Intelligent monitoring and early warning method and system for forest fire prevention

Abstract

The invention is suitable for the technical field of forest fire prevention and early warning, and provides an intelligent monitoring and early warning method and system for forest fire prevention. The method comprises the following steps: dividing a plurality of monitoring sub-regions, and calculating risk indexes of the plurality of sub-regions; performing hierarchical analysis, selecting a plurality of risk sub-regions, and determining corresponding risk levels; matching a plurality of monitoring densities and monitoring frequencies; performing unmanned aerial vehicle flight and monitoring shooting control in the plurality of monitoring sub-areas; and according to RGB three-color channels, calculation and comparison of fire judgment are carried out. A plurality of monitoring sub-regions can be divided for monitoring, risk indexes of the sub-regions are calculated, a plurality of risk sub-regions are selected, corresponding risk levels are determined, different monitoring densities and monitoring frequencies are matched for unmanned aerial vehicle flight and monitoring, and then calculation and comparison of fire judgment are carried out according to RGB three-color channels. The forest fire prevention monitoring blind area influence is effectively reduced, the monitoring comprehensiveness is improved, the accuracy is high, and the requirements of intelligence and low cost can be met.

Description

Technical Field

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

[0002] Forest fire warning is the process of assessing the risk of forest fire based on meteorological conditions, ground conditions and other relevant factors, and issuing alarm information accordingly. It aims to raise people's awareness of fire prevention and take necessary preventive measures to reduce the occurrence and spread of forest fires.

[0003] In the existing technology, forest fire monitoring mainly relies on satellite remote sensing and ground patrols. Although it can achieve forest fire monitoring and early warning to a certain extent, it has the following defects: (1) Due to the obstruction of trees, towers or other buildings in the forest, forest fire monitoring has blind spots, so the monitoring is not comprehensive enough and the accuracy is not high; (2) In the case of complex forest terrain and large area, ground patrols are difficult to play a role and cannot be deployed in the entire forest area. In addition, ground patrols greatly increase the cost of forest fire monitoring and early warning, which does not meet the existing intelligent and low-cost requirements. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide an intelligent monitoring and early warning method and system for forest fire prevention, aiming to solve the technical problems existing in the prior art mentioned in the background technology.

[0005] The embodiment of the present invention is implemented as follows: An intelligent monitoring and early warning method for forest fire prevention, the method specifically comprising the following steps: Determine a forest monitoring area, divide it into a plurality of monitoring sub-areas, collect fire risk factor data of the plurality of monitoring sub-areas, and calculate sub-area risk indexes of the plurality of monitoring sub-areas; Performing hierarchical analysis on the risk indexes of the plurality of sub-regions, selecting a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determining corresponding risk levels; According to the multiple risk levels, multiple monitoring densities and surveillance frequencies are matched, and according to the multiple monitoring densities, flight monitoring planning is performed on the multiple monitoring sub-areas to generate multiple flight monitoring routes; According to the plurality of flight monitoring routes and corresponding monitoring frequencies, the drone flight and monitoring control are performed in the plurality of monitoring sub-areas, and a plurality of fire prevention monitoring images are acquired that are directly or collaboratively transmitted; According to the RGB three-color channels, a plurality of fire prevention monitoring images are calculated and compared for fire judgment. When it is determined that there is a forest fire, the location of the forest fire is determined and a fire alarm is issued.

