Fire danger forecasting method based on fire danger indexes

Abstract

The invention discloses a fire danger forecasting method based on a fire danger index, and relates to the technical field of fire danger forecasting, and the method comprises the following steps: obtaining a meteorological factor parameter and an index in combination with the monitoring point data of a meteorological station in advance, the indexes at least comprise a daily maximum temperature index, a daily minimum relative humidity index, a daily maximum wind speed index, a continuous rain-free day index and daily rainfall; and respectively calculating a fire danger index HX1, a fire danger index HX2 and a fire danger index HX3 according to the meteorological factor parameters and the index indexes. According to the method, the indexes are quantitatively analyzed and calculated, the fire danger index is a specific numerical value, fire risks of different regions and different time periods can be quantitatively compared, the potential danger degree of forest fire occurring in one region can be accurately reflected, the fire prevention work efficiency is improved, and a scientific basis is provided for fire prevention decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of fire risk forecasting, and in particular to a fire risk forecasting method based on a fire risk index. Background Art

[0002] Meteorological conditions are closely related to the occurrence and development of forest fires. Meteorological data can be collected in real time by setting up meteorological stations in and around forest areas and using meteorological sensors such as temperature, humidity, precipitation, wind speed, and wind direction. For example, high temperature, low humidity, and strong winds can easily cause forest fires. By monitoring these meteorological elements, basic data can be provided for fire prediction. Professional meteorological monitoring systems can provide accurate meteorological information to forest fire prevention departments to help them determine the possibility of forest fires. In some key forest areas, the meteorological department will cooperate with the forestry department to establish a special forest meteorological monitoring station network to better predict and prevent fires.

[0003] At present, for forest fire prediction, on the one hand, video monitoring is used to install cameras at key locations in the forest area, and transmit video signals to the monitoring center through wired or wireless communication networks. Monitoring personnel can view the situation in the forest area in real time, and issue an alarm in time when abnormal conditions such as smoke and flames are found; but video monitoring requires the installation of a large number of cameras in the forest area to achieve full coverage. In some remote areas with complex terrain, it is difficult and costly to install cameras, resulting in limitations in the coverage of video monitoring. In addition, the camera's field of view will also be blocked by trees, mountains, etc., affecting the monitoring effect. On the other hand, machine learning algorithms are used for prediction, but the interpretability of machine learning algorithms is poor. Although some forest fire prediction models based on machine learning have high prediction accuracy, the internal mechanism of the model is relatively complex and the interpretability is poor. This makes it difficult for forestry departments to understand how the prediction results of the model are generated when using these models, thus affecting the trust in the model and the application effect.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a fire risk forecasting method based on the fire risk index to overcome the above technical problems existing in the existing related art.

[0006] The technical solution of the present invention is achieved in this way:

[0007] A fire risk forecasting method based on a fire risk index comprises the following steps:

[0008] Combined with the data of the meteorological observation station monitoring points in advance, meteorological factor parameters and index indexes are obtained, wherein the index indexes at least include: daily maximum temperature index, daily minimum relative humidity index, daily maximum wind speed index, continuous rainless day index and daily rainfall;

[0009] According to the meteorological factor parameters and index index, fire danger index HX1, fire danger index HX2 and fire danger index HX3 are calculated respectively;

[0010] A fire risk level comparison table is preset, and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are compared with the fire risk level comparison table to determine the corresponding fire risk level;

[0011] Check the correlation values ​​between the actual fire risk level of the previous cycle (past 7 days) and the fire risk index HX1, fire risk index HX2 and fire risk index HX3, and select the fire risk index corresponding to the correlation value that meets the preset judgment rules as the predicted fire risk warning for the next cycle.

[0012] The fire risk level comparison table divides the forest fire risk meteorological index into 5 levels from low to high according to the percentile method, expressed as:

[0013]

[0014] The fire risk index HX1 is calculated as follows:

[0015] HX1=(T+H+W+DR)*Cr*Cs;

[0016] Among them, T is the daily maximum temperature index, taking the grid forecast result; H is the daily minimum relative humidity index, taking the grid h forecast result, Cs is the surface correction coefficient (using the forest coverage rate in the land type of the geographic information factor, taking 1 in forested areas and 0 in other areas), Cr is the precipitation correction coefficient, expressed as:

[0017]

[0018] Where W is the daily maximum wind speed index, and the forecast result of grid point w is expressed as:

