Forest fire prevention and monitoring system for forestry engineering

Abstract

The invention discloses a forest fire prevention and monitoring system for forestry engineering, and relates to the technical field of forest fire prevention and alarm. A preliminary early warning updating and integrating module is used for integrating and updating risk intervals of preliminary early warning and judgment; the data acquisition module is used for monitoring and acquiring forest environment data in a forest area M in a current monitoring stage in real time, and the risk processing and feedback module is used for sequentially calculating and outputting a fire risk index HZF, an alarm threshold BHZ and a risk adjustment coefficient FT. And the alarm and adjustment module is used for carrying out preliminary early warning and triggering an alarm according to comparative analysis of the fire risk index HZF and the alarm threshold BHZ, and carrying out early warning and adjustment on the risk adjustment coefficient FT on the basis of comparative analysis of the fire risk index HZF and the alarm threshold BHZ and under the condition that the alarm is not triggered. The fire risk can be evaluated more comprehensively, and the alarm threshold value and the monitoring strategy can be adjusted dynamically, so that the self-adaptive capability and the early warning accuracy of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of forest fire prevention and alarm, and in particular to a forest fire prevention monitoring system for a forestry engineering project. Background Art

[0002] Forest fire is one of the main natural disasters that endanger forest resources. It is characterized by strong suddenness, rapid spread and serious losses. In order to protect forest resources, prevent and reduce the occurrence of forest fires, the forestry engineering forest fire prevention and monitoring system came into being. The system is based on modern sensor technology, communication technology, data processing technology and artificial intelligence technology. High-tech means to achieve real-time monitoring and early warning of the forest environment.

[0003] At present, some existing forest fire prevention systems often rely only on single meteorological data and simple sensor data, which are often not comprehensive and are easily affected by environmental noise and instrument errors, resulting in inaccurate warning results. It should be noted that existing systems often use fixed alarm thresholds, which makes it difficult to dynamically adjust according to real-time meteorological conditions and historical fire data, resulting in limited sensitivity and accuracy of the early warning system. Some systems lack flexible monitoring strategies, making it difficult to dynamically adjust the monitoring frequency and alarm thresholds according to changes in fire risks, resulting in waste of resources and untimely warnings. In addition, many systems lack an effective closed-loop feedback mechanism, making it difficult to continuously optimize and improve the system based on the warning results and actual conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a forest fire prevention monitoring system for forestry engineering, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions, including a monitoring system for dynamic monitoring and early warning of forest fire risks; It includes a preliminary warning update integration module, a data acquisition module, a risk processing and feedback module, an alarm and adjustment module and a display module. The risk processing and feedback module includes a unit for comprehensively reflecting the forest fire risk level, a unit for providing preliminary alarm judgment basis, and a monitoring and alarm judgment strategy adjustment unit; The specific implementation is as follows: Initial warning update integration module: used for the integration and update of initial warning and risk intervals; Data collection module: used to monitor and collect forest environment data within the forest area M in the current monitoring stage in real time. The forest environment data includes temperature, humidity, combustibles, wind force, wind direction and climate conditions; Risk processing and feedback module: used to calculate and output the fire risk index HZF, alarm threshold BHZ, and risk adjustment coefficient FT in sequence; Alarm and adjustment module: used to make preliminary warning and trigger alarm based on the comparison analysis of fire risk index HZF and alarm threshold BHZ, and to make warning and adjustment to risk adjustment factor FT when the comparison analysis of fire risk index HZF and alarm threshold BHZ is based on and alarm is not triggered; Display module: used to display the results of monitoring, early warning and adjustment; Among them, if the comparison analysis between the fire risk index HZF and the alarm threshold BHZ produces a preliminary warning and triggers an alarm, there is no need to calculate the risk adjustment factor FT.

[0006] Optionally, the steps of integrating and updating the risk intervals by the preliminary warning update integration module are as follows: Step 1: extracting the risk adjustment factor FT stored in the data acquisition module and which has not recently occurred in a fire; Step 2: using the preliminary warning update integration module to integrate and collect; Step 3: Based on the integrated risk adjustment factor FT, the risk interval {FT min ,FT max}; Step 4: Based on the recent risk adjustment factor FT, regularly update the risk range according to the rules set above; Among them, FT min is the minimum risk adjustment factor, FT max is the maximum risk adjustment factor.

