forest information; wherein the forest information is established by obtaining multiple sub-images obtained by flying a drone over the forest in advance, the multiple sub-images constitute a complete image of the forest, and each sub-image corresponds to information about an area of ​​the forest; When a preset trigger condition is met, triggering the drone to perform a patrol mission according to information of at least one area in the forest information; The information of the area includes the risk value of the area, and the risk value is determined by one or a combination of the following parameters: The density of vegetation in the area, the type of vegetation, and the distance between the vegetation area and water sources; The triggering conditions include one or a combination of the following conditions: The thunderstorm ended; The humidity in any area is lower than the preset threshold and the duration exceeds the preset threshold; Arrived at the preset time.

2. The method according to claim 1, characterized in that The patrol missions include: patrol missions over the entire forest area and / or patrol missions over high-risk areas of the forest; The high-risk area is an area where the risk value is higher than a preset threshold.

3. The method according to claim 1, characterized in that The method further comprises: The forest information is updated using the information obtained during the execution of the patrol mission.

4. The method according to claim 1, characterized in that: Different trigger conditions correspond to different patrol missions; in different patrol missions, the patrol equipment carried by the UAV and / or the patrol range of the UAV are different.

5. The method according to claim 1, characterized in that The method further comprises: If an area with a temperature higher than a preset threshold is found during the execution of the patrol mission, an alarm signal is issued.

6. A forest fire early warning device, characterized in that: include: A memory for storing program instructions; A processor, configured to call the program instructions stored in the memory, and execute the method according to any one of claims 1 to 5 according to the obtained program.

7. A computer program product for a computer, characterized in that The invention comprises a software code portion for executing the method according to any one of claims 1 to 5 when the product is run on the computer.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable the computer to execute the method according to any one of claims 1 to 5.

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