CN117475570B - Radar monitoring intelligent early warning system suitable for forest fire prevention - Google Patents
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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Abstract
The invention relates to the technical field of forest fire prevention, and aims to solve the problems that in the existing mode of monitoring and early warning forest fire prevention, reasonable arrangement of monitoring equipment is difficult, monitoring coverage is insufficient, early judgment of forest fire danger cannot be achieved, and accurate early warning of forest fire danger cannot be achieved. According to the invention, the arrangement density of the radar monitors is effectively determined, the forest area is monitored in real time by utilizing the radar technology, and the fire detection and early warning are carried out on the forest in a data analysis mode, so that timely and accurate fire information is provided, and fire prevention and early control measures are enhanced, so that the occurrence and hazard of forest fires are effectively reduced.
Description
Technical Field
The invention relates to the technical field of forest fire prevention, in particular to an intelligent early warning system for radar monitoring applicable to forest fire prevention.
Background
Forest is an important natural resource and ecosystem on earth, and has important effects on maintaining the ecological balance of earth, protecting biodiversity and regulating climate. The fire prevention early warning can help discover and inhibit fire disaster early, reduce forest damage and protect ecological environment stability.
However, in the existing forest fire prevention monitoring and early warning mode, the monitoring and early warning effect is mainly dependent on the cognition and response of personnel to early warning. And the early warning is carried out after the dangerous case is happened in most cases, and the early monitoring analysis is lacking, so that the fire sign cannot be found earlier and the response cannot be quickly carried out;
if personnel do not process the early warning signal properly or unconsciously neglect, the effect of the forest fire prevention early warning system is greatly limited, and the timely notification of the dangerous case of the forest fire cannot be realized, so that the effect of forest fire prevention cannot be realized.
In the conventional arrangement of monitoring equipment for forest fire prevention monitoring and early warning, reasonable arrangement of the monitoring equipment is achieved, so that dangerous forest fire situations cannot be monitored accurately, fire details of a forest cannot be accurately mastered, and forest safety cannot be guaranteed.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
The invention aims to solve the problems that in the existing forest fire prevention monitoring and early warning mode, reasonable layout of monitoring equipment is difficult to achieve, monitoring coverage is insufficient, accurate monitoring and early warning of forest fire danger cannot be achieved, pre-judging analysis of forest fire danger cannot be achieved, forest safety cannot be guaranteed, the layout density of a radar monitor is effectively determined through comprehensive analysis of the basic condition state of a forest and the performance condition of a monitoring radar, a forest area is monitored in real time by utilizing a radar technology, fire detection and early warning are conducted on the forest in a data analysis mode, timely and accurate fire information is provided, fire prevention and early control measures are enhanced, accordingly occurrence and harm of forest fire are effectively reduced, and an intelligent radar monitoring early warning system suitable for forest fire prevention is provided.
The aim of the invention can be achieved by the following technical scheme: a radar monitoring intelligent early warning system suitable for forest fire prevention includes: the system comprises a data acquisition unit, a cloud database, a radar density layout analysis unit, a forest fire early warning unit, a forest fire pre-analysis unit and a display early warning terminal;
The data acquisition unit is used for acquiring basic parameter information of a forest, performance parameter information of a radar sensor, environmental parameter information and fire parameter information of the forest, and sending various information to the cloud database for storage;
the cloud database is also used for storing a density grade judging table of equipment layout and a fire trend spreading degree data table;
the radar density layout analysis unit is used for monitoring basic parameter information of the monitored forest and performance parameter information of the used radar sensor, so as to quantitatively analyze the layout density of the radar of the forest and output the layout density grade of the forest;
The forest fire early warning unit analyzes smoke concentration values in environmental parameter information of a forest according to the set arrangement density of radar sensors required by the forest, so as to mark a fire area of the forest, judge, analyze and process the fire, output the spreading degree of the fire trend corresponding to the fire area, display early warning description on the spreading degree of the fire trend through a control display center, or misjudge a signal of dangerous case, trigger a pre-analysis instruction and send the pre-analysis instruction to the forest fire pre-analysis unit;
The forest fire pre-analysis unit is used for monitoring environmental parameter information and plant state information of a forest according to the received pre-analysis instruction, so that early warning analysis is carried out on a fire trend state of the forest, a fire risk state of a corresponding area in the forest is clear, the fire risk state comprises a high risk area and a low risk area, and all areas marked as the high risk areas are sent to the display early warning terminal for early warning display.