[0007] Optionally, the calculation formula for the unit that comprehensively reflects the forest fire risk level is as follows: ; f = X × |cos(J)|; in: HZF is the fire risk index; W is the current temperature; S is the current humidity; KR is the mass of fuel, KR represents the total amount of fuel in the forest; M is the forest area, and M represents the total forest area in the monitoring area; FL is the wind force value, FL indicates the current wind force; J is the angle value, which reflects the angle between the wind direction and the direction of the monitoring area, indicating the degree of influence of the wind on the monitoring area; f is the wind direction adjustment coefficient, which is used to adjust the impact of wind direction on fire risk, and its value range is {-1,1}; X is a constant, and since the value range of f is {-1,1}, X>0.

[0008] Optionally, the calculation formula of the unit for providing the basis for preliminary alarm judgment is as follows: ; QH = a×W + b×S + c×FL; PL = FH t / Z t ; Where: BHZ is the alarm threshold; QH is the current climate condition index; a, b, and c are weight coefficients, and a + b + c = 1; QH max is the maximum climate condition index; QH min is the minimum climate condition index; PL is the historical fire occurrence frequency; FH t is the time of fire occurrence, and Z t is the total monitoring time; SS is the loss caused by historical fires; The historical fire occurrence frequency PL and the loss SS caused by historical fires are in the same monitoring stage.

[0009] Optionally, based on the alarm threshold BHZ and the fire risk index HZF: If HZF > BHZ, there is no need to calculate the monitoring and alarm judgment strategy adjustment unit, and an alarm is triggered; If HZF < BHZ, calculate the monitoring and alarm judgment strategy adjustment unit.

[0010] Optionally, the calculation formula of the monitoring and alarm judgment strategy adjustment unit is as follows: ; Where: FT is the risk adjustment coefficient; HZF prev is the fire risk index in the previous monitoring stage; J prev is the included angle value in the previous monitoring stage.

[0011] Optionally, based on the risk adjustment coefficient FT and the risk interval {FT min , FT max}, the monitoring and prevention adjustment is as follows: If the risk adjustment coefficient FT > the minimum risk adjustment coefficient FT min , it means that the current forest fire risk is high. While triggering the alarm, it is necessary to increase the monitoring frequency; If the risk adjustment factor FT is in the risk interval {FT min ,FT max}, it means that the current forest fire risk is within an acceptable range and the current monitoring frequency should be maintained; If the risk adjustment factor FT < the maximum risk adjustment factor FT max , it means that the current forest fire risk is low and the monitoring frequency should be reduced.

[0012] Optionally, the devices used by the preliminary warning update integration module include servers and storage devices; The equipment used in the data acquisition module includes temperature sensors, humidity sensors, combustible quality measuring instruments, wind force measuring instruments, wind direction sensors, and data acquisition and transmission equipment; The equipment used in the risk processing and feedback module includes computing and storage equipment; The equipment used in the alarm and adjustment module includes an alarm; The devices used by the display module include a display device.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The system of the present invention can more accurately assess the risk level of forest fires by comprehensively considering multiple factors and using the fire risk index HZF calculated by the unit that comprehensively reflects the risk level of forest fires. At the same time, the unit that provides preliminary alarm judgment basis dynamically adjusts the alarm threshold BHZ according to climate conditions and historical fire data, thereby further improving the accuracy of early warning. The monitoring and alarm judgment strategy adjustment unit realizes dynamic adjustment of the monitoring strategy by calculating the risk adjustment coefficient FT, so that the system can automatically increase the monitoring frequency to quickly respond to changes in fire risks.

[0014] Second, the present invention is based on the risk adjustment coefficient FT and the risk interval {FT min ,FT max}, so that the system can flexibly adjust the monitoring frequency of the monitored area, thereby achieving reasonable allocation and efficient utilization of resources, which not only avoids waste of resources but also improves the timeliness and effectiveness of early warning.

[0015] 3. The system of the present invention regularly updates the risk interval {FT min ,FT max}And recalculate the fire risk index HZF, alarm threshold BHZ and risk adjustment factor FT, forming a closed-loop feedback system, which can not only continuously optimize the performance of the system, but also fine-tune the algorithm formula according to actual conditions to adapt to the characteristics and needs of different forest areas.