[0019]

[0020] Wherein, D represents the continuous rainless day index, expressed as: D = d*5 (d ≥ 10);

[0021] Among them, R is the daily rainfall, and the forecast result of grid point r is expressed as:

[0022]

[0023] Among them, the fire risk index HX2 is calculated as:

[0024] HX2=(0.3*C1+0.7*C2)*Cr*Cs;

[0025] Among them, C1 and C2 are term indexes, 0.3 and 0.7 are coefficient values, Cs is the surface correction coefficient (here the forest coverage rate in the land type of the geographic information factor is used, 1 is taken for forested areas, and 0 is taken for other areas), Cr is the precipitation correction coefficient, and the value is 1 or 0, expressed as:

[0026]

[0027] Among them, the term index C1 is expressed as:

[0028] C1=IT+IH+IV+ID;

[0029] Among them, IT is the daily maximum temperature index, and the forecast result of grid point t is expressed as:

[0030]

[0031] Where IH is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as:

[0032]

[0033] Among them, IV is the daily maximum wind speed index, and the numerical value w is used as the forecast result, which is expressed as:

[0034]

[0035] Among them, ID is the continuous rainless day index, taking the continuous rainless day d value, expressed as:

[0036]

[0037] Among them, the term index C2 is expressed as:

[0038] C2=IpT+IpH+IpV+IpD

[0039] Among them, IpT represents the daily maximum temperature index, and the forecast result of grid point t is expressed as:

[0040]

[0041] Among them, IpH is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as:

[0042]

[0043] Among them, IpV is the daily maximum wind speed index, and the numerical value w is used as the forecast result, which is expressed as:

[0044]

[0045] Among them, IpD is the continuous rainless day index, which is expressed as:

[0046]

[0047] The calculation of the fire risk index HX3 includes the following steps:

[0048] Pre-fetch supply index I FED , expressed as:

[0049] I FED =1.275D 0.987 +exp(0.0338t-0.0345H+0.0234V);

[0050] Among them, t is the daily maximum temperature (unit: °C), H is the daily minimum relative humidity (unit: %), and V is the daily average wind speed (unit: km / h);

[0051] The drought factor D is expressed as

[0052]

[0053] Where I is I KBD The index of (unit: mm), N is the number of consecutive days without precipitation, and R is the daily precipitation (unit: mm);

[0054] I KBD It is expressed as:

[0055] I KBD =Q n-1 +dQ n ;

[0056] Among them, dQ n is the daily evaporation of soil moisture, Q n-1 is the cumulative loss of forest soil moisture on the previous day, that is, the difference in soil moisture at a depth of 10 cm between the n-1 day and the n-2 day, expressed as:

[0057] Q′ n-1 =Q n-1 -r n ;

[0058] Among them, Q′ n-1 is the residual after deducting the net precipitation from the accumulated loss of forest soil moisture on the previous day, r n is the net precipitation;

[0059] Set the daily evaporation of soil moisture dQn It is a function of the daily maximum temperature t and the annual average precipitation R (in), expressed as:

[0060]

[0061] Get the fire risk index HX3, expressed as:

[0062] HX3=I FED *Cr*Cs;

[0063] Among them, Cs is the surface correction coefficient (using the forest coverage rate in the land type of the geographic information factor, 1 is taken for forested areas and 0 is taken for other areas), Cr is the precipitation correction coefficient, and the value is 1 or 0, expressed as:

[0064]

[0065] The checking of the correlation values ​​between the actual fire risk level of the previous cycle and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 respectively comprises the following steps:

[0066] The actual fire risk level of the previous cycle is pre-calibrated as y = {y1, y2, y3, y4, y5, y6, y7}, where y i represents the number of fire hazards on the i-th day; calibrate each fire hazard index j (j = HX1, HX2, HX3), there is a sequence x j ={x j1 ,x j2 ,x j3 ,x j4 ,x j5 ,x j6 ,x j7}, where xji represents the value of the j-th fire danger index on the i-th day;

[0067] Calculate the mean of y, expressed as:

[0068] Calculate the mean value of the fire danger index j, expressed as:

[0069] Calculate the covariance, expressed as:

[0070] Calculate the standard deviation of y, expressed as:

[0071] x i The standard deviation of is expressed as:

[0072] Get the relevant values ​​corresponding to the fire risk index j respectively, expressed as:

[0073] Beneficial effects of the present invention:

[0074] The present invention obtains meteorological factor parameters and index indexes by combining the data of the monitoring points of the meteorological observation station, wherein the index indexes at least include: the daily maximum temperature index, the daily minimum relative humidity index, the daily maximum wind speed index, the continuous rainless day index and the daily rainfall; the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are calculated respectively according to the meteorological factor parameters and the index index; a fire risk level comparison table is preset, and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are compared with the fire risk level comparison table respectively to determine the corresponding fire risk level, and realize the quantitative analysis and calculation of the index index. The fire risk index is a specific numerical value, which can quantitatively compare the fire risks in different regions and different time periods, and can more accurately reflect the potential danger degree of forest fires in a region, so that the relevant departments can more intuitively understand the fire risk conditions in various regions, so as to allocate fire prevention resources and forces in a targeted manner, improve the efficiency of fire prevention work, and provide a scientific basis for fire prevention decision-making.

[0075] At the same time, short-term fire risk forecasts can be made based on real-time data, and medium- and long-term fire risk forecasts can be made by combining historical data and long-term meteorological trends. This helps relevant departments to formulate long-term fire prevention plans and strategies, and make preparations in advance for periods of high fire risk. According to the fire risk index, the fire prevention department can reasonably allocate human, material and financial resources. In areas with a high fire risk index, the number of patrols can be increased, more fire checkpoints can be set up, and more fire-fighting equipment can be equipped; in areas with a low fire risk index, resource input can be appropriately reduced, thereby improving resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0077] Figure 1 The figure is a flow chart of a fire risk forecasting method based on fire risk index according to an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the accompanying 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 belong to the scope of protection of the present invention.

[0079] According to an embodiment of the present invention, a fire risk forecasting method based on a fire risk index is provided.

[0080] like Figure 1 As shown, the fire risk forecasting method based on the fire risk index according to an embodiment of the present invention comprises the following steps:

[0081] Combined with the data of the meteorological observation station monitoring points in advance, meteorological factor parameters and index indexes are obtained, wherein the index indexes at least include: daily maximum temperature index, daily minimum relative humidity index, daily maximum wind speed index, continuous rainless day index and daily rainfall;

[0082] According to the meteorological factor parameters and index index, fire danger index HX1, fire danger index HX2 and fire danger index HX3 are calculated respectively;

[0083] A fire risk level comparison table is preset, and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are compared with the fire risk level comparison table to determine the corresponding fire risk level;

[0084] Check the correlation values ​​between the actual fire risk level of the previous cycle (past 7 days) and the fire risk index HX1, fire risk index HX2 and fire risk index HX3, and select the fire risk index corresponding to the correlation value that meets the preset judgment rules as the predicted fire risk warning for the next cycle.

[0085] In this technical solution, the temperature index, a higher temperature will make combustibles dry, increasing the possibility of fire. Generally speaking, the higher the temperature, the higher the fire risk index. The corresponding relationship between temperature and fire risk index can be determined based on historical data and experimental research. For example, when the temperature exceeds a certain threshold (such as 30°C), the fire risk index begins to rise rapidly.

[0086] Among them, humidity index, humidity is an important factor affecting the moisture content of combustibles. The lower the humidity, the drier the combustibles are and the easier they are to burn. It is usually measured by relative humidity. The lower the relative humidity, the higher the fire risk index. For example, when the relative humidity is below 30%, the fire risk level may reach a high level.

[0087] In addition, the rainless day index and daily rainfall, precipitation can increase the moisture content of combustibles and reduce the risk of fire. Recent precipitation and the number of consecutive days without precipitation are important considerations. If there is no precipitation for many consecutive days, the fire risk index will gradually increase; and after a large rainfall, the fire risk index will significantly decrease. Determine the degree of influence of precipitation on the fire risk index based on the climate characteristics and vegetation types of different regions.

[0088] In addition, wind speed index: wind speed will affect the occurrence and spread of fire. Strong wind will rapidly expand the fire and increase the difficulty of fire fighting. Generally, the wind force level is converted into the corresponding wind meteorological index. The stronger the wind force, the higher the wind meteorological index, and the fire risk index will increase accordingly.