Preferably, the monitoring of the basic parameter information of the monitored forest and the performance parameter information of the radar sensor used is carried out, and the specific monitoring process is as follows:
The method comprises the steps of acquiring a floor area value and a terrain complex value in basic parameter information of a monitored forest through a satellite sensing technology, calibrating the floor area value and the terrain complex value into av and cv respectively, performing calculation and analysis on two data, and according to a set data model: fdv =λ1×av+λ2×cv, thereby outputting a layout influence value fdv of the monitored forest, wherein λ1 and λ2 are normalization factors of the floor area value and the terrain complexity value, respectively, and λ1 and λ2 are natural numbers greater than 0;
the method comprises the steps of obtaining detection values, energy supply values and output values in performance parameter information of a used radar sensor, calibrating the detection values, the energy supply values and the output values as dct, esv and ov respectively, calculating and analyzing three items of data, and setting a data model: pdv =ρ1×dct+ρ2× esv +ρ3xov, thereby outputting the performance value pdv of the radar sensor used, where ρ1, ρ2, and ρ3 are the normalization factors of the detection value, the energy supply value, and the output value, respectively, and ρ1, ρ2, and ρ3 are natural numbers greater than 0.
Preferably, the quantitative analysis is performed on the layout density of the radar of the forest, and the specific analysis process is as follows:
Acquiring a monitored layout influence value of a forest and a performance value of a used radar sensor in real time, comprehensively analyzing the two items of data, and according to a set data model: the density layout coefficient dlc of the used radar sensor of the forest is output, wherein gamma 1 and gamma 2 are weight factor coefficients of a layout influence value and a performance value respectively, and gamma 1 and gamma 2 are natural numbers larger than 0;
Comparing and matching the density layout coefficients of the radar sensors used in the forest with a device layout density grade judging table stored in a cloud database, so as to obtain layout density grades of the forest, wherein each obtained density layout coefficient corresponds to one layout density grade, and the layout density grades comprise a primary layout density grade, a secondary layout density grade and a tertiary layout density grade;
if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M1 subunit areas, and sequentially setting M1 radar sensors in the M1 subunit areas;
if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M2 subunit areas, and sequentially setting M2 radar sensors in the M2 subunit areas;
And if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M3 subunit areas, and sequentially setting M3 radar sensors in the M3 subunit areas.
Preferably, the analysis is performed on the smoke concentration value in the environmental parameter information of the forest, and the specific analysis process is as follows:
According to the set arrangement density of the radar sensors required by the forest, the smoke concentration value in the environmental parameter information of the forest in the corresponding area is monitored in real time by each radar sensor, a smoke comparison threshold value of the smoke concentration value is set, and the smoke concentration value of each area is compared and analyzed with the preset smoke comparison threshold value;
If the smoke concentration value is larger than a preset smoke comparison threshold value, marking the corresponding area as a fire burning area, triggering a fire analysis instruction, performing fire judgment analysis processing on all the areas marked as the fire burning area according to the fire analysis instruction, outputting the fire trend spreading degree of the corresponding fire burning area according to the fire judgment analysis processing, and displaying and early warning explanation on the output fire trend spreading degree through a display early warning terminal;
otherwise, if the smoke concentration value is larger than the preset smoke comparison threshold value, a dangerous case misjudgment signal is generated, and the pre-analysis instruction is triggered and sent to the forest fire pre-analysis unit.
Preferably, the fire judgment analysis process comprises the following specific processing steps:
Acquiring flame height and flame brightness in the flame parameter information marked as the flame area in real time, comprehensively analyzing the two flame parameter data and the wind speed value of the environment where the flame area is located, and according to a set data model: frb = ω1×hgd+ω2×hld+ω3×fs, whereby a fire coefficient frb corresponding to the region of the fire is generated, wherein hgd is denoted as flame height, hld is denoted as flare luminance, ω1, ω2 and ω3 are weight factor coefficients of flame height, flare luminance and wind speed values, respectively, and ω1, ω2 and ω3 are natural numbers greater than 0;
and (3) comparing and matching the fire coefficients with a fire trend spreading degree data table stored in a cloud database, thereby obtaining the fire trend spreading degree corresponding to the burning fire area, wherein each obtained fire coefficient corresponds to one fire trend spreading degree, and the fire trend spreading degree comprises slow spreading, rapid spreading and violent spreading.
