A method and system for predicting forest fire risk of an energy corridor based on multi-source data fusion

Abstract

The application discloses a kind of energy corridor forest fire risk prediction method and system based on multi-source data fusion, method includes: constructing fusion dataset, determine risk evolution parameter;If risk evolution parameter exceeds preset threshold, trigger vegetation difference analysis function, obtain section heterogeneity label;After obtaining section heterogeneity label, temporal and spatial inhomogeneity compensation correction is carried out to historical data by time series change tracking method, the risk transition probability of each section is calculated, and potential critical point is judged;If potential critical point is judged as high probability, activate difference early warning mechanism, generate targeted risk level map according to section heterogeneity label and risk transition probability, obtain dynamic early warning signal;Real-time update stream in fusion dataset is used to iteratively verify the dynamic early warning signal, adjust risk level in combination with the prediction output of long short-term memory network, and determine the final risk prediction result.The application improves the timeliness and accuracy of fire risk prediction.

Description

Technical Field

[0001] This invention relates to the field of forest fire risk prediction technology, and in particular to a method and system for predicting forest fire risks in energy corridors based on multi-source data fusion. Background Technology

[0002] Energy corridors, as vital channels for ensuring energy transmission, carry critical infrastructure such as power transmission lines and oil and gas pipelines. Their safe operation is crucial to national energy security and public safety. However, these corridors often traverse complex environments such as mountains and forests, making them highly vulnerable to forest fires. Once a fire occurs, it can lead to significant economic losses and ecological damage. Therefore, researching how to effectively predict and prevent forest fire risks has become an indispensable key area in the protection of energy corridors.

[0003] Currently, although some methods have been developed to assess forest fire risk, most suffer from difficulty adapting to complex environmental changes. Especially when faced with variable climate conditions and terrain features, these methods often fail to capture the changing trends of key factors influencing fire occurrence in a timely manner, and are also difficult to flexibly adjust to the specific circumstances of different regions. This limitation often results in delayed risk warnings, missing the optimal window for prevention and control, particularly in the failure to effectively identify potential threats in the early stages of a fire.

[0004] A deeper technical challenge lies in accurately grasping the interactions between dynamic factors influencing forest fire risk. A core issue is the instantaneous fluctuation of meteorological conditions; for example, sudden changes in wind speed and humidity can rapidly alter the likelihood of fire spread. These fluctuations further exacerbate regional differences in vegetation dryness, causing some areas to rapidly escalate from low-risk to high-risk. For instance, in an energy corridor traversing mountains, during the high temperatures of summer, vegetation in a certain section may become extremely flammable due to prolonged drought. If a sudden strong wind were to occur, the fire could spiral out of control within hours. However, current technology struggles to comprehensively consider these changing factors in a short time, accurately determine whether the risk has reached a critical point, and cannot provide targeted analysis for the specific conditions of different locations.

[0005] Therefore, how to dynamically track the interaction between meteorological conditions and vegetation status in time and space, and how to achieve accurate risk warnings for environmental differences in different sections along the energy corridor, has become a key issue that urgently needs to be addressed. Summary of the Invention

[0006] To address the technical problems existing in the prior art, this invention proposes a method and system for predicting forest fire risks in energy corridors based on multi-source data fusion, which improves the timeliness and accuracy of fire risk prediction.

[0007] On the one hand, to achieve the above objectives, this invention provides a method for predicting forest fire risks in energy corridors based on multi-source data fusion, comprising:

[0008] Data on wind speed, wind direction, temperature, humidity, and vegetation moisture content of the target energy corridor are collected, and spatiotemporal distribution records are extracted from the historical fire database to construct a fusion dataset. Based on the fusion dataset, a long short-term memory network is used to model the time series of meteorological fluctuations and vegetation differences to determine risk evolution parameters.

[0009] If the risk evolution parameter exceeds the preset threshold, the vegetation difference analysis function is triggered to extract the vegetation type and water content distribution of a specific segment from the fused dataset, and the corresponding features are classified by a random forest classifier to obtain segment heterogeneity labels.

[0010] After obtaining the segment heterogeneity labels, the historical data is corrected for spatiotemporal inhomogeneity through the time series change tracking method, the risk transition probability of each segment is calculated, and then the potential critical point is determined.

[0011] If the potential critical point is judged to be of high probability, the differential early warning mechanism is activated, and a targeted risk level map is generated based on the segment heterogeneity label and risk transition probability to obtain a dynamic early warning signal.