[0016] In addition, the algorithm formula used in this system is not only innovative but also highly practical. It can comprehensively consider multiple factors to achieve accurate assessment and dynamic early warning of fire risks, providing strong technical support for forest fire prevention in forestry projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a method flow chart of the forest fire prevention and monitoring system of this forestry project; Figure 2 It is a schematic flow chart of the steps of integrating and updating the risk intervals by the preliminary warning update integration module in the present invention; Figure 3 It is a schematic diagram of the structure of the risk processing and feedback module of the present invention; Figure 4 It is a warning diagram of the risk adjustment factor FT of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Regarding the forest fire prevention and monitoring system of this forestry project, it is different from the existing forest fire prevention system. The existing forest fire prevention system has the problems of incomplete data, inability to dynamically adjust according to real-time meteorological conditions and historical fire data, lack of flexible monitoring strategies and closed-loop feedback mechanisms. This algorithm unit comprehensively considers multiple key parameters and dynamic data, can more comprehensively assess fire risks, dynamically adjust alarm thresholds and monitoring strategies, so as to improve the system's adaptability and warning accuracy. These beneficial effects enable the monitoring system to better respond to the challenges of forest fires and protect the safety and stability of forest resources.

[0020] For example, see Figures 1 to 4 ,This implementation provides a forest fire prevention monitoring system for forestry projects, including a monitoring system for dynamic monitoring and early warning of forest fire risks; It includes a preliminary warning update integration module, a data acquisition module, a risk processing and feedback module, an alarm and adjustment module and a display module. The risk processing and feedback module includes a unit that comprehensively reflects the forest fire risk level, a unit that provides preliminary alarm judgment basis, and a monitoring and alarm judgment strategy adjustment unit. The specific implementation is as follows: Initial warning update integration module: used for the integration and update of initial warning and risk intervals; Data collection module: used to monitor and collect forest environment data within the forest area M in the current monitoring stage in real time. The forest environment data includes temperature, humidity, combustibles, wind force, wind direction and climate conditions; Risk processing and feedback module: used to calculate and output the fire risk index HZF, alarm threshold BHZ, and risk adjustment coefficient FT in sequence; Alarm and adjustment module: used to make preliminary warning and trigger alarm based on the comparison analysis of fire risk index HZF and alarm threshold BHZ, and to make warning and adjustment to risk adjustment factor FT when the comparison analysis of fire risk index HZF and alarm threshold BHZ is based on and alarm is not triggered; Display module: used to display the results of monitoring, early warning and adjustment; Among them, if the comparison analysis of the fire risk index HZF and the alarm threshold BHZ produces a preliminary warning and triggers an alarm, there is no need to calculate the risk adjustment factor FT; The equipment used by the preliminary warning update integration module includes servers and storage devices; The equipment used in the data acquisition module includes temperature sensors, humidity sensors, combustible quality measuring instruments, wind force measuring instruments, wind direction sensors, and data acquisition and transmission equipment; The equipment used in the risk processing and feedback module includes computing and storage equipment; The equipment used in the alarm and adjustment module includes alarms; The devices used by the display module include display devices.

[0021] In this embodiment, the system cooperates with each other through three algorithm units and combines the three operation results of HZF, BHZ and FT to form a complete, efficient and adaptive monitoring system. Specifically, HZF is a fire risk index, which is obtained by integrating multiple factors closely related to fire risk, and can comprehensively evaluate the fire risk of the current forest environment, and provide basic data for subsequent alarm threshold calculation and monitoring strategy optimization. BHZ is an alarm threshold. By dynamically adjusting the BHZ value, the system can be more adapted to current environmental conditions and historical experience to improve the accuracy and sensitivity of the alarm. FT is a risk adjustment coefficient, which reflects the change of the current fire risk relative to the last calculation, as well as the impact of wind direction changes on the fire risk. The calculation result of BHZ can affect the feedback to HZF, and the calculation result of FT can also affect the feedback to the calculation of HZF and BHZ, so that the three algorithms of this system play an important role in the forest fire prevention and monitoring system of forestry engineering. Through the mutual correlation and cyclic influence of these three algorithms, the system can continuously learn and optimize, improve the accuracy and response speed of early warning, and provide more powerful support for the prevention and control of forest fires.