[0006] As a further limitation of the technical solution of the embodiment of the present invention, the determining of the forest monitoring area, dividing the forest monitoring area into a plurality of monitoring sub-areas, collecting the fire risk factor data of the plurality of monitoring sub-areas, and calculating the sub-area risk index of the plurality of monitoring sub-areas specifically include the following steps: Identify forest monitoring areas that require forest fire monitoring; Divide the forest monitoring area into a plurality of monitoring sub-areas according to preset fine division requirements; Collecting fire risk factor data of a plurality of monitoring sub-areas; According to the plurality of fire risk factor data, sub-region risk indexes of the plurality of monitoring sub-regions are calculated.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the sub-region risk index of the multiple monitoring sub-regions is: ; in, Representative monitoring sub-areas, For the Sub-region risk index for each monitored sub-region, For the The time-limited temperature of each monitoring sub-area, is the first temperature conversion coefficient, is the second temperature conversion coefficient, is the maximum temperature adjustment coefficient, is the humidity conversion factor, For the The time-limited humidity of each monitoring sub-area, is the minimum humidity adjustment factor, is the wind speed conversion coefficient, For the The time-limited wind speed in each monitoring sub-area is For the The number of consecutive days without rainfall in each monitoring sub-area.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the hierarchical analysis of the risk indexes of the plurality of sub-regions, selecting a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determining the corresponding risk levels specifically include the following steps: Based on a preset risk threshold, comparing and analyzing the risk indexes of the plurality of sub-regions, and selecting a plurality of target risk indexes; Selecting multiple risk sub-areas from multiple monitoring sub-areas according to multiple target risk indexes; Based on the preset standard grading data, the plurality of target risk indices are graded and matched, and the graded matching results are recorded; According to the hierarchical matching result, the risk levels corresponding to the plurality of risk sub-areas are determined.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, matching multiple monitoring densities and surveillance frequencies according to multiple risk levels, performing flight monitoring planning for multiple monitoring sub-areas according to multiple monitoring densities, and generating multiple flight monitoring routes specifically include the following steps: According to the multiple risk levels, multiple monitoring densities and surveillance frequencies are matched; Obtaining sub-area electronic maps of a plurality of the monitoring sub-areas; Based on the electronic maps of the multiple sub-areas and according to the multiple monitoring densities, flight monitoring planning is performed on the multiple monitoring sub-areas to generate multiple flight monitoring routes.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the drone flight and monitoring control are performed in multiple monitoring sub-areas according to the multiple flight monitoring routes and the corresponding monitoring frequencies, and the acquisition of multiple fire prevention monitoring images transmitted directly or collaboratively specifically includes the following steps: Performing flight control of the UAV in multiple monitoring sub-areas according to the multiple flight monitoring routes; According to the plurality of monitoring frequencies, monitoring and controlling the drones in the plurality of monitoring sub-areas; Determine the direct transmission state and the cooperative transmission state according to the signal strength of the drone; In the direct transmission state, the fire prevention monitoring images directly transmitted by the drone are obtained; In the collaborative transmission state, the target UAV and multiple collaborative UAVs are determined, and the fire prevention monitoring images of the target UAV are collaboratively transmitted through the multiple collaborative UAVs.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the calculation and comparison of the fire judgment of the plurality of fire prevention monitoring images according to the RGB three-color channels, determining the location of the forest fire when it is determined that there is a forest fire, and issuing a fire alarm specifically include the following steps: According to the RGB three-color channels, a plurality of fire prevention monitoring images are compared and calculated for fire judgment to obtain a calculation and comparison result; According to the calculation and comparison results, determining whether there is a forest fire; When there is a forest fire, determine the location of the forest fire; Based on the forest fire location, a positioning alarm signal is generated and a fire alarm is issued.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the formula for calculating and comparing the fire judgment of the plurality of fire prevention monitoring images is: ; in, is the pixel coordinates in the fire monitoring image, for The R value of the pixel, for The G value of the pixel, for The B value of the pixel, It is the accumulation of R values ​​of all pixels in the fire monitoring image.

[0013] An intelligent monitoring and early warning system for forest fire prevention, the system includes a risk index calculation module, a risk classification analysis module, a flight monitoring planning module, a flight monitoring control module and a fire judgment alarm module, wherein: A risk index calculation module is used to determine the forest monitoring area, divide it into multiple monitoring sub-areas, collect fire risk factor data of multiple monitoring sub-areas, and calculate the sub-area risk index of multiple monitoring sub-areas; A risk classification analysis module, used to perform classification analysis on the risk indexes of the plurality of sub-regions, select a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determine the corresponding risk levels; A flight monitoring planning module is used to match multiple monitoring densities and surveillance frequencies according to multiple risk levels, perform flight monitoring planning for multiple monitoring sub-areas according to multiple monitoring densities, and generate multiple flight monitoring routes; A flight monitoring control module is used to perform drone flight and monitoring control in multiple monitoring sub-areas according to multiple flight monitoring routes and corresponding monitoring frequencies, and obtain multiple fire prevention monitoring images transmitted directly or collaboratively; The fire judgment alarm module is used to calculate and compare the fire judgment of multiple fire prevention monitoring images according to the RGB three-color channels. When it is determined that there is a forest fire, the location of the forest fire is determined and a fire alarm is issued.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the risk index calculation module specifically includes: An area determination unit is used to determine the forest monitoring area that needs to be monitored for forest fire prevention; A region division unit, used to divide the forest monitoring area into a plurality of monitoring sub-areas according to a preset fine division requirement; A data collection unit, used for collecting fire risk factor data of a plurality of the monitoring sub-areas; The risk index calculation unit is used to calculate the sub-area risk indexes of multiple monitoring sub-areas according to the multiple fire risk factor data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention divides multiple monitoring sub-areas, calculates the risk indexes of multiple sub-areas; performs hierarchical analysis, selects multiple risk sub-areas, and determines the corresponding risk levels; matches multiple monitoring densities and monitoring frequencies; performs drone flight and monitoring control in multiple monitoring sub-areas; and performs calculation and comparison of fire judgment according to the RGB three-color channels. It can divide multiple monitoring sub-areas for monitoring, calculate the risk indexes of multiple sub-areas, select multiple risk sub-areas, and determine the corresponding risk levels, match different monitoring densities and monitoring frequencies for drone flight and monitoring, and then perform calculation and comparison of fire judgment according to the RGB three-color channels, which effectively reduces the blind spot impact of forest fire monitoring, improves the comprehensiveness of monitoring, and has high accuracy, which can meet the requirements of intelligence and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of an intelligent monitoring and early warning method for forest fire prevention provided by an embodiment of the present invention is shown; Figure 2 A flowchart of monitoring sub-area division analysis in the method provided by an embodiment of the present invention is shown; Figure 3 A flow chart showing risk sub-region selection and classification in the method provided by an embodiment of the present invention is shown; Figure 4 A flowchart of performing flight monitoring planning in a method provided by an embodiment of the present invention is shown; Figure 5 A flowchart of drone flight and surveillance control in the method provided by an embodiment of the present invention is shown; Figure 6 A flowchart showing the calculation and comparison of fire judgment in the method provided by an embodiment of the present invention is shown; Figure 7 The application architecture diagram of the intelligent monitoring and early warning system for forest fire prevention provided by an embodiment of the present invention is shown; Figure 8 It shows a structural block diagram of a risk index calculation module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] It is understandable that in the existing technology, forest fire monitoring mainly relies on satellite remote sensing and ground patrols. Although it can achieve forest fire monitoring and early warning to a certain extent, it has the following defects: (1) Due to the obstruction of trees, towers or other buildings in the forest, forest fire monitoring has blind spots, so the monitoring is not comprehensive enough and the accuracy is not high; (2) In the case of complex forest terrain and large area, ground patrols are difficult to play a role and cannot be deployed in the entire forest area. In addition, ground patrols greatly increase the cost of forest fire monitoring and early warning, which does not meet the existing intelligent and low-cost requirements.