Preferably, the monitoring of the environmental parameter information and the plant status information of the forest specifically includes the following steps:
according to the set arrangement density of the radar sensors required by the forest, monitoring the temperature value, the humidity value and the wind speed value in the environmental parameter information of the forest in the corresponding area in real time through each radar sensor, calibrating the temperature value, the humidity value and the wind speed value as wd, sd and fs respectively, comprehensively analyzing three items of data, and according to a set data model: ftc=δ1×wd+δ2×sd+δ3×fs, thereby outputting an environmental coefficient ftc of each region of the forest, where δ1, δ2, and δ3 are error factor coefficients of a temperature value, a humidity value, and a wind speed value, respectively, and δ1, δ2, and δ3 are natural numbers greater than 0;
The vegetation density value, the withered branch density value and the flammable vegetation value in the plant state information of the corresponding area are monitored in real time, and are respectively calibrated into vd, sdc and szb, and the two items of data are calculated and analyzed according to a set data model: veg=σ1×vd+σ2×sdc+σ3× szb, thereby outputting vegetation coefficients veg of each region in the forest, wherein σ1, σ2 and σ3 are weight factor coefficients of a vegetation density value, a withered branch density value and a flammable vegetation value, respectively, and σ1, σ2 and σ3 are natural numbers greater than 0.
Preferably, the early warning analysis is performed on the forest fire trend state, and the specific analysis process is as follows:
Acquiring the environmental coefficient and the vegetation coefficient of each area of the forest in real time, comprehensively analyzing the two items of data, and according to the formula: tre=μ× (ftc+veg), whereby a fire trend coefficient tre of each region of the forest is output, where μ is a conversion factor coefficient for converting physical quantities of all data items into data coefficients of the same physical quantity, and μ is a natural number greater than 0;
setting a comparison threshold value of the fire trend coefficient, comparing and analyzing the fire trend coefficient of each area in the forest with a preset comparison threshold value, if the fire trend coefficient is greater than or equal to the preset comparison threshold value, calibrating the corresponding area in the forest as a high risk area, and if the fire trend coefficient is greater than or equal to the preset comparison threshold value, calibrating the corresponding area in the forest as a low risk area;
and sending all the areas marked as high risk areas to a display early warning terminal for early warning display.
The invention has the beneficial effects that:
According to the invention, the occupation area and the terrain complexity of the forest are analyzed, the forest basic condition state is clarified, powerful data support is provided for realizing the effective layout of the radar sensor, the performance state of the monitoring radar is clarified by combining the detection capability, the power supply capability and the data transmission capability of the radar, the reasonable density layout of the monitoring radar required by forest fire prevention is realized by adopting the data calibration, the data model analysis and the database substitution comparison mode, and the foundation is laid for realizing the accurate monitoring and early warning of forest fire danger;
According to the set arrangement density of the radar sensors required by the forest, the smoke concentration condition of the area is monitored in real time through the radar sensors, the fire condition of the forest is definitely judged by adopting a data comparison and division mode, and the fire trend spreading degree is definitely determined by adopting a formula calculation analysis and data comparison and matching mode in combination with each fire parameter, so that the forest fire early warning is accurately output, fire information can be timely provided for related personnel, and further disaster relief personnel can make emergency treatment;
the temperature value, the humidity value and the wind speed value in the environmental parameter information of the forest are monitored in real time through the radar sensor, all environmental parameters are comprehensively analyzed, and the risk assessment and the prediction of the forest fire are realized by adopting a data comparison analysis mode, so that the occurrence and the harm of the forest fire are effectively reduced.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention is a radar monitoring intelligent early warning system suitable for forest fire prevention, comprising: the system comprises a data acquisition unit, a cloud database, a radar density layout analysis unit, a forest fire early warning unit, a forest fire pre-analysis unit and a display early warning terminal.
The data acquisition unit is used for acquiring basic parameter information of the forest, performance parameter information of the radar sensor, environmental parameter information of the forest and fire parameter information, and sending various information to the cloud database for storage.
The cloud database is also used for storing a density grade judging table of equipment layout and a fire trend spreading degree data table.