[0012] The dynamic early warning signal is iteratively verified using the real-time update stream from the fused dataset. The risk level is adjusted by combining the prediction output of the long short-term memory network to determine the final risk prediction result.

[0013] On the other hand, to achieve the above objectives, the present invention also provides a prediction system applied to a method for predicting forest fire risks in energy corridors based on multi-source data fusion, comprising:

[0014] The data fusion processing module is used to collect wind speed, wind direction, temperature, humidity and vegetation moisture content data of the target energy corridor, and extract spatiotemporal distribution records from the historical fire database to construct a fusion dataset.

[0015] The risk evolution parameter determination module is used to model the time series of meteorological fluctuations and vegetation differences using a long short-term memory network based on the fused dataset, and determine the risk evolution parameters.

[0016] The vegetation difference analysis module is used to trigger the vegetation difference analysis function when the risk evolution parameter exceeds a preset threshold. It extracts the vegetation type and water content distribution of a specific segment from the fused dataset, classifies the corresponding features through a random forest classifier, and obtains segment heterogeneity labels.

[0017] The risk transition probability calculation module is used to obtain the segment heterogeneity label and then perform spatiotemporal non-uniformity compensation and correction on historical data through the time series change tracking method to calculate the risk transition probability of each segment and then determine the potential critical point.

[0018] The dynamic early warning signal generation module is used to activate the differential early warning mechanism when the potential critical point is judged to be of high probability, and generate a targeted risk level map based on the segment heterogeneity label and risk transition probability to obtain a dynamic early warning signal.

[0019] The final risk assessment result determination module is used to iteratively verify the dynamic early warning signal using the real-time update stream in the fused dataset, adjust the risk level by combining the prediction output of the long short-term memory network, and determine the final risk prediction result.

[0020] The differentiated early warning information transmission module is used to transmit differentiated early warning information to the energy corridor management system based on the final risk assessment results. The refined risk judgment system is obtained by optimizing the model parameters based on the feedback loop of the random forest classifier.

[0021] Compared with the prior art, the present invention has the following advantages and technical effects:

[0022] This invention constructs a multi-source data fusion framework, standardizing meteorological sensor data, satellite remote sensing data, and historical fire records into a unified spatiotemporal grid dataset. It employs a Long Short-Term Memory (LSTM) network to capture temporal features and determine risk evolution parameters. When these parameters exceed thresholds, a random forest classifier generates segment heterogeneity labels, and a temporal change tracking algorithm calculates the risk transition probability to identify potential critical points, thereby generating dynamic early warning signals and risk level maps. This invention combines real-time data stream iterative verification to optimize model parameters, forming a refined risk assessment system. Finally, it transmits differentiated early warning information to the energy corridor management system through an output interface, achieving precise risk prevention and control. Its core technological effect lies in improving the timeliness and accuracy of fire risk prediction through the combination of data fusion and intelligent algorithms, providing scientific support for energy corridor safety management. Attached Figure Description

[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart of a forest fire risk prediction method for energy corridors based on multi-source data fusion, according to an embodiment of the present invention. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0027] This embodiment proposes a method for predicting forest fire risk in energy corridors based on multi-source data fusion. Figure 1 ,include:

[0028] Data on wind speed, wind direction, temperature, humidity, and vegetation moisture content of the target energy corridor are collected, and spatiotemporal distribution records are extracted from the historical fire database to construct a fusion dataset. Based on the fusion dataset, a long short-term memory network is used to model the time series of meteorological fluctuations and vegetation differences to determine risk evolution parameters.

[0029] If the risk evolution parameter exceeds the preset threshold, the vegetation difference analysis function is triggered to extract the vegetation type and water content distribution of a specific segment from the fused dataset, and the corresponding features are classified by a random forest classifier to obtain segment heterogeneity labels.

[0030] After obtaining the segment heterogeneity labels, the historical data is corrected for spatiotemporal inhomogeneity through the time series change tracking method, the risk transition probability of each segment is calculated, and then the potential critical point is determined.

[0031] If the potential critical point is judged to be of high probability, the differential early warning mechanism is activated, and a targeted risk level map is generated based on the segment heterogeneity label and risk transition probability to obtain a dynamic early warning signal.

[0032] The dynamic early warning signal is iteratively verified using the real-time update stream from the fused dataset. The risk level is adjusted by combining the prediction output of the long short-term memory network to determine the final risk prediction result.