[0022] See also Figures 1 to 4 , the calculation formula for the unit that comprehensively reflects the forest fire risk degree is as follows: ; f = X × |cos(J)|; in: HZF is the fire risk index; W is the current temperature; S is the current humidity; KR is the mass of fuel, KR represents the total amount of fuel in the forest; M is the forest area, and M represents the total forest area in the monitoring area; FL is the wind force value, FL indicates the current wind force; J is the angle value, which reflects the angle between the wind direction and the direction of the monitoring area, indicating the degree of influence of the wind on the monitoring area; f is the wind direction adjustment coefficient, which is used to adjust the impact of wind direction on fire risk, and its value range is {-1,1}; X is a constant, and since the value range of f is {-1,1}, X>0.

[0023] In this embodiment: First, in this algorithm unit, " "The calculation part calculates the temperature divided by the square of humidity. Temperature and humidity are key factors affecting forest fire risk. They jointly determine the dryness of the forest environment. By calculating the square of the division between them, this effect can be amplified, making the fire risk index HZF more sensitive to changes in temperature and humidity. As part of the calculation of the fire risk index HZF, it reflects the direct impact of temperature and humidity on fire risk and is an important basic data for assessing the degree of forest fire risk. “ "The calculation part calculates the combustible mass KR divided by the square of the forest area M. The combustible mass KR determines the potential amount of fuel in the forest, while the forest area M determines the scope of fire spread. By calculating the square of the division between them, the impact of these two factors on fire risk can be comprehensively considered. As part of the calculation of the fire risk index HZF, it reflects the joint impact of combustible mass and forest area on fire risk, and is an indispensable part of assessing the degree of fire risk. “ "The " part calculates the difference between the wind force and the angle value J, and the square of the difference divided by the wind direction adjustment coefficient f. The wind force determines the speed and direction of fire spread, and the wind direction adjustment coefficient f is used to adjust the impact of wind direction on fire risk. By calculating the square of this difference, the impact of wind force and wind direction on fire risk can be comprehensively considered. As the last part of the calculation of the fire risk index HZF, it reflects the direct impact of wind force and wind direction on fire risk, and is an important factor to be considered when assessing the degree of fire risk. The temperature and humidity in this algorithm unit are key factors affecting the dryness and flammability of combustibles. High temperature and low humidity will increase the dryness of combustibles, thereby increasing the risk of fire. The unit that comprehensively reflects the risk of forest fires can more accurately assess the possibility of fire by considering these two parameters. The quantity, type and distribution of combustibles have a direct impact on the fire risk. The unit that comprehensively reflects the risk of forest fires quantifies the quality of combustibles KR and incorporates it into the risk assessment system, thereby improving the accuracy of the assessment. The larger the forest area M, the higher the potential fire risk. The unit that comprehensively reflects the risk of forest fires can more comprehensively assess the threat of fire to the entire forest ecosystem by considering the forest area M. In addition, wind force and wind direction are important factors affecting the speed and direction of fire spread. The unit that comprehensively reflects the risk of forest fires can more accurately predict the spread trend of fires by introducing these two parameters, providing a basis for formulating response measures. The unit that comprehensively reflects the forest fire risk level can more comprehensively assess the risk level of forest fires and avoid the one-sidedness of single factor assessment. At the same time, the fire risk index HZF value calculated by the unit that comprehensively reflects the forest fire risk level also provides key basic data for subsequent formulas, ensuring the consistency and accuracy of the entire monitoring system.

[0024] See also Figures 1 to 4 , the calculation formula for providing the preliminary alarm judgment basis unit is as follows: ; QH=a×W+b×S+c×FL; PL=FH t / Z t ; in: BHZ is the alarm threshold; QH is the current climate condition index; a, b and c are weight coefficients, and a+b+c=1; QH max is the maximum index of climate conditions; QH min is the minimum index of climate conditions; PL is the historical fire frequency; FHt When a fire occurs, Z t is the total monitoring time; SS is the damage caused by historical fire; The historical fire occurrence frequency PL and the historical fire loss SS are in the same monitoring stage.