[0019] To solve the above problems, an embodiment of the present invention discloses an intelligent monitoring and early warning method and system for forest fire prevention, which determines a forest monitoring area, divides it into multiple monitoring sub-areas, collects fire risk factor data of multiple monitoring sub-areas, and calculates sub-area risk indexes of multiple monitoring sub-areas; performs hierarchical analysis on the risk indexes of multiple sub-areas, selects multiple risk sub-areas from multiple monitoring sub-areas, and determines corresponding risk levels; matches multiple monitoring densities and monitoring frequencies according to multiple risk levels, performs flight monitoring planning for multiple monitoring sub-areas according to multiple monitoring densities, and generates multiple flight monitoring routes; performs drone flight and monitoring control in multiple monitoring sub-areas according to multiple flight monitoring routes and corresponding monitoring frequencies, and obtains multiple fire prevention monitoring images that are directly or collaboratively transmitted; performs calculation and comparison of fire judgment on multiple fire prevention monitoring images according to RGB three-color channels, and when it is determined that there is a forest fire, determines the location of the forest fire and issues a fire alarm. It can divide multiple monitoring sub-areas for monitoring, calculate risk indexes of multiple sub-areas, select multiple risk sub-areas, and determine the corresponding risk levels. It can match different monitoring densities and surveillance frequencies for drone flight and surveillance, and then perform calculations and comparisons for fire judgment according to the RGB three-color channels. It effectively reduces the blind spot impact of forest fire monitoring, improves the comprehensiveness of monitoring, and has high accuracy, which can meet the requirements of intelligence and low cost.

[0020] Specifically, Figure 1 A flow chart of an intelligent monitoring and early warning method for forest fire prevention provided by an embodiment of the present invention is shown.

[0021] In a preferred embodiment of the present invention, an intelligent monitoring and early warning method for forest fire prevention comprises the following steps: Step S101, determine a forest monitoring area, divide it into multiple monitoring sub-areas, collect fire risk factor data of the multiple monitoring sub-areas, and calculate the sub-area risk indexes of the multiple monitoring sub-areas.

[0022] In an embodiment of the present invention, a forest monitoring area that needs to be monitored for forest fire prevention is determined, and the forest monitoring area is divided into a plurality of monitoring sub-areas according to a preset fine division requirement, and then the fire risk factor data including temperature, humidity, wind speed, rainfall, etc. in the plurality of monitoring sub-areas are obtained through weather forecasts, on-site collection, etc., and then the sub-area risk indexes of the plurality of monitoring sub-areas are calculated according to the plurality of fire risk factor data. Specifically, the calculation formula of the sub-area risk indexes of the plurality of monitoring sub-areas is: ; in, Representative monitoring sub-areas, For the Sub-region risk index for each monitored sub-region, For the The time-limited temperature of each monitoring sub-area, is the first temperature conversion coefficient, is the second temperature conversion coefficient, is the maximum temperature adjustment coefficient, is the humidity conversion factor, For the The time-limited humidity of each monitoring sub-area, is the minimum humidity adjustment factor, is the wind speed conversion coefficient, For the The time-limited wind speed in each monitoring sub-area is For the The number of consecutive days without rainfall in each monitoring sub-area.

