Forest fire early warning method and system based on artificial intelligence

Abstract

The present invention provides a forest fire early warning method and system based on artificial intelligence, which relates to the technical field of forest fire early warning. The method includes real-time collection of forest fire data, including environmental, vegetation, and external data, and acquisition of historical data. The forward-backward algorithm is used to analyze the forest fire data to obtain fire-causing factor data. At the same time, the historical data is classified and statistically analyzed to obtain combustion material data, and the fire-related factor data is calculated with the help of the association rule algorithm. The Aurora optimization algorithm is used to calculate the fire-related combination value, and the fire-causing threshold is determined by the threshold setting method based on the fire-causing factor data. Based on the fire-related combination value, it is judged whether the fire-related factor data exceeds the fire-causing threshold. If it exceeds the threshold, the risk elimination mechanism is triggered. If it does not exceed the threshold, the forest fire data is continuously collected. The present invention integrates the forward-backward algorithm, the association rule algorithm and the Aurora optimization algorithm to give full play to the advantages of each algorithm, accurately process complex data relationships, and curb the occurrence of fires at the source.

Description

Technical Field

[0001] The present invention relates to the technical field of forest fire early warning technology, and in particular to an artificial intelligence-based forest fire early warning method and system. Background Art

[0002] Early fire warnings rely primarily on manual monitoring and empirical judgment. Typically, forest firefighters and monitoring stations identify fire sources through visual observation and patrols. This approach is limited by its slow response time, limited coverage, and heavy reliance on human experience and judgment. With the development of aerospace technology, remote sensing has been introduced into forest fire monitoring. Satellite imagery and aerial photography have enabled large-scale monitoring, helping to identify fire-risk areas. While this has improved the breadth and accuracy of monitoring, data processing and early warning efficiency remain limited. Advances in sensor technology and the Internet of Things have made real-time data collection possible. By deploying environmental sensors (such as those for temperature, humidity, and wind speed), fire risk factors can be monitored in real time, significantly improving data collection and transmission efficiency and providing timely feedback on environmental changes, providing a foundation for early warning.

[0003] Forest fire early warning methods use a range of technologies and methods to identify and predict the likelihood of forest fires in advance, enabling preventive measures or timely responses. These methods integrate knowledge and technologies from multiple disciplines, including meteorology, ecology, geographic information systems (GIS), remote sensing, the Internet of Things (IoT), and artificial intelligence (AI). They predict and prevent forest fires through real-time monitoring, data analysis, and intelligent decision-making support. These systems rely on a variety of advanced technologies and tools to improve the accuracy of fire warnings and response times.

[0004] In existing technologies, only a single or simple algorithm is used to analyze data, and multiple potential triggering factors are ignored. It is difficult to comprehensively consider complex and multiple fire factors and early warning systems only take action after detecting high-risk signals. There is a lack of proactive intervention measures, and it is impossible to curb the occurrence of fires at the source. Summary of the Invention

[0005] The present invention provides an artificial intelligence-based forest fire early warning method and system to address the shortcomings of the existing technology, such as ignoring multiple potential triggering factors when analyzing data, and having difficulty in comprehensively considering complex and multiple fire factors; and the early warning system only taking action after detecting high-risk signals, lacking proactive intervention measures.

[0006] In one aspect, the present invention provides a forest fire early warning method based on artificial intelligence, comprising:

[0007] S1: Obtain forest fire data of the target forest area in real time, and obtain historical data based on databases and literature. Forest fire data includes environmental data, vegetation data, and external data.

[0008] S2: Analyze forest fire data through the forward-backward algorithm to obtain data on fire-causing factors. Classify and count historical data according to different dimensions to obtain combustion material data. Use the association rule algorithm to calculate the combustion material data and the fire-causing factor data to obtain the fire-related factor data.

[0009] S3: The fire-related combination value is calculated by the Aurora optimization algorithm on the environmental data, vegetation data and external data, and the fire triggering threshold is obtained from the fire triggering factor data according to the threshold setting method.

[0010] S4: Determine whether the fire-related factor data exceeds the fire-causing threshold based on the fire-related combination value. If so, trigger the risk elimination mechanism; otherwise, continue to collect forest fire data in real time.