[0089] Among them, the fire danger level comparison table divides the forest fire danger meteorological index into 5 levels from low to high according to the percentile method, expressed as:

[0090]

[0091] The fire risk level comparison table is shown in Table 1, as follows:

[0092] Relative ranking / % grade Danger level Flammability Extent of spread (95,100) 5 Very high Very flammable Very easy to spread [90,95] 4 high Easy to burn Easy to spread (75,90) 3 Higher More flammable Easier to spread [60,75] 2 Moderate Can burn Can spread slowly (0,60) 1 Low Not easy to burn Not easy to spread

[0093] Table 1 Fire hazard level comparison table

[0094] In addition, the fire risk index HX1 is calculated as:

[0095] HX1=(T+H+W+DR)*Cr*Cs;

[0096] Among them, T is the daily maximum temperature index, taking the grid forecast result; H is the daily minimum relative humidity index, taking the grid h forecast result, Cs is the surface correction coefficient (using the forest coverage rate in the land type of the geographic information factor, taking 1 in forest-covered areas and 0 in other areas), Cr is the precipitation correction coefficient,

[0097] Among them, T is the daily maximum temperature index, and the grid point forecast results are shown in Table 2:

[0098]

[0099]

[0100] Table 2 Daily maximum temperature reference table, where H is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as:

[0101]

[0102] Where W is the daily maximum wind speed index, and the forecast result of grid point w is expressed as:

[0103]

[0104] Wherein, D represents the continuous rainless day index, expressed as: D = d*5 (d ≥ 10);

[0105] In this technical solution, a daily rainfall < 1 is considered a rainless day, and consecutive rainless days d must be calculated for more than 10 days. If there is no historical situation, the predicted daily rainfall is used to determine and accumulate consecutive rainless days d.

[0106] Among them, R is the daily rainfall, and the forecast result of grid point r is expressed as:

[0107]

[0108]

[0109] In addition, the fire danger index HX2 is calculated and expressed as:

[0110] HX2=(0.3*C1+0.7*C2)*Cr*Cs;

[0111] Among them, C1 and C2 are term indexes, 0.3 and 0.7 are coefficient values, Cs is the surface correction coefficient (here the forest coverage rate in the land type of the geographic information factor is used, 1 is taken for forested areas, and 0 is taken for other areas), Cr is the precipitation correction coefficient, and the value is 1 or 0, expressed as:

[0112]

[0113] In addition, for the above C1 term index, it is expressed as:

[0114] C1=IT+IH+IV+ID

[0115] Among them, IT is the daily maximum temperature index, and the forecast result of grid point t is expressed as:

[0116]

[0117] Where IH is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as:

[0118]

[0119] Among them, IV is the daily maximum wind speed index, and the numerical value w is used as the forecast result, which is expressed as:

[0120]

[0121] Among them, ID is the continuous rainless day index, taking the continuous rainless day d value, expressed as:

[0122]

[0123] In addition, for the above C2 term index, it is expressed as:

[0124] C2=IpT+IpH+IpV+IpD

[0125] Among them, IpT represents the daily maximum temperature index, and the forecast result of grid point t is expressed as:

[0126]

[0127] Among them, IpH is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as:

[0128]

[0129] Among them, IpV is the daily maximum wind speed index, and the numerical value w is used as the forecast result, which is expressed as:

[0130]

[0131] Among them, IpD is the continuous rainless day index, which is expressed as:

[0132]

[0133] In addition, the calculation of the fire risk index HX3 includes the following steps:

[0134] Pre-fetch supply index I FED , expressed as:

[0135] I FED =1.275D 0.987 +exp(0.0338t-0.0345H+0.0234V);

[0136] Among them, t is the daily maximum temperature (unit: °C), H is the daily minimum relative humidity (unit: %), and V is the daily average wind speed (unit: km / h);

[0137] The drought factor D is expressed as

[0138]

[0139] Where I is I KBD The index of (unit: mm), N is the number of consecutive days without precipitation, and R is the daily precipitation (unit: mm);

[0140] I KBD It is expressed as:

[0141] I KBD =Q n-1 +dQ n ;

[0142] Among them, dQ n is the daily evaporation of soil moisture, Q n-1is the cumulative loss of forest soil moisture on the previous day, that is, the difference in soil moisture at a depth of 10 cm between the n-1 day and the n-2 day, expressed as:

[0143] Q′ n-1 =Q n-1 -r n ;

[0144] Among them, Q′ n-1 is the residual after deducting the net precipitation from the accumulated loss of forest soil moisture on the previous day, r n is the net precipitation, i.e., the soil moisture after deducting the net precipitation on day n (unit: mm) from the difference in soil moisture at a depth of 10 cm between day n-1 and day n-2;