The radar density layout analysis unit is used for monitoring basic parameter information of the monitored forest and performance parameter information of the used radar sensor, and the specific monitoring process is as follows:
The method comprises the steps of acquiring a floor area value and a terrain complex value in basic parameter information of a monitored forest through a satellite sensing technology, calibrating the floor area value and the terrain complex value into av and cv respectively, performing calculation and analysis on two data, and according to a set data model: fdv =λ1×av+λ2×cv, thereby outputting a layout influence value fdv of the monitored forest, wherein λ1 and λ2 are normalization factors of the floor area value and the terrain complexity value, respectively, and λ1 and λ2 are natural numbers larger than 0, and the normalization factors are used for representing coefficients for converting various data in the data model into a dimensionless form;
The terrain complexity value refers to the form of the forest and the complexity of the terrain, and is generally represented by the numerical value of the height difference in unit area, and the larger the duty ratio of the height difference in the forest terrain in unit area is, the more the form and the terrain of the forest are described;
The method comprises the steps of obtaining detection values, energy supply values and output values in performance parameter information of a used radar sensor, calibrating the detection values, the energy supply values and the output values as dct, esv and ov respectively, calculating and analyzing three items of data, and setting a data model: pdv =ρ1×dct+ρ2× esv +ρ3xov, thereby outputting the performance value pdv of the radar sensor used, wherein ρ1, ρ2 and ρ3 are the normalization factors of the detection value, the energy supply value and the output value, respectively, and ρ1, ρ2 and ρ3 are natural numbers greater than 0;
The detection value refers to the range which can be detected by the radar sensor, the energy supply value refers to the power supply capability which can be provided for the radar sensor when the radar sensor continuously works, the power supply capability is generally measured by the power supply time length, and the output value refers to the distance of the radar sensor for data transmission;
the quantitative analysis is carried out on the layout density of the forest radar, and the specific analysis process is as follows:
Acquiring a monitored layout influence value of a forest and a performance value of a used radar sensor in real time, comprehensively analyzing the two items of data, and according to a set data model: the density layout coefficient dlc of the used radar sensor of the forest is output, wherein gamma 1 and gamma 2 are weight factor coefficients of a layout influence value and a performance value respectively, gamma 1 and gamma 2 are natural numbers larger than 0, and the weight factor coefficients are used for balancing the duty ratio weight of each item of data in formula calculation, so that the accuracy of a calculation result is promoted;
Comparing and matching the density layout coefficients of the radar sensors used in the forest with a device layout density grade judging table stored in a cloud database, so as to obtain layout density grades of the forest, wherein each obtained density layout coefficient corresponds to one layout density grade, and the layout density grades comprise a primary layout density grade, a secondary layout density grade and a tertiary layout density grade;
if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M1 subunit areas, and sequentially setting M1 radar sensors in the M1 subunit areas;
if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M2 subunit areas, and sequentially setting M2 radar sensors in the M2 subunit areas;
If the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M3 subunit areas, and sequentially setting M3 radar sensors in the M3 subunit areas;
Wherein, the specific values of m1 > m2 > m3, M, m1, m2, m3 are set specifically in specific cases by the person skilled in the art.
The forest fire early warning unit analyzes the smoke concentration value in the environmental parameter information of the forest according to the set arrangement density of the radar sensors required by the forest, and the specific analysis process is as follows:
According to the set arrangement density of the radar sensors required by the forest, the smoke concentration value in the environmental parameter information of the forest in the corresponding area is monitored in real time by each radar sensor, a smoke comparison threshold value of the smoke concentration value is set, and the smoke concentration value of each area is compared and analyzed with the preset smoke comparison threshold value;
if the smoke concentration value is larger than the preset smoke comparison threshold value, marking the corresponding area as a fire burning area, triggering a fire analysis instruction, and performing fire judgment analysis processing on all the areas marked as the fire burning areas according to the fire analysis instruction, wherein the specific processing process is as follows:
Acquiring flame height and flame brightness in the flame parameter information marked as the flame area in real time, comprehensively analyzing the two flame parameter data and the wind speed value of the environment where the flame area is located, and according to a set data model: frb = ω1×hgd+ω2×hld+ω3×fs, whereby a fire coefficient frb corresponding to the region of the fire is generated, wherein hgd is denoted as flame height, hld is denoted as flare luminance, ω1, ω2 and ω3 are weight factor coefficients of flame height, flare luminance and wind speed values, respectively, and ω1, ω2 and ω3 are natural numbers greater than 0;
Performing comparison matching analysis on the fire coefficients and a fire trend spreading degree data table stored in a cloud database, thereby obtaining the fire trend spreading degree corresponding to a burning area, wherein each obtained fire coefficient corresponds to one fire trend spreading degree, and the fire trend spreading degree comprises slow spreading, rapid spreading and violent spreading;
the output fire trend spreading degree is displayed and early-warning description is carried out through a display early-warning terminal;
otherwise, if the smoke concentration value is larger than the preset smoke comparison threshold value, a dangerous case misjudgment signal is generated, and the pre-analysis instruction is triggered and sent to the forest fire pre-analysis unit.