[0033] Furthermore, the fused dataset is constructed, including:

[0034] Initial environmental datasets were constructed by collecting wind speed, wind direction, temperature and humidity values, and vegetation water content data of the target energy corridor using meteorological sensors and satellite remote sensing equipment.

[0035] Based on the initial environmental dataset and combined with the spatiotemporal distribution information in historical fire records, the multi-source data is mapped into a unified grid system to complete the data alignment process and obtain the aligned grid dataset.

[0036] For the aligned grid dataset, a standardization process is used to unify the units and adjust the dimensions of the wind speed data, wind direction data, temperature and humidity values, and vegetation water content data to obtain a standardized grid dataset.

[0037] If the proportion of missing data fields in a standardized grid dataset exceeds a preset threshold, the data is completed by interpolation of neighboring grid cells to obtain the completed grid dataset, which is the fused dataset.

[0038] Specifically, when constructing the initial environmental dataset, wind speed and direction data can be collected hourly using meteorological sensors. For example, assuming a wind speed of 5.2 m / s and a northeasterly wind direction in a certain area, satellite remote sensing equipment can acquire temperature and humidity values ​​such as a temperature of 25.3 degrees Celsius, humidity of 60%, and vegetation moisture content of 35%. Since these data come from different devices and may have inconsistent temporal and spatial resolutions, they need to be mapped to a unified grid system, such as aligning the data to a 10 km x 10 km grid cell, to ensure the accuracy of subsequent analysis.

[0039] In data alignment, the time and location information of fires in historical fire records can be matched with the timestamps and geographic coordinates of environmental data. Assuming a grid cell has experienced three fires in the past five years, combining this with current environmental data can provide a preliminary assessment of the potential risks in the area. This alignment process helps integrate multi-source data to form a unified grid dataset, laying the foundation for subsequent analysis. Regarding standardization, for example, the dimensions of data such as wind speed, temperature, and humidity are adjusted. Wind speed is standardized to meters per second, temperature to degrees Celsius, and the data range is adjusted to 0 to 1 through linear transformation to facilitate model input. This process effectively eliminates the influence of different units. To address the issue of missing data, if the missing wind speed data ratio in a grid cell reaches 30%, exceeding the preset threshold of 20%, it can be filled by interpolation using the average value of neighboring grid cells. For example, if the wind speeds of four surrounding grid cells are 4.8, 5.0, 5.1, and 4.9 meters per second, the average value of 5.0 meters per second can be used to fill in the missing data. This method ensures data integrity and avoids model prediction bias due to missing data.

[0040] Further, determining the risk evolution parameters includes:

[0041] Time-series data related to meteorological fluctuations and vegetation differences are extracted from the fused dataset. Through data segmentation, long-term trends and short-term changes are stored as independent data subsets to obtain a hierarchical time-series dataset.

[0042] For the hierarchical time series dataset, a long short-term memory network is used to model short-term changes and long-term trends separately, capturing instantaneous features and evolution patterns to obtain the output results of the dynamic monitoring model;

[0043] Based on the output of the dynamic monitoring model and combined with the distribution characteristics of environmental variables, the instantaneous characteristics of meteorological fluctuations and vegetation differences are weighted to determine the preliminary distribution of risk parameters.

[0044] The initial distribution of risk parameters is mapped in a grid, and the evolution pattern of time series data is combined to identify potential high-risk areas. If the risk parameters of a certain high-risk area exceed the preset risk parameter threshold, it is marked as a key monitoring object, and a list of key areas is obtained.

[0045] For the list of key areas, real-time meteorological fluctuations and vegetation differences are obtained. By comparing and analyzing with historical time-series data, it is determined whether short-term changes show abnormal trends and the dynamic risk level is determined.

[0046] Based on the dynamic risk level and the long-term trend evolution pattern, the environmental variables of key monitoring objects are continuously tracked to determine the risk evolution parameters.

[0047] Specifically, when processing fused datasets, we can first focus on extracting time-series data on meteorological fluctuations and vegetation differences. Meteorological fluctuations may include diurnal variations in wind speed and temperature and humidity, while vegetation differences involve seasonal fluctuations in water content. Suppose that the wind speed in a certain area has two significant variations each day over the past month, one in the morning and one in the evening. Records show that the average wind speed in the morning is 3.5 m / s, increasing to 6.0 m / s in the evening, while the vegetation water content decreases from 40% at the beginning of the month to 28% at the end. By segmenting the data and storing it according to long-term trends (e.g., monthly averages) and short-term variations (e.g., daily fluctuations), a hierarchical time-series dataset is formed, providing a clear data foundation for subsequent modeling.