[0011] According to an artificial intelligence-based forest fire early warning method provided by the present invention, the steps of obtaining data on fire-causing factors include:

[0012] The missing values, error values ​​and outliers in the forest fire data are removed to obtain cleaned data, and the fire intensity of the forest fire is divided into multiple fire stages, which are divided into the first stage, the second stage, the third stage and the fourth stage.

[0013] Initialize the initial probability distribution of the fire stage, the state transition probability matrix, and the observation probability matrix, and calculate the forward probability and backward probability of the sequence in the cleaned data through the forward-backward algorithm.

[0014] The time probability and moment probability are calculated through forward probability and backward probability, and the initial probability distribution, state transition probability matrix and observation probability matrix are updated to obtain the key factor identification data.

[0015] Fire-causing factor data were obtained by analyzing updated key factor identification data under multiple fire stages and observation values, including humidity, temperature, and human activities.

[0016] According to an artificial intelligence-based forest fire early warning method provided by the present invention, the forward probability and backward probability are expressed as follows:

[0017]

[0018] In the formula is the number of fire stages, It's time The observed value of is a moment in the time series, is the index of the fire stage, Indicates transition from state To status The probability of The system is in the fire stage The probability of is Moment, based on the previous observations, the system is in the fire stage probability.

[0019] According to an artificial intelligence-based forest fire early warning method provided by the present invention, in step S2, the step of obtaining combustion material data includes:

[0020] Collect information on combustion residues during the fire stage and conduct preliminary classification into vegetation residues and non-vegetation residues. For vegetation residues, combine on-site survey records and vegetation distribution maps to determine the plant species and plant parts. For non-vegetation residues, identify whether they contain flammable substances.

[0021] For vegetation residues, the burned area and vegetation density are used to calculate the biomass of the burning materials. For non-vegetation burning materials, the non-biomass is calculated based on the volume or area of ​​the on-site debris. The biomass and non-biomass are combined to obtain the total amount of burning materials.

[0022] The degree of completeness of combustion can be determined by inspecting the remaining unburned materials on site, and the intensity and speed of combustion can be determined by analyzing the temperature change curve of the recorder.

[0023] According to an artificial intelligence-based forest fire early warning method provided by the present invention, the steps of obtaining fire-related factor data include:

[0024] S21: Discretize the combustion material data and the fire-causing factor data and convert them into the form used for the association rule algorithm. Suppose the combustion material data is B (b1, b2, ..., b m ), where m is the number of elements in the combustion material data, and the fire-causing factor data is F (f1, f2, ..., f n ), where n is the number of elements in the fire-causing factor data.

[0025] S22: Define the minimum support and minimum confidence as the thresholds for the frequent itemsets that need to be retained, find all frequent itemsets, and calculate the support G of each frequent item set.

[0026] S23: Generate all possible binomial combinations from the frequent itemsets to construct candidate itemsets.

[0027] S24: Calculate the support T of each candidate item set and retain the item sets that meet the minimum support.

[0028] S25: Repeat steps S22 to S24 until the preset number of iterations is reached, generate corresponding association rules from the frequent item set, and calculate whether the confidence of the association rule is greater than the minimum confidence. If so, retain the association rule, and extract the factors closely related to the fire from the association rule as the fire-related factor data; otherwise, delete the association rule.

[0029] According to an artificial intelligence-based forest fire early warning method provided by the present invention, in step S3, the step of obtaining the fire correlation combination value includes:

[0030] S31: Aurora emission stage: Process missing values ​​and outliers in environmental data, vegetation data and external data, and perform standardization to obtain comprehensive data. Let the environmental data be U (U1, U2, ..., U Q ), where Q represents the number of environmental data, vegetation data O (O1, O2, ..., O W ), where W represents the number of vegetation data, and the external data P (P1, P2, ..., P E ), where E represents the number of external data.

[0031] S32: Extract key features from the comprehensive data as optimization targets, randomly generate a set of aurora particles as ignition factor values, and define the fitness function. The aurora particles X include (c1, c2, ..., c j ), where j is the number of auroral particles.

[0032] S33: Radiation attenuation stage: Introduce the attenuation factor and adjust the position update formula according to the attenuation factor to obtain the aurora position update formula.

[0033] S34: Update the aurora particle position using the aurora position update formula.