The forest fire pre-analysis unit is used for monitoring environmental parameter information and plant state information of the forest according to the received pre-analysis instruction, and the specific monitoring process is as follows:
According to the set arrangement density of the radar sensors required by the forest, monitoring the temperature value, the humidity value and the wind speed value in the environmental parameter information of the forest in the corresponding area in real time through each radar sensor, calibrating the temperature value, the humidity value and the wind speed value as wd, sd and fs respectively, comprehensively analyzing three items of data, and according to a set data model: ftc=δ1×wd+δ2×sd+δ3×fs, thereby outputting an environmental coefficient ftc of each region of the forest, where δ1, δ2, and δ3 are error factor coefficients of a temperature value, a humidity value, and a wind speed value, respectively, and δ1, δ2, and δ3 are natural numbers greater than 0, and the error factor coefficients are used to improve measurement accuracy of the temperature value, the humidity value, and the wind speed value in each measured value, so as to implement accuracy of formula calculation;
The vegetation density value, the withered branch density value and the flammable vegetation value in the plant state information of the corresponding area are monitored in real time, and are respectively calibrated into vd, sdc and szb, and the two items of data are calculated and analyzed according to a set data model: veg=σ1×vd+σ2×sdc+σ3× szb, thereby outputting vegetation coefficients veg of each region in the forest, wherein σ1, σ2 and σ3 are weight factor coefficients of a vegetation density value, a withered branch density value and a flammable vegetation value, respectively, and σ1, σ2 and σ3 are natural numbers greater than 0;
It should be noted that, the vegetation density value refers to the ratio of the number of growing vegetation contained in the unit area, the dead branch density value refers to the density value of dead branch fallen leaves accumulated in the unit area, which is generally measured by the stacking thickness, and the flammable vegetation value refers to the ratio of the number of flammable vegetation contained in the unit area;
Therefore, early warning analysis is carried out on the forest fire trend state, and the specific analysis process is as follows:
Acquiring the environmental coefficient and the vegetation coefficient of each area of the forest in real time, comprehensively analyzing the two items of data, and according to the formula: tre=μ× (ftc+veg), whereby a fire trend coefficient tre of each region of the forest is output, where μ is a conversion factor coefficient for converting physical quantities of all data items into data coefficients of the same physical quantity, and μ is a natural number greater than 0;
setting a comparison threshold value of the fire trend coefficient, comparing and analyzing the fire trend coefficient of each area in the forest with a preset comparison threshold value, if the fire trend coefficient is greater than or equal to the preset comparison threshold value, calibrating the corresponding area in the forest as a high risk area, and if the fire trend coefficient is greater than or equal to the preset comparison threshold value, calibrating the corresponding area in the forest as a low risk area;
and sending all the areas marked as high risk areas to a display early warning terminal for early warning display.