[0023] It is understandable that the forest monitoring area can be divided into multiple monitoring sub-areas according to factors such as ecological functions, biodiversity, vegetation types, topography, soil types, and the impact of human activities. The specific division factors that need to be considered can be pre-organized to generate detailed division requirements, so that the forest monitoring area can be divided into multiple monitoring sub-areas directly according to the detailed division requirements.

[0024] It can be understood that the time-limited temperature can be the average temperature of 7 days; the time-limited humidity can be the average humidity of 7 days; and the time-limited wind speed can be the average wind speed of 7 days.

[0025] Specifically, Figure 2 A flow chart of monitoring sub-area division analysis in the method provided by an embodiment of the present invention is shown.

[0026] Among them, in the preferred embodiment provided by the present invention, the determining of the forest monitoring area, dividing into a plurality of monitoring sub-areas, collecting the fire risk factor data of the plurality of monitoring sub-areas, and calculating the sub-area risk index of the plurality of monitoring sub-areas specifically include the following steps: Step S1011: determine the forest monitoring area that needs to be monitored for forest fire prevention.

[0027] Step S1012: Divide the forest monitoring area into multiple monitoring sub-areas according to the preset fine division requirements.

[0028] Step S1013: Collect fire risk factor data of multiple monitoring sub-areas.

[0029] Step S1014: Calculate sub-region risk indexes of multiple monitoring sub-regions according to the multiple fire risk factor data.

[0030] Furthermore, the intelligent monitoring and early warning method for forest fire prevention also includes the following steps: Step S102: performing hierarchical analysis on the risk indexes of the plurality of sub-regions, selecting a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determining corresponding risk levels.

[0031] In an embodiment of the present invention, based on a preset risk threshold, a comparative analysis is performed on multiple sub-region risk indexes, and the sub-region risk index greater than the risk threshold is marked as a target risk index, so that multiple target risk indices can be selected, and then the monitoring sub-regions corresponding to the multiple target risk indices are marked as risk sub-regions, so that multiple risk sub-regions can be selected from the multiple monitoring sub-regions, and then based on preset standard grading data, the multiple target risk indices are graded and matched, the graded matching results are recorded, and then according to the graded matching results, the risk levels corresponding to the multiple risk sub-regions are determined.

[0032] It can be understood that the risk levels may include risk level 1, risk level 2 and risk level 3. The higher the risk level, the greater the corresponding target risk index, and the higher the probability of fire occurrence.

[0033] Specifically, Figure 3 A flow chart of risk sub-area selection and classification in the method provided by an embodiment of the present invention is shown.

[0034] Among them, in the preferred embodiment provided by the present invention, the hierarchical analysis of the risk indexes of the plurality of sub-regions, selecting a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determining the corresponding risk levels specifically include the following steps: Step S1021: Based on a preset risk threshold, compare and analyze the risk indexes of the plurality of sub-regions, and select a plurality of target risk indexes.

[0035] Step S1022: Select multiple risk sub-areas from multiple monitoring sub-areas according to multiple target risk indices.

[0036] Step S1023: Based on the preset standard grading data, the plurality of target risk indices are graded and matched, and the graded matching results are recorded.

[0037] Step S1024: Determine the risk levels corresponding to the multiple risk sub-areas according to the hierarchical matching result.

[0038] Furthermore, the intelligent monitoring and early warning method for forest fire prevention also includes the following steps: Step S103: Match multiple monitoring densities and surveillance frequencies according to the multiple risk levels, perform flight monitoring planning for the multiple monitoring sub-areas according to the multiple monitoring densities, and generate multiple flight monitoring routes.

[0039] In an embodiment of the present invention, according to multiple risk levels, multiple corresponding monitoring densities and surveillance frequencies are matched, and then sub-area electronic maps of multiple monitoring sub-areas are obtained. Then, in the multiple sub-area electronic maps, different flight monitoring plans are performed on the multiple monitoring sub-areas according to the multiple monitoring densities, and multiple flight monitoring routes are generated.

[0040] It is understandable that different risk levels correspond to different monitoring densities and surveillance frequencies. Specifically, the higher the risk level, the greater the monitoring density and the higher the surveillance frequency.

[0041] Specifically, Figure 4 A flow chart of flight monitoring planning in the method provided by an embodiment of the present invention is shown.

[0042] Among them, in the preferred embodiment provided by the present invention, the matching of multiple monitoring densities and surveillance frequencies according to multiple risk levels, performing flight monitoring planning for multiple monitoring sub-areas according to multiple monitoring densities, and generating multiple flight monitoring routes specifically include the following steps: Step S1031: Match multiple monitoring densities and surveillance frequencies according to the multiple risk levels.