[0034] S35: Aurora scattering optimization stage: The fitness of each aurora particle is calculated according to the fitness function, the updated aurora particle position is determined according to the fitness, and the individual best particle and the global best particle are iteratively updated.

[0035] S36: Repeat steps S33 to S35 until the fitness change is less than a preset threshold, and select the ignition factor value with the highest fitness as the ignition association combination value.

[0036] According to an artificial intelligence-based forest fire early warning method provided by the present invention, in step S34, the aurora position update formula is expressed as:

[0037]

[0038] Where, is the updated aurora particle position, is the current position of the aurora particle, is the current optimal position of the aurora particles, is the global optimal position, and is a random number between (0,1), is the attenuation factor.

[0039] According to an artificial intelligence-based forest fire early warning method provided by the present invention, in step S3, the step of obtaining a fire triggering threshold comprises:

[0040] The data on fire-causing factors are organized into a table format, with each row representing a fire event and each column corresponding to a fire-causing factor.

[0041] The mean and standard deviation of each fire-causing factor are calculated, and the threshold range is determined based on the preset K value to obtain the fire-causing threshold.

[0042] The threshold range is determined based on the preset value and is divided into a lower threshold and an upper threshold.

[0043] According to an artificial intelligence-based forest fire early warning method provided by the present invention, the formulas for the lower threshold and the upper threshold are:

[0044]

[0045] Where L is the lower threshold, U is the upper threshold, K is the preset constant, and u is the mean. is the standard deviation.

[0046] On the other hand, the present invention also provides an artificial intelligence-based forest fire early warning system, which adopts any of the artificial intelligence-based forest fire early warning methods described above, and the early warning system includes:

[0047] Data acquisition module: acquires forest fire data of the target forest area in real time, and obtains historical data based on database and literature. Forest fire data includes environmental data, vegetation data and external data.

[0048] Analysis module: Analyze forest fire data through the forward-backward algorithm to obtain data on fire-causing factors, classify and count historical data according to different dimensions to obtain combustion material data, and use the association rule algorithm to calculate the combustion material data and fire-causing factor data to obtain fire-related factor data.

[0049] Risk judgment module: The fire-related combination value is calculated by the Aurora optimization algorithm on environmental data, vegetation data and external data, and the fire-causing threshold is obtained from the fire-causing factor data according to the threshold setting method.

[0050] Prevention and control module: Determine whether the fire-related factor data exceeds the fire-causing threshold based on the fire-related combination value. If so, trigger the risk elimination mechanism; otherwise, continue to collect forest fire data in real time.

[0051] The present invention provides an artificial intelligence-based forest fire early warning method and system, which integrates the forward-backward algorithm, the association rule algorithm and the Aurora optimization algorithm, gives full play to the advantages of each algorithm, accurately processes complex data relationships, improves the accuracy and reliability of risk assessment, solves the limitations of single or simple combination algorithms, and reduces the problems of false alarms and missed alarms in the early warning system. It achieves the beneficial effect of accurately predicting the possibility and development trend of fire, discovering potential fire risks in advance, providing strong support for timely preventive measures, and effectively reducing the losses caused by fire.

[0052] The present invention provides an artificial intelligence-based forest fire early warning method and system, which sets dynamic thresholds based on fire-causing factor data, and quickly triggers prevention and control mechanisms or continuous monitoring by comparing fire-related combination values ​​with thresholds in real time, thereby optimizing the accuracy and timeliness of risk assessment and prevention and control, and solving the defects of fixed thresholds and delayed feedback in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is one of the flow charts of an artificial intelligence-based forest fire early warning method provided by an embodiment of the present invention;

[0055] Figure 2 This is the second flow chart of an artificial intelligence-based forest fire early warning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] The following combination Figure 1-Figure 2 The present invention describes an artificial intelligence-based forest fire early warning method and system.

[0058] Figure 1 This is one of the flow charts of an artificial intelligence-based forest fire early warning method provided by an embodiment of the present invention.

[0059] like Figure 1 As shown, an embodiment of the present invention provides an artificial intelligence-based forest fire early warning method, which mainly includes the following steps:

[0060] S1: Obtain forest fire data of the target forest area in real time, and obtain historical data based on databases and literature. Forest fire data includes environmental data, vegetation data, and external data.