[0048] When modeling hierarchical time-series datasets, long short-term memory networks can be used to handle short-term changes and long-term trends respectively. Short-term change modeling focuses on the instantaneous fluctuations in daily wind speed and temperature / humidity, while long-term trend analysis examines the decreasing pattern of vegetation moisture content over several months. For example, a short-term model might capture an anomaly where wind speed suddenly increases from 4.0 m / s to 8.0 m / s, while a long-term model shows that vegetation moisture content remains consistently below 30%. These characteristics provide important data for dynamic monitoring.

[0049] In the initial determination of the risk parameter distribution, the instantaneous characteristics of meteorological fluctuations and vegetation differences can be weighted. Assuming the weight of wind speed fluctuations on risk is 0.6 and the weight of vegetation moisture content is 0.4, if a region experiences significant daily wind speed fluctuations and a moisture content of only 25%, a higher risk parameter value will be calculated. This weighting method helps to highlight the impact of key environmental factors.

[0050] In grid-based mapping and identification of potentially high-risk areas, risk parameters can be mapped to a 10 km by 10 km grid. If the risk parameter of a certain grid reaches 0.75, exceeding the preset threshold of 0.7, it is marked as a key monitoring target. For example, if three grids in a forest area consecutively exceed the threshold, a list of key areas is created to facilitate subsequent resource allocation.

[0051] Further, the segment heterogeneity label is obtained, including:

[0052] If the risk evolution parameter exceeds the evolution threshold, vegetation type and water content distribution data for a specific segment are extracted from the fused dataset to complete the preliminary data screening and obtain the segment basic dataset.

[0053] For the aforementioned basic dataset, a random forest classifier is used to classify vegetation types and water content distribution, identify the heterogeneity characteristics of different segments, and obtain the classified segment heterogeneity labels.

[0054] Specifically, in scenarios where the risk evolution parameter exceeds the evolution threshold, vegetation type and water content distribution data for specific sections are extracted from the fused dataset. This process can be understood as a data filtering mechanism designed to focus on areas with potential risks. For example, if the risk parameter for a section of a forest area reaches 0.8, exceeding the threshold of 0.7, the analysis module will prioritize extracting vegetation type data (e.g., coniferous or broadleaf forest) and water content distribution data (e.g., the distribution of areas with an average water content below 30%) for that section, forming a basic dataset for that section and laying the foundation for subsequent analysis.

[0055] For the basic dataset of a given area, when using a random forest classifier to classify vegetation type and water content distribution, vegetation type can be divided into two categories: highly flammable and lowly flammable. Water content distribution can be further divided into three levels: high moisture, medium moisture, and low moisture. For example, if the vegetation in a certain area is predominantly highly flammable coniferous forest with a moisture content mostly below 20%, the classifier will label it as having high heterogeneity. This classification method helps to quickly identify potential differences between different areas.

[0056] Furthermore, the risk transition probability of each segment is calculated to determine potential critical points, including:

[0057] After obtaining the segment heterogeneity labels, the historical data is corrected for spatiotemporal inhomogeneity using a time-series change tracking method. Based on the corrected data, the risk transition probability of each segment is calculated to determine a preliminary list of potential critical points.

[0058] Based on the preliminary list of potential critical points, a preset threshold is used to filter the risk transition probability of each segment. If the risk transition probability of a certain segment exceeds the preset risk transition threshold, it is marked as a high-risk segment, thus obtaining a set of high-risk segments.

[0059] For the set of high-risk sections, the time-series change records of the corresponding sections are extracted from historical data, the stability of the change trend is analyzed, it is determined whether there is continuous risk accumulation, and the risk accumulation assessment results are obtained.

[0060] Based on the risk accumulation assessment results, if the continuous risk accumulation in certain sections reaches the preset standard, the latest environmental information of the corresponding sections will be obtained from external data sources to determine the degree of deviation between the current status and historical data.

[0061] Based on the degree of deviation between the current status and historical data, the changing trends of each segment are analyzed by comparison, and a random forest classifier is used to prioritize segments whose deviation exceeds the preset range to obtain a list of key monitoring segments.