[0025] In this embodiment, first, "The calculation part calculates the product of the fire risk index HZF and an adjustment factor based on the current climate condition index QH. The current climate condition index QH combines temperature, humidity, and wind factors, and reflects the impact of current climate conditions on fire risk. By calculating this adjustment factor, the value of the alarm threshold BHZ can be dynamically adjusted according to changes in climate conditions. As part of the calculation of the alarm threshold BHZ, it reflects the impact of climate conditions on the alarm threshold BHZ, so that the alarm threshold BHZ can be dynamically adjusted according to current climate conditions, thereby improving the accuracy and timeliness of the alarm; “ "The calculation part calculates the square root of the sum of the squares of the historical fire frequency PL and the historical fire losses SS. These two factors together reflect the historical situation and potential risks of forest fires. By calculating the square root of the sum of their squares, the impact of these two factors on the alarm threshold BHZ can be comprehensively considered. As another part of the calculation of the alarm threshold BHZ, it reflects the impact of the historical fire situation on the alarm threshold BHZ, so that the alarm threshold BHZ can be dynamically adjusted according to historical data, thereby improving the early warning capability of the system; This algorithm unit introduces climate conditions and historical fire data to enable the unit that provides the basis for preliminary alarm judgment to dynamically adjust the alarm threshold BHZ value. Climate conditions reflect the dryness and temperature conditions of the current environment, while historical fire data provide information on the frequency and scale of past fires. The combination of these data makes the alarm threshold BHZ value more suitable for current environmental conditions and historical experience, thereby improving the sensitivity and accuracy of the alarm system. When the fire risk index HZF exceeds the alarm threshold BHZ, the system can quickly trigger an alarm. This timely early warning mechanism provides valuable response time, helps to take timely response measures and reduce losses caused by fire. The alarm threshold BHZ value of this algorithm is not only used to judge the alarm status, but also provides key input data for the monitoring and alarm judgment strategy adjustment unit. The monitoring and alarm judgment strategy adjustment unit can further optimize the monitoring strategy and alarm threshold by considering the alarm threshold BHZ value and other parameters to form a closed-loop feedback system.

[0026] See also Figures 1 to 4 , the calculation formula of the monitoring and alarm judgment strategy adjustment unit is as follows: ; in: FT is the risk adjustment factor; HkDJ prev is the fire risk index of the previous monitoring stage; J prev is the angle value of the previous monitoring stage.

[0027] In this embodiment, the algorithm unit first " The calculation part calculates the fire risk index HZF of the previous monitoring stage. prev The ratio of the fire risk calculated last time to the current alarm threshold BHZ. This ratio reflects the relative relationship between the fire risk calculated last time and the current alarm threshold BHZ. As part of the calculation of the risk adjustment factor FT, it reflects the relative relationship between the fire risk calculated last time and the current alarm threshold BHZ, and is an important basis for adjusting the monitoring strategy and the alarm threshold BHZ. “ The calculation part calculates the angle between the current wind direction and the direction of the monitored area and the angle value J in the previous monitoring stage. prev The difference reflects the change of wind direction and is another part of the risk adjustment factor FT calculation. It reflects the impact of wind direction changes on fire risk and is one of the important factors to be considered when adjusting monitoring strategies and alarm thresholds BHZ. By calculating the risk adjustment coefficient FT value in this algorithm unit, the monitoring and alarm judgment strategy adjustment unit can dynamically adjust the monitoring frequency and alarm threshold BHZ of the monitoring area according to the current fire risk situation. This flexible adjustment mechanism enables the monitoring system to use resources more efficiently and realize key monitoring of high-risk areas and moderate monitoring of low-risk areas. The monitoring and alarm judgment strategy adjustment unit introduces dynamic data of wind direction angle, so that the monitoring system can adapt to environmental changes and changes in fire risks more flexibly. This adaptive capability enables the monitoring system to maintain efficient operation under different environmental conditions, improving the stability and reliability of the system. The calculation results of the monitoring and alarm judgment strategy adjustment unit will affect the calculation of the alarm threshold BHZ in the unit that provides the basis for preliminary alarm judgment, and then affect the application of the unit that comprehensively reflects the degree of forest fire risk. This closed-loop feedback mechanism enables the monitoring system to continuously learn and optimize. By continuously accumulating experience and data, the monitoring system can gradually improve the accuracy of early warning and response speed, providing more powerful support for the prevention and control of forest fires.