When the system is used, the forest basic condition state is defined by analyzing the occupation area and the terrain complexity of the forest, powerful data support is provided for realizing the effective layout of the radar sensor, the performance state of the monitoring radar is defined by combining the detection capability, the power supply capability and the data transmission capability of the radar, and the reasonable density layout of the monitoring radar required by forest fire prevention is realized by adopting the data calibration, the data model analysis and the database substitution comparison mode, and the foundation is laid for realizing the accurate monitoring and early warning of forest fire danger;
According to the set arrangement density of the radar sensors required by the forest, the smoke concentration condition of the area is monitored in real time through the radar sensors, the fire condition of the forest is definitely judged by adopting a data comparison and division mode, and the fire trend spreading degree is definitely determined by adopting a formula calculation analysis and data comparison and matching mode in combination with each fire parameter, so that the forest fire early warning is accurately output, fire information can be timely provided for related personnel, and further disaster relief personnel can make emergency treatment;
the temperature value, the humidity value and the wind speed value in the environmental parameter information of the forest are monitored in real time through the radar sensor, all environmental parameters are comprehensively analyzed, and the risk assessment and the prediction of the forest fire are realized by adopting a data comparison analysis mode, so that the occurrence and the harm of the forest fire are effectively reduced.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (5)
1. Radar monitoring intelligent early warning system suitable for forest fire prevention, characterized in that includes:
The data acquisition unit is used for acquiring basic parameter information of the forest, performance parameter information of the radar sensor, environmental parameter information of the forest and fire parameter information, and sending various information to the cloud database for storage;
the cloud database is also used for storing a density grade judging table of equipment layout and a fire trend spreading degree data table;
The radar density layout analysis unit is used for monitoring the basic parameter information of the monitored forest and the performance parameter information of the used radar sensor, so as to quantitatively analyze the layout density of the radar of the forest and output the layout density grade of the forest according to the quantitative analysis;
The forest fire early warning unit is used for marking a fire burning area of the forest and carrying out fire judgment analysis processing on the fire burning area according to the set arrangement density of the radar sensors required by the forest, outputting the fire trend spreading degree corresponding to the fire burning area, displaying early warning explanation on the fire trend spreading degree through the control display center, or triggering a pre-analysis instruction according to a dangerous situation misjudgment signal, and sending the pre-analysis instruction to the forest fire pre-analysis unit;
The forest fire pre-analysis unit is used for monitoring environmental parameter information and plant state information of a forest according to the received pre-analysis instruction, so as to perform early warning analysis on a fire trend state of the forest, and accordingly, a fire risk state of a corresponding area in the forest is defined, the fire risk state comprises a high risk area and a low risk area, and all areas marked as the high risk areas are sent to the display early warning terminal for early warning display;
the monitoring of the basic parameter information of the monitored forest and the performance parameter information of the used radar sensor comprises the following specific monitoring process:
The method comprises the steps of acquiring a floor area value and a terrain complex value in basic parameter information of a monitored forest through a satellite sensing technology, calibrating the floor area value and the terrain complex value into av and cv respectively, performing calculation and analysis on two data, and according to a set data model: fdv =λ1×av+λ2×cv, thereby outputting a layout influence value fdv of the monitored forest, wherein λ1 and λ2 are normalization factors of the floor area value and the terrain complexity value, respectively, and λ1 and λ2 are natural numbers greater than 0;
The method comprises the steps of obtaining detection values, energy supply values and output values in performance parameter information of a used radar sensor, calibrating the detection values, the energy supply values and the output values as dct, esv and ov respectively, calculating and analyzing three items of data, and setting a data model: pdv =ρ1×dct+ρ2× esv +ρ3xov, thereby outputting the performance value pdv of the radar sensor used, wherein ρ1, ρ2 and ρ3 are the normalization factors of the detection value, the energy supply value and the output value, respectively, and ρ1, ρ2 and ρ3 are natural numbers greater than 0;
The quantitative analysis is carried out on the layout density of the forest radar, and the specific analysis process is as follows:
Acquiring a monitored layout influence value of a forest and a performance value of a used radar sensor in real time, comprehensively analyzing the two items of data, and according to a set data model: the density layout coefficient dlc of the used radar sensor of the forest is output, wherein gamma 1 and gamma 2 are weight factor coefficients of a layout influence value and a performance value respectively, and gamma 1 and gamma 2 are natural numbers larger than 0;
Comparing and matching the density layout coefficients of the radar sensors used in the forest with a device layout density grade judging table stored in a cloud database, so as to obtain layout density grades of the forest, wherein each obtained density layout coefficient corresponds to one layout density grade, and the layout density grades comprise a primary layout density grade, a secondary layout density grade and a tertiary layout density grade;
if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M1 subunit areas, and sequentially setting M1 radar sensors in the M1 subunit areas;
if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M2 subunit areas, and sequentially setting M2 radar sensors in the M2 subunit areas;
And if the arrangement density level of the forest is the first-level arrangement density level, equally dividing the forest area with the area M into M3 subunit areas, and sequentially setting M3 radar sensors in the M3 subunit areas.