[0062] Based on the list of key monitoring sections, real-time environmental dynamic data is obtained for the sections ranked first. Combined with the time-series changes of historical data, it is determined whether there are signs of further deterioration and dynamic risk assessment results are obtained.

[0063] Based on the dynamic risk assessment results, if signs of deterioration persist in certain sections, in-depth data mining will be conducted to analyze the specific driving factors of risk transitions and determine the final potential tipping point.

[0064] Specifically, after obtaining the heterogeneity labels for a given section, correcting for spatiotemporal heterogeneity in historical data using a time-series change tracking method can be understood as a data standardization process aimed at eliminating data bias caused by temporal or spatial differences. For example, if data collected from a section of a forest area shows significant fluctuations in different seasons—such as higher moisture content in summer and lower moisture content in winter—this heterogeneity may affect the accuracy of subsequent analyses. During correction, the data can be smoothed based on historical trends to ensure the reliability of subsequent calculations.

[0065] When calculating the risk transition probability for each segment based on the corrected data, the likelihood of a segment transitioning from a low-risk to a high-risk state can be inferred by analyzing historical trends. For example, if an environmental indicator for a segment has continuously deteriorated over the past three months, its risk transition probability is assessed at 0.75, exceeding the preset threshold of 0.6, and it is then marked as a high-risk segment. This method helps to identify potential problem areas in advance.

[0066] When analyzing the time-series changes in a set of high-risk sections, the stability of the trend is of paramount importance. For example, if the moisture content of a section gradually decreases from 40% to 25% over the past six months without significant fluctuations, it is judged that there is a continuous accumulation of risk. This analysis can help identify long-term hidden dangers.

[0067] If a persistent risk accumulation reaches a certain threshold, the latest environmental information can be obtained from external data sources, and current data can be acquired through real-time monitoring equipment. For example, if the latest moisture content in a certain section drops to 20%, which deviates significantly from the historical average, it indicates that the current condition may worsen further. This comparison helps to promptly grasp the current situation.

[0068] When prioritizing sections with significant deviations using a random forest classifier, a comprehensive evaluation of the sections can be conducted based on multiple environmental characteristics. For example, a section with a moisture content as low as 18% and located on a wind-prone slope would be given the highest priority. This prioritization method ensures more precise focus.

[0069] When acquiring real-time environmental dynamic data for top-ranked sections, the latest indicators can be obtained through sensors, and combined with historical trends to identify signs of deterioration. For example, if the moisture content of a certain section continues to decline and the wind speed has frequently exceeded 4.5 m / s recently, it is considered to be a sign of deterioration. This dynamic assessment can capture short-term changes.

[0070] Furthermore, if the potential critical point is determined to be of high probability, a differential early warning mechanism is activated. Based on the segment heterogeneity label and risk transition probability, a targeted risk level map is generated, resulting in a dynamic early warning signal, including:

[0071] If the potential critical point is determined to be a high-probability event, the difference warning mechanism is activated, relevant information is extracted from the preset segment heterogeneity label database, and combined with risk transition probability data, preliminary risk level distribution information is generated through comparative analysis to obtain an initial distribution map.

[0072] Based on the initial distribution map, for the segments whose risk level exceeds the preset level, historical time-series records are obtained, the matching degree between the segment heterogeneity labels and the current probability analysis results is analyzed, and the priority ranking of high-risk segments is determined.

[0073] The high-risk sections are prioritized and filtered using a preset threshold. If the risk level of a certain section exceeds the preset threshold, the real-time environmental information of the corresponding section is obtained from an external data source to determine the dynamic trend.

[0074] Based on the dynamic change trend, combined with historical time-series records and real-time environmental information, dynamic early warning signals that may be triggered are identified through logical comparison and analysis.

[0075] Specifically, when activating the differential early warning mechanism, information can be extracted from a pre-defined database of heterogeneous section labels, and combined with risk transition probability data to generate preliminary risk level distribution information. Suppose multiple sections within a forest area are labeled with different heterogeneous labels, such as steep terrain or sparse vegetation. By comparing these labels with probability data, the preliminary distribution map may show that some sections have higher risk levels. This distribution map provides a basic reference for subsequent analysis.