[0028] See also Figures 1 to 4 The steps for integrating and updating the risk intervals in the preliminary warning update integration module are as follows: Step 1: Extract the risk adjustment factor FT stored in the data acquisition module and which has not recently occurred in a fire; Step 2: Use the preliminary warning update integration module to integrate and collect; Step 3: Based on the integrated risk adjustment factor FT, the risk interval {FT min ,FT max}; Step 4: Based on the recent risk adjustment factor FT, regularly update the risk range according to the rules set above; Among them, FT min is the minimum risk adjustment factor, FT max is the maximum risk adjustment factor; Based on the risk adjustment factor FT and the risk interval {FT min ,FT max The monitoring and preventive adjustments of} are as follows: If the risk adjustment factor FT> the minimum risk adjustment factor FT min , it means that the current forest fire risk is high, and the alarm needs to be triggered and the monitoring frequency needs to be increased; If the risk adjustment factor FT is in the risk interval {FT min ,FT max}, it means that the current forest fire risk is within an acceptable range and the current monitoring frequency should be maintained; If the risk adjustment factor FT < the maximum risk adjustment factor FT max , it means that the current forest fire risk is low and the monitoring frequency should be reduced.

[0029] In this embodiment, based on the circular feedback mechanism of the monitoring and alarm judgment strategy adjustment unit, the monitoring system can dynamically adjust the parameters and calculation process in the unit that comprehensively reflects the forest fire risk degree according to the current fire risk situation, so that the system can better adapt to environmental changes and changes in fire risks. Among them, the circular feedback mechanism of the monitoring and alarm judgment strategy adjustment unit enables the monitoring system to continuously learn and optimize, reduces false alarms and missed alarms caused by single factors and erroneous data, and improves the stability and reliability of the early warning system. Through the circular feedback mechanism of the monitoring and alarm judgment strategy adjustment unit, the monitoring system can continuously accumulate experience and data, and provide strong support for subsequent algorithm optimization and system improvement. This continuous improvement and optimization process will make the monitoring system more complete and efficient; In addition, the unit that comprehensively reflects the forest fire risk level, the unit that provides the basis for preliminary alarm judgment, and the monitoring and alarm judgment strategy adjustment unit comprehensively consider multiple factors such as temperature, humidity, wind force, wind direction, and combustibles, and calculate the fire risk index HZF and the alarm threshold BHZ through an accurate mathematical model, so as to more accurately assess the current forest fire risk. When the fire risk index HZF exceeds the alarm threshold BHZ, the system can immediately trigger an alarm, avoiding the problem of untimely warning caused by calculation delays; According to the value range of the risk adjustment factor FT, the system can automatically adjust the monitoring frequency to ensure that the number of monitoring times is increased when the fire risk is high to capture possible fire signs. This dynamic adjustment strategy not only improves monitoring efficiency, but also reduces the cost burden caused by excessive monitoring. When the risk adjustment coefficient FT is lower than the set minimum value, the system can automatically reduce the monitoring frequency to avoid excessive monitoring when the fire risk is low, which helps to save manpower, material and financial resources and improve resource utilization efficiency. By introducing a unit that comprehensively reflects the degree of forest fire risk, providing a preliminary alarm judgment basis unit, a monitoring and alarm judgment strategy adjustment unit and a risk adjustment coefficient FT risk interval {FT min ,FT max} settings, the system can automatically adjust the early warning strategy according to real-time data, realizing the intelligent upgrade of the early warning system. This level of intelligence not only improves the accuracy of early warning, but also reduces the frequency and difficulty of manual intervention; The parameters in the unit that comprehensively reflects the risk level of forest fires and provides the basis for preliminary alarm judgment and the monitoring and alarm judgment strategy adjustment unit can be adjusted according to the forest types, climate conditions and combustible factors in different regions to meet the fire prevention needs under different environmental conditions. This adaptability enables the system to maintain a high level of early warning accuracy in different regions and seasons to ensure the effectiveness and reliability of the early warning system. This flexibility helps the system better cope with various complex situations and improve the overall early warning effect. By introducing mathematical models and quantitative indicators, including the fire risk index HZF, the alarm threshold BHZ and the risk adjustment coefficient FT, the system can provide scientific basis and data support for forest fire prevention work, which helps to more accurately understand the current forest fire risk status, so as to formulate scientific and reasonable prevention measures, and help reduce fire accidents caused by poor management and improve the overall effect of forest fire prevention work; In summary, closely combined with the unit comprehensively reflecting the forest fire risk level, the unit providing the basis for preliminary alarm judgment, the unit for monitoring and adjusting the alarm judgment strategy, and the parameters therein are used to set the judgment value, adjust the monitoring frequency, and trigger an alarm when the fire risk index HZF is greater than the alarm threshold BHZ without the need to calculate the monitoring and alarm judgment strategy adjustment unit. This strategy has significant beneficial effects in the forest fire prevention and monitoring system of forestry engineering. It can not only improve the accuracy and timeliness of early warning, optimize resource allocation and reduce costs, but also enhance the adaptability and flexibility of the system, and promote the scientific and standardized forest fire prevention work.