[0043] Step S1032: Obtain sub-area electronic maps of the plurality of monitoring sub-areas.

[0044] Step S1033: Based on the electronic maps of the multiple sub-areas and according to the multiple monitoring densities, flight monitoring planning is performed on the multiple monitoring sub-areas to generate multiple flight monitoring routes.

[0045] Furthermore, the intelligent monitoring and early warning method for forest fire prevention also includes the following steps: Step S104: according to the plurality of flight monitoring routes and corresponding monitoring frequencies, drone flight and monitoring control are performed in the plurality of monitoring sub-areas to obtain a plurality of fire prevention monitoring images transmitted directly or collaboratively.

[0046] In an embodiment of the present invention, different flight controls are performed on drones in multiple monitoring sub-areas according to multiple flight monitoring routes, and different monitoring controls are performed on drones in multiple monitoring sub-areas according to multiple monitoring frequencies. In addition, according to the signal strength of drones in different monitoring sub-areas, a direct transmission state and a collaborative transmission state are determined, and different signal transmissions are performed. Specifically, in the direct transmission state, fire prevention monitoring images directly transmitted by the drones are acquired; in the collaborative transmission state, drones whose signal strength is not greater than a preset signal threshold are marked as target drones, and one or more collaborative drones whose signal strength is greater than the signal threshold between the target drone and the signal receiving position are selected. The target drone can transmit the fire prevention monitoring image to the nearest collaborative drone, which can directly perform collaborative transmission of the fire prevention monitoring image, or relay the fire prevention monitoring image between multiple collaborative drones.

[0047] It can be understood that by comparing the signal strength with a preset signal threshold, when the signal strength is greater than the signal threshold, it is determined to be a direct transmission state; when the signal strength is not greater than the signal threshold, it is determined to be a collaborative transmission state.

[0048] Specifically, Figure 5 A flow chart of drone flight and surveillance control in the method provided by an embodiment of the present invention is shown.

[0049] Among them, in the preferred embodiment provided by the present invention, the drone flight and monitoring control are performed in multiple monitoring sub-areas according to the multiple flight monitoring routes and the corresponding monitoring frequencies, and the acquisition of multiple fire prevention monitoring images transmitted directly or collaboratively specifically includes the following steps: Step S1041: perform UAV flight control in multiple monitoring sub-areas according to the multiple flight monitoring routes.

[0050] Step S1042: Perform monitoring and filming control on the drones in the multiple monitoring sub-areas according to the multiple monitoring and filming frequencies.

[0051] Step S1043: Determine the direct transmission state and the cooperative transmission state according to the signal strength of the drone.

[0052] Step S1044: in the direct transmission state, obtain the fire prevention monitoring image directly transmitted by the drone.

[0053] Step S1045: In the collaborative transmission state, a target UAV and multiple collaborative UAVs are determined, and the fire prevention monitoring images of the target UAV are collaboratively transmitted through the multiple collaborative UAVs.

[0054] Furthermore, the intelligent monitoring and early warning method for forest fire prevention also includes the following steps: Step S105: perform calculation and comparison of the fire judgment on the plurality of fire prevention monitoring images according to the RGB three-color channels, and when it is determined that there is a forest fire, determine the location of the forest fire and issue a fire alarm.

[0055] In an embodiment of the present invention, a calculation comparison of fire judgment is performed on multiple fire prevention monitoring images according to the RGB three-color channels, and a calculation comparison result is obtained. Then, according to the calculation comparison result, it is judged whether there is a forest fire, and when it is determined that there is a forest fire, the location of the forest fire is determined according to the image positioning information of the corresponding fire prevention monitoring image, and then based on the forest fire location, a positioning alarm signal is generated, and a fire alarm is performed to achieve accurate and timely fire monitoring and early warning. Specifically, the formula for calculating and comparing fire judgment on multiple fire prevention monitoring images is: ; in, is the pixel coordinates in the fire monitoring image, for The R value of the pixel, for The G value of the pixel, for The B value of the pixel, It is the accumulation of R values ​​of all pixels in the fire monitoring image.

[0056] It is understandable that, while satisfying and , it indicates that there are fire pixels in the corresponding fire prevention monitoring image. At this time, it is determined that there is a forest fire.

[0057] Specifically, Figure 6 The flowchart of the calculation and comparison of fire judgment in the method provided by the embodiment of the present invention is shown.

[0058] Among them, in the preferred embodiment provided by the present invention, the calculation and comparison of the fire judgment of the plurality of fire prevention monitoring images according to the RGB three-color channels, when it is determined that there is a forest fire, the location of the forest fire is determined, and the fire alarm is specifically provided by the following steps: Step S1051: perform calculation and comparison of the fire judgment on the plurality of fire prevention monitoring images according to the RGB three-color channels to obtain calculation and comparison results.