[0145] Set the daily evaporation of soil moisture dQ n It is a function of the daily maximum temperature t and the annual average precipitation R (in), expressed as:

[0146]

[0147] Get the fire risk index HX3, expressed as:

[0148] HX3=I FED *Cr*Cs;

[0149] Among them, Cs is the surface correction coefficient (using the forest coverage rate in the land type of the geographic information factor, 1 is taken for forested areas and 0 is taken for other areas), Cr is the precipitation correction coefficient, and the value is 1 or 0, expressed as:

[0150]

[0151] In addition, the checking of the correlation values ​​between the actual fire risk level of the previous cycle and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 respectively includes the following steps:

[0152] The actual fire risk level of the previous cycle is pre-calibrated as y = {y1, y2, y3, y4, y5, y6, y7}, where y i represents the number of fire hazards on the i-th day; calibrate each fire hazard index j (j = HX1, HX2, HX3), there is a sequence x j ={x j1 ,x j2 ,x j3 ,x j4 ,x j5 ,x j6 ,x j7}, where xji represents the value of the j-th fire danger index on the i-th day;

[0153] Calculate the mean of y, expressed as:

[0154] Calculate the mean value of the fire danger index j, expressed as:

[0155] Calculate the covariance, expressed as:

[0156] Calculate the standard deviation of y, expressed as:

[0157] Calculate x i The standard deviation of is expressed as:

[0158] Get the relevant values ​​corresponding to the fire risk index j respectively, expressed as:

[0159] In addition, the preset judgment rules include the following steps:

[0160] Get the relevant values ​​corresponding to the fire risk index j | r j |;

[0161] If | r j The closer it is to the judgment threshold, the value is 1, which means that the linear correlation between the current fire risk index j and the actual fire risk level is stronger, and the current fire risk index j is used as the next period prediction fire risk warning.

[0162] In summary, with the help of the above technical solution of the present invention, the following effects can be achieved:

[0163] The present invention obtains meteorological factor parameters and index indexes by combining the data of the monitoring points of the meteorological observation station, wherein the index indexes at least include: the daily maximum temperature index, the daily minimum relative humidity index, the daily maximum wind speed index, the continuous rainless day index and the daily rainfall; the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are calculated respectively according to the meteorological factor parameters and the index index; a fire risk level comparison table is preset, and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are compared with the fire risk level comparison table respectively to determine the corresponding fire risk level, and realize the quantitative analysis and calculation of the index index. The fire risk index is a specific numerical value, which can quantitatively compare the fire risks in different regions and different time periods, and can more accurately reflect the potential danger degree of forest fires in a region, so that the relevant departments can more intuitively understand the fire risk conditions in various regions, so as to allocate fire prevention resources and forces in a targeted manner, improve the efficiency of fire prevention work, and provide a scientific basis for fire prevention decision-making.

[0164] At the same time, short-term fire risk forecasts can be made based on real-time data, and medium- and long-term fire risk forecasts can be made by combining historical data and long-term meteorological trends. This helps relevant departments to formulate long-term fire prevention plans and strategies, and make preparations in advance for periods of high fire risk. According to the fire risk index, the fire prevention department can reasonably allocate human, material and financial resources. In areas with a high fire risk index, the number of patrols can be increased, more fire checkpoints can be set up, and more fire-fighting equipment can be equipped; in areas with a low fire risk index, resource input can be appropriately reduced, thereby improving resource utilization efficiency.

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. After considering the disclosure of the specification and the examples, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and the examples are only regarded as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0166] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A fire risk forecasting method based on fire risk index, characterized in that: The following steps are involved: Combined with the data of the meteorological observation station monitoring points in advance, meteorological factor parameters and index indexes are obtained, wherein the index indexes at least include: daily maximum temperature index, daily minimum relative humidity index, daily maximum wind speed index, continuous rainless day index and daily rainfall; According to the meteorological factor parameters and index index, fire danger index HX1, fire danger index HX2 and fire danger index HX3 are calculated respectively; A fire risk level comparison table is preset, and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 are compared with the fire risk level comparison table to determine the corresponding fire risk level; Check the correlation values ​​between the actual fire risk level of the previous cycle and the fire risk index HX1, fire risk index HX2 and fire risk index HX3 respectively, and select the fire risk index corresponding to the correlation value that meets the preset judgment rules as the predicted fire risk warning for the next cycle.