[0030] Example 2, please refer to Figures 1 to 4 , based on the alarm threshold BHZ and the fire risk index HZF: If HZF > BHZ, there is no need to calculate the monitoring and alarm judgment strategy adjustment unit, and an alarm is triggered; If HZF < BHZ, calculate the monitoring and alarm judgment strategy adjustment unit.

[0031] In this embodiment, it should be noted that in an emergency, time is life. By setting the rule that when the fire risk index HZF > the alarm threshold BHZ, an alarm is triggered, the decision-making process can be greatly simplified, the system processing time can be reduced, and thus the fire early warning information can be received faster, so as to take actions quickly and reduce the losses caused by the fire; The calculation of the monitoring and alarm judgment strategy adjustment unit involves multiple parameters and complex mathematical operations, which will increase the complexity and error rate of the system. In an emergency, any small calculation error will lead to serious consequences. Therefore, by simplifying the process and avoiding the calculation of the monitoring and alarm judgment strategy adjustment unit, the complexity of the system can be reduced, and the stability and reliability of the system can be improved; The calculation of the monitoring and alarm judgment strategy adjustment unit consumes a certain amount of computing resources and time. In a real-time monitoring system, the consumption of these resources will affect the overall performance and response time of the system. By setting the rule that when the fire risk index HZF > the alarm threshold BHZ, an alarm is triggered, unnecessary calculations can be avoided, resource consumption can be reduced, and the system performance can be optimized; Setting the rule that when the fire risk index HZF > the alarm threshold BHZ, an alarm is triggered provides a clear early warning standard, which can also make it clearer when to take actions, thereby enhancing the operability and practicability of the system. In addition, setting the alarm trigger point at HZF > BHZ actually strengthens the front-end monitoring and early warning capabilities, which means that the system can detect potential fire risks earlier and win more time and space for subsequent response and disposal work. This is of great significance for protecting forest resources and reducing fire losses; To sum up, if the fire risk index HZF>alarm threshold BHZ, the alarm is triggered without the need for monitoring and alarm judgment strategy adjustment. The beneficial effects of the unit calculation mechanism are mainly reflected in simplifying the decision-making process, improving response speed, reducing system complexity, reducing resource consumption, clarifying warning standards, and reserving flexible adjustment space. This rule not only improves the practicality and operability of the system, but also provides strong support for the prevention and control of forest fires.

[0032] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A forest fire prevention and monitoring system for forestry engineering, characterized in that: A monitoring system including dynamic monitoring and early warning of forest fire risks; It includes a preliminary warning update integration module, a data acquisition module, a risk processing and feedback module, an alarm and adjustment module and a display module. The risk processing and feedback module includes a unit for comprehensively reflecting the forest fire risk level, a unit for providing preliminary alarm judgment basis, and a monitoring and alarm judgment strategy adjustment unit; The specific implementation is as follows: Initial warning update integration module: used for the integration and update of initial warning and risk intervals; Data collection module: used to monitor and collect forest environment data within the forest area M in the current monitoring stage in real time. The forest environment data includes temperature, humidity, combustibles, wind force, wind direction and climate conditions; Risk processing and feedback module: used to calculate and output the fire risk index HZF, alarm threshold BHZ, and risk adjustment coefficient FT in sequence; Alarm and adjustment module: used to make preliminary warning and trigger alarm based on the comparison analysis of fire risk index HZF and alarm threshold BHZ, and to make warning and adjustment to risk adjustment factor FT when the comparison analysis of fire risk index HZF and alarm threshold BHZ is based on and alarm is not triggered; Display module: used to display the results of monitoring, early warning and adjustment; Among them, if the comparison analysis between the fire risk index HZF and the alarm threshold BHZ produces a preliminary warning and triggers an alarm, there is no need to calculate the risk adjustment factor FT.