[0059] Step S1052: Determine whether there is a forest fire based on the calculation and comparison results.

[0060] Step S1053: When there is a forest fire, determine the location of the forest fire.

[0061] Step S1054: Based on the forest fire location, a positioning alarm signal is generated, and a fire alarm is issued.

[0062] Furthermore, Figure 7 The application architecture diagram of the intelligent monitoring and early warning system for forest fire prevention provided by an embodiment of the present invention is shown.

[0063] Among them, in another preferred embodiment provided by the present invention, an intelligent monitoring and early warning system for forest fire prevention includes: The risk index calculation module 101 is used to determine the forest monitoring area, divide it into multiple monitoring sub-areas, collect fire risk factor data of multiple monitoring sub-areas, and calculate the sub-area risk indexes of multiple monitoring sub-areas.

[0064] In the embodiment of the present invention, the risk index calculation module 101 determines the forest monitoring area that needs to be monitored for forest fire prevention, divides the forest monitoring area into multiple monitoring sub-areas according to the preset fine division requirements, and then obtains the fire risk factor data including temperature, humidity, wind speed, rainfall, etc. in the multiple monitoring sub-areas through weather forecasts, on-site collection, etc., and then calculates the sub-area risk indexes of the multiple monitoring sub-areas according to the multiple fire risk factor data. Specifically, the calculation formula of the sub-area risk indexes of the multiple monitoring sub-areas is: ; in, Representative monitoring sub-areas, For the Sub-region risk index for each monitored sub-region, For the The time-limited temperature of each monitoring sub-area, is the first temperature conversion coefficient, is the second temperature conversion coefficient, is the maximum temperature adjustment coefficient, is the humidity conversion factor, For the The time-limited humidity of each monitoring sub-area, is the minimum humidity adjustment factor, is the wind speed conversion coefficient, For the The time-limited wind speed in each monitoring sub-area is For the The number of consecutive days without rainfall in each monitoring sub-area.

[0065] Specifically, Figure 8 It shows a structural block diagram of the risk index calculation module 101 in the system provided by the embodiment of the present invention.

[0066] Among them, in the preferred implementation mode provided by the present invention, the risk index calculation module 101 specifically includes: The area determination unit 1011 is used to determine the forest monitoring area that needs to be monitored for forest fire prevention.

[0067] The area division unit 1012 is used to divide the forest monitoring area into multiple monitoring sub-areas according to the preset fine division requirements.

[0068] The data collection unit 1013 is used to collect the fire risk factor data of the plurality of monitoring sub-areas.

[0069] The risk index calculation unit 1014 is used to calculate the sub-region risk indexes of multiple monitoring sub-regions according to the multiple fire risk factor data.

[0070] Furthermore, the intelligent monitoring and early warning system for forest fire prevention also includes: The risk classification analysis module 102 is used to perform classification analysis on the risk indexes of the plurality of sub-regions, select a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determine corresponding risk levels.

[0071] In an embodiment of the present invention, the risk grading analysis module 102 compares and analyzes multiple sub-region risk indexes based on a preset risk threshold, and marks the sub-region risk index greater than the risk threshold as a target risk index, so that multiple target risk indices can be selected, and then the monitoring sub-regions corresponding to the multiple target risk indices are marked as risk sub-regions, so that multiple risk sub-regions can be selected from the multiple monitoring sub-regions, and then based on the preset standard grading data, the multiple target risk indices are graded and matched, the graded matching results are recorded, and then the risk levels corresponding to the multiple risk sub-regions are determined according to the graded matching results.

[0072] The flight monitoring planning module 103 is used to match multiple monitoring densities and surveillance frequencies according to the multiple risk levels, perform flight monitoring planning for the multiple monitoring sub-areas according to the multiple monitoring densities, and generate multiple flight monitoring routes.

[0073] In an embodiment of the present invention, the flight monitoring planning module 103 matches multiple corresponding monitoring densities and surveillance frequencies according to multiple risk levels, and then obtains sub-area electronic maps of multiple monitoring sub-areas, and then performs different flight monitoring plans for multiple monitoring sub-areas according to multiple monitoring densities in the multiple sub-area electronic maps to generate multiple flight monitoring routes.

[0074] The flight monitoring control module 104 is used to perform drone flight and monitoring control in multiple monitoring sub-areas according to multiple flight monitoring routes and corresponding monitoring frequencies, and obtain multiple fire prevention monitoring images that are directly or collaboratively transmitted.