2. The intelligent early warning system for radar monitoring applicable to forest fire prevention according to claim 1, wherein the analysis of the smoke concentration value in the environmental parameter information of the forest is carried out by the following specific analysis process:
According to the set arrangement density of the radar sensors required by the forest, the smoke concentration value in the environmental parameter information of the forest in the corresponding area is monitored in real time by each radar sensor, a smoke comparison threshold value of the smoke concentration value is set, and the smoke concentration value of each area is compared and analyzed with the preset smoke comparison threshold value;
If the smoke concentration value is larger than a preset smoke comparison threshold value, marking the corresponding area as a fire burning area, triggering a fire analysis instruction, performing fire judgment analysis processing on all the areas marked as the fire burning area according to the fire analysis instruction, outputting the fire trend spreading degree of the corresponding fire burning area according to the fire judgment analysis processing, and displaying and early warning explanation on the output fire trend spreading degree through a display early warning terminal;
otherwise, if the smoke concentration value is larger than the preset smoke comparison threshold value, a dangerous case misjudgment signal is generated, and the pre-analysis instruction is triggered and sent to the forest fire pre-analysis unit.
3. The intelligent early warning system for radar monitoring suitable for forest fire prevention according to claim 2, wherein the fire judgment analysis processing comprises the following specific processing procedures:
Acquiring flame height and flame brightness in the flame parameter information marked as the flame area in real time, comprehensively analyzing the two flame parameter data and the wind speed value of the environment where the flame area is located, and according to a set data model: frb = ω1×hgd+ω2×hld+ω3×fs, whereby a fire coefficient frb corresponding to the region of the fire is generated, wherein hgd is denoted as flame height, hld is denoted as flare luminance, ω1, ω2 and ω3 are weight factor coefficients of flame height, flare luminance and wind speed values, respectively, and ω1, ω2 and ω3 are natural numbers greater than 0;
and (3) comparing and matching the fire coefficients with a fire trend spreading degree data table stored in a cloud database, thereby obtaining the fire trend spreading degree corresponding to the burning fire area, wherein each obtained fire coefficient corresponds to one fire trend spreading degree, and the fire trend spreading degree comprises slow spreading, rapid spreading and violent spreading.
4. The intelligent early warning system for radar monitoring applicable to forest fire prevention according to claim 1, wherein the monitoring of environmental parameter information and plant state information of the forest is carried out by the following specific monitoring process:
According to the set arrangement density of the radar sensors required by the forest, monitoring the temperature value, the humidity value and the wind speed value in the environmental parameter information of the forest in the corresponding area in real time through each radar sensor, calibrating the temperature value, the humidity value and the wind speed value as wd, sd and fs respectively, comprehensively analyzing three items of data, and according to a set data model: ftc=δ1×wd+δ2×sd+δ3×fs, thereby outputting an environmental coefficient ftc of each region of the forest, where δ1, δ2, and δ3 are error factor coefficients of a temperature value, a humidity value, and a wind speed value, respectively, and δ1, δ2, and δ3 are natural numbers greater than 0;
the vegetation density value, the withered branch density value and the flammable vegetation value in the plant state information of the corresponding area are monitored in real time, and are respectively calibrated into vd, sdc and szb, and the two items of data are calculated and analyzed according to a set data model: veg=σ1×vd+σ2×sdc+σ3× szb, thereby outputting vegetation coefficients veg of each region in the forest, wherein σ1, σ2 and σ3 are weight factor coefficients of a vegetation density value, a withered branch density value and a flammable vegetation value, respectively, and σ1, σ2 and σ3 are natural numbers greater than 0.
5. The intelligent early warning system for radar monitoring applicable to forest fire prevention according to claim 1, wherein the early warning analysis is performed on the forest fire trend state, and the specific analysis process is as follows:
acquiring the environmental coefficient and the vegetation coefficient of each area of the forest in real time, comprehensively analyzing the two items of data, and according to the formula: tre=μ× (ftc+veg), whereby a fire trend coefficient tre of each region of the forest is output, where μ is a conversion factor coefficient for converting physical quantities of all data items into data coefficients of the same physical quantity, and μ is a natural number greater than 0;
setting a comparison threshold value of the fire trend coefficient, comparing and analyzing the fire trend coefficient of each area in the forest with a preset comparison threshold value, if the fire trend coefficient is greater than or equal to the preset comparison threshold value, calibrating the corresponding area in the forest as a high risk area, and if the fire trend coefficient is greater than or equal to the preset comparison threshold value, calibrating the corresponding area in the forest as a low risk area;
and sending all the areas marked as high risk areas to a display early warning terminal for early warning display.
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