[0061] S2: Analyze forest fire data through the forward-backward algorithm to obtain data on fire-causing factors. Classify and count historical data according to different dimensions to obtain combustion material data. Use the association rule algorithm to calculate the combustion material data and the fire-causing factor data to obtain the fire-related factor data.

[0062] In step S2, the step of obtaining fire-causing factor data includes:

[0063] The missing values, error values ​​and outliers in the forest fire data are removed to obtain cleaned data, and the fire intensity of the forest fire is divided into multiple fire stages, which are divided into the first stage, the second stage, the third stage and the fourth stage.

[0064] Initialize the initial probability distribution of the fire stage, the state transition probability matrix, and the observation probability matrix. Calculate the forward and backward probabilities of the sequences in the cleaned data using the forward-backward algorithm. The formula is:

[0065]

[0066] Where N is the number of fire stages, It's time The observation value of , t is the moment in the time series, is the index of the fire stage, Indicates transition from state To status The probability of The system is in the fire stage The probability of is Moment, based on the previous observations, the system is in the fire stage probability.

[0067] The time probability and moment probability are calculated through forward probability and backward probability, and the initial probability distribution, state transition probability matrix and observation probability matrix are updated to obtain the key factor identification data.

[0068] Fire-causing factor data were obtained by analyzing updated key factor identification data under multiple fire stages and observation values, including humidity, temperature, and human activities.

[0069] The steps to obtain combustion data include:

[0070] Collect information on combustion residues during the fire stage and conduct preliminary classification into vegetation residues and non-vegetation residues. For vegetation residues, combine on-site survey records and vegetation distribution maps to determine the plant species and plant parts. For non-vegetation residues, identify whether they contain flammable substances.

[0071] For vegetation residues, the burned area and vegetation density are used to calculate the biomass of the burning materials. For non-vegetation burning materials, the non-biomass is calculated based on the volume or area of ​​the on-site debris. The biomass and non-biomass are combined to obtain the total amount of burning materials.

[0072] The degree of completeness of combustion can be determined by inspecting the remaining unburned materials on site, and the intensity and speed of combustion can be determined by analyzing the temperature change curve of the recorder.

[0073] Figure 2 This is the second flow chart of an artificial intelligence-based forest fire early warning method provided by an embodiment of the present invention.

[0074] like Figure 2 As shown, the steps of obtaining fire-related factor data include:

[0075] S21: Discretize the combustion material data and the fire-causing factor data and convert them into the form used for the association rule algorithm. Suppose the combustion material data is B (b1, b2, ..., b m ), where m is the number of elements in the combustion material data, and the fire-causing factor data is F (f1, f2, ..., f n ), where n is the number of elements in the fire-causing factor data.

[0076] S22: Define the minimum support and minimum confidence as the thresholds for the frequent itemsets that need to be retained, find all frequent itemsets, and calculate the support G of each frequent item set.

[0077] S23: Generate all possible binomial combinations from the frequent itemsets to construct candidate itemsets.

[0078] S24: Calculate the support T of each candidate item set and retain the item sets that meet the minimum support.

[0079] S25: Repeat steps S22 to S24 until the preset number of iterations is reached, generate corresponding association rules from the frequent item set, and calculate whether the confidence of the association rule is greater than the minimum confidence. If so, retain the association rule, and extract the factors closely related to the fire from the association rule as the fire-related factor data; otherwise, delete the association rule.

[0080] S3: The fire-related combination value is calculated by the Aurora optimization algorithm on the environmental data, vegetation data and external data, and the fire triggering threshold is obtained from the fire triggering factor data according to the threshold setting method.

[0081] The steps to obtain the fire-related combination value include:

[0082] S31: Aurora emission stage: Process missing values ​​and outliers in environmental data, vegetation data and external data, and perform standardization to obtain comprehensive data. Let the environmental data be U (U1, U2, ..., U Q ), where Q represents the number of environmental data, vegetation data O (O1, O2, ..., O W ), where W represents the number of vegetation data, external data P (P1, P2, ..., P E ), where E represents the number of external data.

[0083] S32: Extract key features from the comprehensive data as optimization targets, randomly generate a set of aurora particles as ignition factor values, and define the fitness function. The aurora particles X include (c1, c2, ..., c j ), where j is the number of auroral particles.

[0084] The fitness function is expressed as:

[0085]

[0086] Where, is the classification accuracy of a specific combination, is the support of the combination in the dataset, is the confidence of the combination, , , is the weight coefficient.