[0075] In an embodiment of the present invention, the flight monitoring control module 104 performs different flight controls on drones in multiple monitoring sub-areas according to multiple flight monitoring routes, and performs different monitoring controls on drones in multiple monitoring sub-areas according to multiple monitoring frequencies, and determines the direct transmission state and the collaborative transmission state according to the signal strength of the drones in different monitoring sub-areas, and performs different signal transmissions. Specifically, in the direct transmission state, the fire prevention monitoring image directly transmitted by the drone is obtained; in the collaborative transmission state, the drone whose signal strength is not greater than the preset signal threshold is marked as the target drone, and one or more collaborative drones whose signal strength is greater than the signal threshold between the target drone and the signal receiving position are selected. The target drone can transmit the fire prevention monitoring image to the nearest collaborative drone, and the collaborative drone will directly transmit the fire prevention monitoring image in a collaborative manner, or relay the fire prevention monitoring image between multiple collaborative drones.

[0076] The fire judgment alarm module 105 is used to calculate and compare the fire judgment of the plurality of fire prevention monitoring images according to the RGB three-color channels, and when it is determined that there is a forest fire, determine the location of the forest fire and issue a fire alarm.

[0077] In the embodiment of the present invention, the fire judgment alarm module 105 performs calculation comparison of fire judgment on multiple fire prevention monitoring images according to the RGB three-color channels, obtains the calculation comparison results, and then judges whether there is a forest fire according to the calculation comparison results. When it is determined that there is a forest fire, the forest fire location is determined according to the image positioning information of the corresponding fire prevention monitoring image, and then based on the forest fire location, a positioning alarm signal is generated, and a fire alarm is performed to achieve accurate and timely fire monitoring and early warning. Specifically, the calculation comparison formula for fire judgment on multiple fire prevention monitoring images is: ; in, is the pixel coordinates in the fire monitoring image, for The R value of the pixel, for The G value of the pixel, for The B value of the pixel, It is the accumulation of R values ​​of all pixels in the fire monitoring image.

[0078] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0079] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0080] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent monitoring and early warning method for forest fire prevention, characterized in that: The method specifically comprises the following steps: Determine a forest monitoring area, divide it into a plurality of monitoring sub-areas, collect fire risk factor data of the plurality of monitoring sub-areas, and calculate sub-area risk indexes of the plurality of monitoring sub-areas; Performing hierarchical analysis on the risk indexes of the plurality of sub-regions, selecting a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determining corresponding risk levels; According to the multiple risk levels, multiple monitoring densities and surveillance frequencies are matched, and according to the multiple monitoring densities, flight monitoring planning is performed on the multiple monitoring sub-areas to generate multiple flight monitoring routes; According to the plurality of flight monitoring routes and corresponding monitoring frequencies, the drone flight and monitoring control are performed in the plurality of monitoring sub-areas, and a plurality of fire prevention monitoring images are acquired that are directly or collaboratively transmitted; According to the RGB three-color channels, a plurality of fire prevention monitoring images are calculated and compared for fire judgment. When it is determined that there is a forest fire, the location of the forest fire is determined and a fire alarm is issued.

2. The intelligent monitoring and early warning method for forest fire prevention according to claim 1 is characterized in that: The determining of the forest monitoring area, dividing the area into a plurality of monitoring sub-areas, collecting the fire risk factor data of the plurality of monitoring sub-areas, and calculating the sub-area risk index of the plurality of monitoring sub-areas specifically comprises the following steps: Identify forest monitoring areas that require forest fire monitoring; Divide the forest monitoring area into a plurality of monitoring sub-areas according to preset fine division requirements; Collecting fire risk factor data of a plurality of monitoring sub-areas; According to the plurality of fire risk factor data, sub-region risk indexes of the plurality of monitoring sub-regions are calculated.

3. The intelligent monitoring and early warning method for forest fire prevention according to claim 2 is characterized in that: The calculation formula of the sub-region risk index of the plurality of monitoring sub-regions is: ； in, Representative monitoring sub-areas, For the Sub-region risk index for each monitored sub-region, For the The time-limited temperature of each monitoring sub-area, is the first temperature conversion coefficient, is the second temperature conversion coefficient, is the maximum temperature adjustment coefficient, is the humidity conversion factor, For the The time-limited humidity of each monitoring sub-area, is the minimum humidity adjustment factor, is the wind speed conversion coefficient, For the The time-limited wind speed in each monitoring sub-area is For the The number of consecutive days without rainfall in each monitoring sub-area.

4. The intelligent monitoring and early warning method for forest fire prevention according to claim 1 is characterized in that: The step of performing hierarchical analysis on the risk indexes of the plurality of sub-regions, selecting a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determining the corresponding risk levels specifically comprises the following steps: Based on a preset risk threshold, comparing and analyzing the risk indexes of the plurality of sub-regions, and selecting a plurality of target risk indexes; Selecting multiple risk sub-areas from multiple monitoring sub-areas according to multiple target risk indexes; Based on the preset standard grading data, the plurality of target risk indices are graded and matched, and the graded matching results are recorded; According to the hierarchical matching result, the risk levels corresponding to the plurality of risk sub-areas are determined.