2. A forestry engineering forest fire prevention monitoring system according to claim 1, characterized in that: The steps of integrating and updating the risk intervals by the preliminary warning update integration module are as follows: Step 1: extracting the risk adjustment factor FT stored in the data acquisition module and which has not recently occurred in a fire; Step 2: using the preliminary warning update integration module to integrate and collect; Step 3: Based on the integrated risk adjustment factor FT, the risk interval {FT min ,FT max }; Step 4: Based on the recent risk adjustment factor FT, regularly update the risk range according to the rules set above; Among them, FT min is the minimum risk adjustment factor, FT max is the maximum risk adjustment factor.

3. A forestry engineering forest fire prevention monitoring system according to claim 2, characterized in that: The calculation formula for the unit that comprehensively reflects the forest fire risk level is as follows: ； f = X × |cos(J)|; in: HZF is the fire risk index; W is the current temperature; S is the current humidity; KR is the mass of fuel, KR represents the total amount of fuel in the forest; M is the forest area, and M represents the total forest area in the monitoring area; FL is the wind force value, FL indicates the current wind force; J is the angle value, which reflects the angle between the wind direction and the direction of the monitoring area, indicating the degree of influence of the wind on the monitoring area; f is the wind direction adjustment coefficient, which is used to adjust the impact of wind direction on fire risk, and its value range is {-1,1}; X is a constant, and since the value range of f is {-1,1}, X>0.

4. A forestry engineering forest fire prevention monitoring system according to claim 3, characterized in that: The calculation formula of the unit providing preliminary alarm judgment basis is as follows: ； QH=a×W+b×S+c×FL; PL=FH t / Z t ; in: BHZ is the alarm threshold; QH is the current climate condition index; a, b and c are weight coefficients, and a+b+c=1; QH max is the maximum index of climate conditions; QH min is the minimum index of climate conditions; PL is the historical fire frequency; FH t When a fire occurs, Z t is the total monitoring time; SS is the damage caused by historical fire; The historical fire occurrence frequency PL and the historical fire loss SS are in the same monitoring stage.

5. A forestry engineering forest fire prevention monitoring system according to claim 4, characterized in that: Based on the alarm threshold BHZ and the fire risk index HZF: If HZF > BHZ, there is no need to calculate the monitoring and alarm judgment strategy adjustment unit, and an alarm is triggered; If HZF < BHZ, the calculation of the monitoring and alarm judgment strategy adjustment unit is carried out.

6. A forestry engineering forest fire prevention monitoring system according to claim 5, characterized in that: The calculation formula of the monitoring and alarm judgment strategy adjustment unit is as follows: ； Where: FT is the risk adjustment coefficient; HkDJ prev is the fire risk index of the previous monitoring stage; J prev is the angle value of the previous monitoring stage.

7. A forestry engineering forest fire prevention monitoring system according to claim 6, characterized in that: Based on the risk adjustment factor FT and the risk interval {FT min ,FT max The monitoring and preventive adjustments of} are as follows: If the risk adjustment factor FT> the minimum risk adjustment factor FT min , it means that the current forest fire risk is high, and the alarm needs to be triggered and the monitoring frequency needs to be increased; If the risk adjustment factor FT is in the risk interval {FT min ,FT max }, it means that the current forest fire risk is within an acceptable range and the current monitoring frequency should be maintained; If the risk adjustment factor FT < the maximum risk adjustment factor FT max , it means that the current forest fire risk is low and the monitoring frequency should be reduced.

8. A forest fire prevention monitoring system for forestry engineering according to claim 1, characterized in that The preliminary warning update and integration module includes a server and a storage device; The data acquisition module includes a temperature sensor, a humidity sensor, a combustible mass measuring instrument, a wind force measuring instrument, a wind direction sensor, and data acquisition and transmission equipment; The risk processing and feedback module includes a calculation and storage device; The alarm and adjustment module includes an alarm; The display module includes a display device.

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