[0087] S33: Radiation attenuation stage: Introduce the attenuation factor and adjust the position update formula according to the attenuation factor to obtain the aurora position update formula.

[0088] S34: Update the aurora particle position using the aurora position update formula.

[0089] In step S34, the aurora position update formula is expressed as:

[0090]

[0091] Where, is the updated aurora particle position, is the current position of the aurora particle, is the current optimal position of the aurora particles, is the global optimal position, and is a random number between (0,1), is the attenuation factor.

[0092] S35: Aurora scattering optimization stage: The fitness of each aurora particle is calculated according to the fitness function, the updated aurora particle position is determined according to the fitness, and the individual best particle and the global best particle are iteratively updated.

[0093] S36: Repeat steps S33 to S35 until the fitness change is less than a preset threshold, and select the ignition factor value with the highest fitness as the ignition association combination value.

[0094] The steps to obtain the fire triggering threshold include:

[0095] The data on fire-causing factors are organized into a table format, with each row representing a fire event and each column corresponding to a fire-causing factor.

[0096] The mean and standard deviation of each fire-causing factor are calculated, and the threshold range is determined based on the preset K value to obtain the fire-causing threshold.

[0097] The formulas for mean and standard deviation are:

[0098]

[0099] Where, It is observations of fire-causing factors, n is the number of data samples, u is the mean, is the standard deviation and u is the mean.

[0100] The threshold range is determined based on the preset value and is divided into a lower threshold and an upper threshold.

[0101] The formulas for the lower threshold value and the upper threshold value are:

[0102]

[0103] Where L is the lower threshold, U is the upper threshold, K is the preset constant, and u is the mean. is the standard deviation.

[0104] S4: Based on the fire-related combination value, the system determines whether the fire-related factor data exceeds the fire threshold. If so, the risk elimination mechanism is triggered; otherwise, real-time forest fire data collection continues. The system continuously compares the calculated fire-related combination value with the established fire threshold and conducts in-depth analysis, using rigorous logic to determine whether the fire-related factor data exceeds the corresponding threshold range. Once it is determined that the data of fire-related factors exceeds the set fire-causing threshold, the system will quickly and decisively automatically trigger a complete set of comprehensive and efficient risk elimination mechanisms. This mechanism covers such as timely and accurate push of detailed fire risk alarm information to relevant management departments and personnel, and rapid launch of pre-set fire-fighting plans, including specific actions such as deploying fire-fighting resources and organizing professional fire-fighting teams to rush to possible fire scenes; and if, after careful judgment, the data of fire-related factors does not exceed the fire-causing threshold, the system will continue to perform the key task of real-time collection of forest fire data in the target forest area in an orderly manner, to ensure that the real-time status of the forest area can be continuously, dynamically and accurately grasped, thereby providing the latest and reliable data support for subsequent fire risk assessment and early warning, and maintaining the efficient operation and keen monitoring capabilities of the entire forest fire early warning system.

[0105] The present invention uses a forward-backward algorithm to analyze forest fire data to obtain fire-causing factor data, collects combustion material data based on historical data, and uses an association rule algorithm to find out the fire-related factor data. At the same time, the Aurora optimization algorithm is used to calculate the fire-related combination value, and the fire-causing threshold is determined according to the threshold setting method. It can more accurately identify fire risk factors and potential fire associations, improve the accuracy and reliability of the model, reduce false alarms and missed alarms, and more accurately predict the possibility and development trend of fires, discover potential fire risks in advance, provide strong support for timely preventive measures, and effectively reduce the losses caused by fires.

[0106] Based on the same general inventive concept, the present invention also protects an artificial intelligence-based forest fire early warning system, which can adopt the above-mentioned artificial intelligence-based forest fire early warning method. The early warning system includes:

[0107] Data acquisition module: acquires forest fire data of the target forest area in real time and obtains historical data based on databases and literature. The forest fire data includes environmental data, vegetation data, and external data;

[0108] Analysis module: Analyzes the forest fire data using a forward-backward algorithm to obtain fire-causing factor data, classifies and counts the historical data according to different dimensions to obtain combustion material data, and uses an association rule algorithm to calculate the combustion material data and the fire-causing factor data to obtain fire-related factor data;

[0109] Risk judgment module: calculates the environmental data, the vegetation data, and the external data using the Aurora optimization algorithm to obtain a fire-related combination value, and obtains a fire-causing threshold from the fire-causing factor data according to a threshold setting method;

[0110] Prevention and control module: determines whether the fire-related factor data exceeds the fire-initiating threshold value based on the fire-related combination value, and triggers the risk elimination mechanism if it does, otherwise continues to collect the forest fire data in real time.