5. The intelligent monitoring and early warning method for forest fire prevention according to claim 1 is characterized in that: According to the plurality of risk levels, matching a plurality of monitoring densities and surveillance frequencies, performing flight monitoring planning for a plurality of monitoring sub-areas according to the plurality of monitoring densities, and generating a plurality of flight monitoring routes specifically include the following steps: According to the multiple risk levels, multiple monitoring densities and surveillance frequencies are matched; Obtaining sub-area electronic maps of a plurality of the monitoring sub-areas; Based on the electronic maps of the multiple sub-areas and according to the multiple monitoring densities, flight monitoring planning is performed on the multiple monitoring sub-areas to generate multiple flight monitoring routes.

6. The intelligent monitoring and early warning method for forest fire prevention according to claim 1 is characterized in that: The method of performing drone flight and surveillance control in multiple monitoring sub-areas according to the multiple flight monitoring routes and corresponding surveillance frequencies, and obtaining multiple fire prevention surveillance images transmitted directly or collaboratively specifically includes the following steps: Performing flight control of the UAV in multiple monitoring sub-areas according to the multiple flight monitoring routes; According to the plurality of monitoring frequencies, monitoring and controlling the drones in the plurality of monitoring sub-areas; Determine the direct transmission state and the cooperative transmission state according to the signal strength of the drone; In the direct transmission state, the fire prevention monitoring images directly transmitted by the drone are obtained; In the collaborative transmission state, the target UAV and multiple collaborative UAVs are determined, and the fire prevention monitoring images of the target UAV are collaboratively transmitted through the multiple collaborative UAVs.

7. The intelligent monitoring and early warning method for forest fire prevention according to claim 1 is characterized in that: The method of calculating and comparing the fire judgment of the plurality of fire prevention monitoring images according to the RGB three-color channels, determining the location of the forest fire when it is determined that there is a forest fire, and issuing a fire alarm specifically includes the following steps: According to the RGB three-color channels, a plurality of fire prevention monitoring images are compared and calculated for fire judgment to obtain a calculation and comparison result; According to the calculation and comparison results, determining whether there is a forest fire; When there is a forest fire, determine the location of the forest fire; Based on the forest fire location, a positioning alarm signal is generated and a fire alarm is issued.

8. The intelligent monitoring and early warning method for forest fire prevention according to claim 7 is characterized in that: The formula for calculating and comparing the fire judgment of the plurality of fire prevention monitoring images is: ； in, is the pixel coordinates in the fire monitoring image, for The R value of the pixel, for The G value of the pixel, for The B value of the pixel, It is the accumulation of R values ​​of all pixels in the fire monitoring image.

9. An intelligent monitoring and early warning system for forest fire prevention, characterized in that: The system includes a risk index calculation module, a risk classification analysis module, a flight monitoring planning module, a flight monitoring control module and a fire judgment alarm module, wherein: A risk index calculation module is used to determine the forest monitoring area, divide it into multiple monitoring sub-areas, collect fire risk factor data of multiple monitoring sub-areas, and calculate the sub-area risk index of multiple monitoring sub-areas; A risk classification analysis module, used to perform classification analysis on the risk indexes of the plurality of sub-regions, select a plurality of risk sub-regions from the plurality of monitoring sub-regions, and determine the corresponding risk levels; A flight monitoring planning module is used to match multiple monitoring densities and surveillance frequencies according to multiple risk levels, perform flight monitoring planning for multiple monitoring sub-areas according to multiple monitoring densities, and generate multiple flight monitoring routes; A flight monitoring control module is used to perform drone flight and monitoring control in multiple monitoring sub-areas according to multiple flight monitoring routes and corresponding monitoring frequencies, and obtain multiple fire prevention monitoring images transmitted directly or collaboratively; The fire judgment alarm module is used to calculate and compare the fire judgment of multiple fire prevention monitoring images according to the RGB three-color channels. When it is determined that there is a forest fire, the location of the forest fire is determined and a fire alarm is issued.

10. The intelligent monitoring and early warning system for forest fire prevention according to claim 9 is characterized in that: The risk index calculation module specifically includes: An area determination unit is used to determine the forest monitoring area that needs to be monitored for forest fire prevention; A region division unit, used to divide the forest monitoring area into a plurality of monitoring sub-areas according to a preset fine division requirement; A data collection unit, used for collecting fire risk factor data of a plurality of the monitoring sub-areas; The risk index calculation unit is used to calculate the sub-area risk indexes of multiple monitoring sub-areas according to the multiple fire risk factor data.

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