[0076] For sections whose risk level exceeds the preset level, when obtaining historical time-series records and analyzing the matching degree, attention can be paid to the changes in environmental indicators of a certain section over the past year. For example, if the vegetation cover of a certain section continues to decline, which is consistent with the current high-probability risk result, its priority will be increased. This matching analysis helps to accurately locate areas that require key attention.

[0077] When prioritizing and screening high-risk areas, if the preset threshold is 0.7 and the risk probability of a certain area reaches 0.8, then real-time environmental information needs to be obtained from external data sources. For example, if the latest data shows that the soil moisture in this area has plummeted to 15%, far below normal levels, then its dynamic trend warrants attention. This screening method ensures that resource allocation is more concentrated in high-risk areas.

[0078] When combining historical time-series records with real-time environmental information to identify dynamic early warning signals, the changing trends of a certain section can be analyzed through logical comparison. For example, if historical records show a continuous decline in humidity in a certain section, while real-time data indicates an abnormal increase in temperature recently, a preliminary list of early warning signals can be generated. This comparison method helps to identify potential problems.

[0079] Further, determining the final risk prediction result includes:

[0080] By acquiring the latest monitoring information from the real-time data stream, a preliminary comparison is made with the dynamic early warning signal to determine the degree of deviation from the historical records and obtain a preliminary verification result. Based on the preliminary verification result, a preset deviation threshold is used to filter the degree of deviation. If the deviation exceeds the threshold, relevant time segments are extracted from the data stream update to determine the triggering conditions of the abnormal signal.

[0081] Based on the triggering conditions of the abnormal signal, the prediction results output of the Long Short-Term Memory Network are obtained, and compared and analyzed in conjunction with the characteristics of the current dynamic changes in risk to obtain a preliminary adjustment plan for the risk level.

[0082] Based on the initial risk level adjustment plan, and combined with the signal verification logic, the persistence of abnormal signals is reconfirmed to determine whether they have a long-term impact and to determine the scope of application of the adjustment plan.

[0083] If the scope of application of the adjustment plan meets the preset conditions, the dynamic changes of risk are tracked in real time through the latest segment in the data stream update to obtain the dynamically adjusted risk level;

[0084] By dynamically adjusting the risk level and combining it with the rules for generating the final assessment conclusion, all relevant signals are comprehensively processed to obtain the final risk prediction result.

[0085] Specifically, when acquiring the latest monitoring information from real-time data streams and performing preliminary comparisons, we can envision a forest area risk monitoring scenario where environmental data such as temperature and humidity are collected hourly from the sensor network and compared with historical records from the past 30 days. Suppose the current humidity data for a certain area is 20%, while the historical average is 35%, showing a significant deviation. This comparison method helps to quickly detect abnormal fluctuations and provides a basis for subsequent verification.

[0086] When filtering the initial verification results using a preset threshold to determine the degree of deviation, assuming the preset humidity deviation threshold is 10%, if the deviation in the aforementioned segment reaches 15%, the system will automatically mark it as a potential anomaly and extract the most recent 24-hour time series segment from the data stream for in-depth analysis. This filtering mechanism effectively filters out irrelevant interference and focuses on truly noteworthy anomalies.

[0087] When determining the triggering conditions of abnormal signals and combining them with the prediction results of the Long Short-Term Memory (LSTM) network, analysis of the extracted time-series segments revealed that the humidity in this area had been continuously decreasing over the past 12 hours, and the prediction model indicated that it might further decrease to below 15% within the next 6 hours. Considering the current high-temperature characteristics, a preliminary assessment suggests that the risk level may need to be upgraded. This combination of prediction and feature analysis allows for a more comprehensive evaluation of dynamic trends.

[0088] When conducting a secondary confirmation of the initial risk level adjustment plan, if signal verification logic reveals that the humidity decline in the area has persisted for more than 48 hours without any significant signs of mitigation, it can be confirmed that the impact is long-term, and the adjustment plan applies to the area and surrounding related regions. This secondary confirmation avoids misjudgment and ensures the rationality of the adjustment.

[0089] When tracking risk levels in real time via data stream updates and obtaining dynamically adjusted risk levels, the latest data segment can be used to discover that the humidity in a certain area has dropped to 14%. Combined with other indicators, the risk level is adjusted from medium to high. This real-time tracking method can promptly reflect the latest situation and support subsequent decision-making.

[0090] Furthermore, after obtaining the final risk prediction result, the process also includes:

[0091] The final risk assessment results are used to transmit differentiated early warning information to the energy corridor management system, and the model parameters are optimized based on the feedback loop of the random forest classifier to obtain a refined risk judgment system.