[0111] The present invention integrates the forward-backward algorithm, the association rule algorithm and the Aurora optimization algorithm to give full play to the advantages of each algorithm, accurately process complex data relationships, improve the accuracy and reliability of risk assessment, and make up for the limitations of single or simple combination algorithms. It sets dynamic thresholds based on the data of fire-causing factors, and quickly triggers the prevention and control mechanism or continuous monitoring by comparing the fire-related combination value with the threshold in real time, thereby optimizing the accuracy and timeliness of risk judgment and prevention and control, and solving the defects of fixed thresholds and delayed feedback in the existing technology.

[0112] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0113] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A forest fire early warning method based on artificial intelligence, characterized in that: include: S1: Acquire forest fire data of the target forest area in real time and obtain historical data based on databases and literature, wherein the forest fire data includes environmental data, vegetation data, and external data; S2: Analyze the forest fire data using a forward-backward algorithm to obtain fire-causing factor data, classify and count the historical data according to different dimensions to obtain combustion material data, and use an association rule algorithm to calculate the combustion material data and the fire-causing factor data to obtain fire-related factor data; S3: Calculating the environmental data, the vegetation data, and the external data using an Aurora optimization algorithm to obtain a fire-related combination value, and obtaining a fire initiation threshold from the fire initiation factor data according to a threshold setting method; S4: judging whether the fire-related factor data exceeds the fire-initiating threshold value according to the fire-related combination value, and triggering the risk elimination mechanism if it does, otherwise continuing to collect the forest fire data in real time.

2. The artificial intelligence-based forest fire early warning method according to claim 1, characterized in that: In step S2, the step of obtaining the fire-causing factor data includes: Removing missing values, error values, and outliers from the forest fire data to obtain cleaned data, and dividing the forest fire intensity into multiple fire stages, wherein the fire stages are divided into the first stage, the second stage, the third stage, and the fourth stage; Initializing the initial probability distribution, state transition probability matrix, and observation probability matrix of the fire stage, and calculating the forward probability and backward probability of the sequence in the cleaned data by the forward-backward algorithm; Calculating the time probability and the moment probability through the forward probability and the backward probability and updating the initial probability distribution, the state transition probability matrix and the observation probability matrix to obtain key factor identification data; The fire-causing factor data is obtained by analyzing the updated key factor identification data under multiple fire stages and observation values, where the observation values ​​include humidity, temperature, and human activities.

3. The artificial intelligence-based forest fire early warning method according to claim 2, characterized in that: The forward probability and the backward probability are expressed as follows: ; In the formula is the number of fire stages, It's time The observed value of is a moment in the time series, is the index of the fire stage, Indicates transition from state To status The probability of The system is in the fire stage The probability of is Moment, based on the previous observations, the system is in the fire stage probability.

4. The artificial intelligence-based forest fire early warning method according to claim 2, characterized in that: In step S2, the step of obtaining the combustion material data includes: Collect information on combustion residues at the fire stage and perform preliminary classification into vegetation residues and non-vegetation residues. For vegetation residues, determine the plant species and plant parts by combining on-site survey records and vegetation distribution maps; for non-vegetation residues, identify whether they contain flammable substances; For the vegetation residue, the burned area and vegetation density are used to calculate the biomass of the burnt material. For the non-vegetation burnt material, the non-biomass is calculated based on the volume or area of ​​the on-site debris. The biomass and non-biomass are combined to obtain the total amount of the burnt material. The degree of completeness of combustion can be determined by inspecting the remaining unburned materials on site, and the intensity and speed of combustion can be determined by analyzing the temperature change curve of the recorder.