2. The fire risk forecasting method based on fire risk index according to claim 1, characterized in that: The fire risk level comparison table is expressed as follows:

3. The fire risk forecasting method based on fire risk index according to claim 2, characterized in that: The fire risk index HX1 is calculated as: HX1=(T+H+W+DR)*Cr*Cs; Among them, T is the daily maximum temperature index, taking the grid forecast result; H is the daily minimum relative humidity index, taking the grid h forecast result, Cs is the surface correction coefficient, Cr is the precipitation correction coefficient, expressed as: Where W is the daily maximum wind speed index, and the forecast result of grid point w is expressed as: Wherein, D represents the continuous rainless day index, expressed as: D = d*5 (d ≥ 10); Among them, R is the daily rainfall, and the forecast result of grid point r is expressed as:

4. The fire risk forecasting method based on fire risk index according to claim 2, characterized in that: Calculate the fire danger index HX2, expressed as: HX2=(0.3*C1+0.7*C2)*Cr*Cs; Among them, C1 and C2 are term indexes, 0.3 and 0.7 are coefficient values, Cs is the surface correction coefficient, Cr is the precipitation correction coefficient, and the value is 1 or 0, expressed as:

5. The fire risk forecasting method based on fire risk index according to claim 4, characterized in that: The coefficient index C1 is expressed as: C1=IT+IH+IV+ID; Among them, IT is the daily maximum temperature index, and the forecast result of grid point t is expressed as: Where IH is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as: Among them, IV is the daily maximum wind speed index, and the numerical value w is used as the forecast result, which is expressed as: Among them, ID is the continuous rainless day index, taking the continuous rainless day d value, expressed as:

6. The fire risk forecasting method based on fire risk index according to claim 4, characterized in that: The coefficient index C2 is expressed as: C2=IpT+IpH+IpV+IpD Among them, IpT represents the daily maximum temperature index, and the forecast result of grid point t is expressed as: Among them, IpH is the daily minimum relative humidity index, and the forecast result of grid point h is expressed as: Among them, IpV is the daily maximum wind speed index, and the numerical value w is used as the forecast result, which is expressed as: Among them, IpD is the continuous rainless day index, which is expressed as:

7. The fire risk forecasting method based on fire risk index according to claim 2, characterized in that: The calculation of the fire risk index HX3 comprises the following steps: Pre-fetch supply index I FED , expressed as: I FED =1.275D 0.987 +exp(0.0338t-0.0345H+0.0234V); Among them, t is the daily maximum temperature, H is the daily minimum relative humidity, and V is the daily average wind speed; The drought factor D is expressed as Where I is I KBD The index is: N is the number of consecutive days without precipitation, and R is the daily precipitation; I KBD It is expressed as: I KBD =Q n-1 +dQ n ; Among them, dQ n is the daily evaporation of soil moisture, Q n-1 is the accumulated loss of forest soil moisture on the previous day; Set the daily evaporation of soil moisture dQ n It is a function of the daily maximum temperature t and the annual average precipitation R (in), expressed as: Get the fire risk index HX3, expressed as: HX3=I FED *Cr*Cs; Among them, Cs is the surface correction coefficient, Cr is the precipitation correction coefficient, and the value is 1 or 0, expressed as:

8. The fire risk forecasting method based on fire risk index according to claim 1, characterized in that: The checking of the correlation values ​​between the actual fire risk level of the previous cycle and the fire risk index HX1, the fire risk index HX2 and the fire risk index HX3 respectively comprises the following steps: The actual fire risk level of the previous cycle is pre-calibrated as y = {y1, y2, y3, y4, y5, y6, y7}, where y i represents the number of fire hazards on the i-th day; calibrate each fire hazard index j (j = HX1, HX2, HX3), there is a sequence x j ={x j1 ,x j2 ,x j3 ,x j4 ,x j5 ,x j6 ,x j7 }, where x ji represents the value of the j-th fire danger index on the i-th day; Calculate the mean of y, expressed as: Calculate the mean value of the fire danger index j, expressed as: Calculate the covariance, expressed as: Calculate the standard deviation of y, expressed as: Calculate x i The standard deviation of is expressed as: Get the relevant values ​​corresponding to the fire risk index j respectively, expressed as:

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