5. The artificial intelligence-based forest fire early warning method according to claim 1, characterized in that: In step S2, the step of obtaining the fire-related factor data includes: S21: Discretize the combustion material data and the fire-causing factor data and convert them into a form for association rule algorithm. Suppose the combustion material data is B (b1, b2, ..., b m ), where m is the number of elements in the combustion material data, and the fire-causing factor data is F (f1, f2, ..., f n ), where n is the number of elements of the fire-causing factor data; S22: define the minimum support and minimum confidence as the thresholds of the frequent itemsets that need to be retained, find all frequent itemsets, and calculate the support G of each of the frequent itemsets; S23: generating all possible binomial combinations from the frequent itemsets to construct candidate itemsets; S24: Calculate the support T of each candidate item set, and retain the item sets that meet the minimum support; S25: Repeat steps S22 to S24 until a preset number of iterations is reached, generate corresponding association rules from the frequent item set, and calculate whether the confidence of the association rule is greater than the minimum confidence. If so, retain the association rule and extract the factors closely related to the fire from the association rule as the fire-related factor data; otherwise, delete the association rule.

6. The artificial intelligence-based forest fire early warning method according to claim 1, characterized in that: In step S3, the step of obtaining the ignition-related combination value includes: S31: Aurora emission stage: Process missing values ​​and abnormal values ​​in the environmental data, the vegetation data and the external data, and perform standardization to obtain comprehensive data. Let the environmental data be U (U1, U2, ..., U Q ), where Q represents the number of environmental data, the vegetation data O (O1, O2, ..., O W ), where W represents the number of vegetation data, the external data P (P1, P2, ..., P E ), where E represents the number of external data; S32: Extract key features from the comprehensive data as optimization targets, randomly generate a set of aurora particles as ignition factor values, and define a fitness function, wherein the aurora particles X include (c1, c2, ..., c j ), where j is the number of auroral particles; S33: Radiation attenuation stage: introducing an attenuation factor, and adjusting the position update formula according to the attenuation factor to obtain the aurora position update formula; S34: updating the position of the aurora particle using the aurora position update formula; S35: Aurora scattering optimization stage: calculating the fitness of each aurora particle according to the fitness function, determining the updated aurora particle position according to the fitness, and iteratively updating the individual best particle and the global best particle; S36: Repeat steps S33 to S35 until the fitness change is less than a preset threshold, and select the ignition factor value with the highest fitness as the ignition association combination value.

7. The artificial intelligence-based forest fire early warning method according to claim 6, characterized in that: In step S34, the aurora position update formula is expressed as: ; Where, is the updated aurora particle position, is the current position of the aurora particle, is the current optimal position of the aurora particles, is the global optimal position, and is a random number between (0,1), is the attenuation factor.

8. The artificial intelligence-based forest fire early warning method according to claim 1, characterized in that: In step S3, the step of obtaining the fire triggering threshold includes: Arrange the fire-causing factor data into a table, with each row representing a fire event and each column corresponding to a fire-causing factor; Calculating the mean and standard deviation of each of the fire-causing factors, and determining a threshold range based on a preset K value to obtain a fire-causing threshold; The threshold range is determined based on the preset value and is divided into a lower threshold and an upper threshold.

9. The artificial intelligence-based forest fire early warning method according to claim 8, characterized in that: The formulas for the lower threshold and the upper threshold are respectively: ; Where, is the lower threshold, is the upper threshold, is a preset constant, is the mean, is the standard deviation.

10. An artificial intelligence-based forest fire early warning system, which adopts the artificial intelligence-based forest fire early warning method according to any one of claims 1 to 9, characterized in that: The early warning system includes: Data acquisition module: acquires forest fire data of the target forest area in real time and obtains historical data based on databases and literature. The forest fire data includes environmental data, vegetation data, and external data; Analysis module: Analyzes the forest fire data using a forward-backward algorithm to obtain fire-causing factor data, classifies and counts the historical data according to different dimensions to obtain combustion material data, and uses an association rule algorithm to calculate the combustion material data and the fire-causing factor data to obtain fire-related factor data; Risk judgment module: calculates the environmental data, the vegetation data, and the external data using the Aurora optimization algorithm to obtain a fire-related combination value, and obtains a fire-causing threshold from the fire-causing factor data according to a threshold setting method; Prevention and control module: determines whether the fire-related factor data exceeds the fire-initiating threshold value based on the fire-related combination value, and triggers the risk elimination mechanism if it does, otherwise continues to collect the forest fire data in real time.

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