[0092] Specifically, including:

[0093] Based on the final risk prediction results, differentiated early warning information is sent to the energy corridor management system to obtain the transmission status during the transmission process and determine the integrity of information transmission.

[0094] Based on the integrity of the transmission status, the reception status of the energy corridor management system is compared. If there is any missing data, the backup channel is triggered to supplement the transmission and obtain a complete record of the early warning information.

[0095] By recording complete early warning information, we can analyze the response of each key node in the energy corridor, obtain the processing time of the nodes for differentiated early warnings, and determine the response priority of each node.

[0096] Based on the response priority of each node, the distribution order of early warning information is adjusted, and information is pushed to high-priority nodes using preset scheduling rules, and the response feedback after the push is determined.

[0097] By analyzing the response feedback after the push and combining it with the feedback loop mechanism, we can optimize the direction of parameter adjustment of the model, obtain the adjusted parameter configuration, and obtain the basis for updating the model.

[0098] Based on the model's update criteria, parameters are applied to the refined risk assessment system to obtain the system's adaptability in different scenarios and determine the system's applicable scope.

[0099] Based on the system's scope of application, and targeting the long-term monitoring needs in energy corridors, a continuous early warning information distribution mechanism is constructed, the operational status of the distribution mechanism is obtained, and the final risk assessment strategy is determined.

[0100] Specifically, during the early warning information transmission process, the output of the final risk assessment results can be distributed to various management terminals through a customized interface, providing differentiated early warning information. For example, if a key node in an energy corridor is assessed as a high-risk area, early warning information containing a detailed risk description and emergency response recommendations will be generated first and sent to relevant management terminals via the main transmission channel, while simultaneously recording the transmission status. If the transmission status indicates insufficient information integrity, such as a 30% loss of data packets, the system will automatically activate a backup channel for supplementary transmission to ensure that the terminals receive complete information. This dual-channel mechanism effectively improves the reliability of information transmission.

[0101] When analyzing the integrity of transmission status and reception, the system can determine whether any data is missing by monitoring the data integrity feedback from the receiving end in real time. For example, if a terminal receives a warning message that only contains a risk description but lacks response suggestions, the system will immediately retransmit the missing portion through a backup channel and generate a complete warning message record. This method ensures that no information is missed and provides a complete basis for subsequent responses.

[0102] Analyzing the response of critical nodes allows for the determination of response priorities by recording the processing time of each node for early warning information. For example, if a node responds within 5 minutes of receiving an early warning, while another node takes 20 minutes, the system will mark the former as a high-priority node and adjust the order of early warning information distribution, prioritizing its delivery to the high-priority node. This dynamic adjustment ensures that important nodes receive information promptly.

[0103] In analyzing push response feedback, a feedback loop mechanism can be used to optimize model parameters. For example, if a node displays warning messages too frequently, leading to response fatigue, the system will adjust the warning trigger threshold to reduce unnecessary pushes. The optimized parameter configuration will then be used as the basis for model updates. This feedback mechanism helps improve the model's usability.

[0104] The parameters of the risk assessment refinement system can be tested for adaptability in different scenarios. For example, if the system accurately identifies risks under high-temperature, high-load conditions but performs poorly under low-load conditions, the system parameters will be adjusted according to the applicable range. This targeted adjustment improves the system's adaptability to different scenarios.

[0105] When building a continuous early warning information distribution mechanism, the distribution frequency and content templates can be set according to long-term monitoring needs. For example, if a corridor requires daily updates to its risk status, the system will automatically generate briefings and push them out on time, while simultaneously monitoring its operational status to ensure the stability of the distribution mechanism. This mechanism can support long-term risk management.

[0106] The final risk assessment strategy can be determined by comprehensively considering the performance of each of the above stages and formulating a tiered strategy. For example, high-risk areas can be monitored in real time with frequent alerts, while low-risk areas can have reduced update frequency. This strategy can rationally allocate resources and improve management efficiency.

[0107] This embodiment also provides an energy corridor forest fire risk prediction system based on multi-source data fusion, including:

[0108] The data fusion processing module is used to collect wind speed, wind direction, temperature, humidity and vegetation moisture content data of the target energy corridor, and extract spatiotemporal distribution records from the historical fire database to construct a fusion dataset.

[0109] The risk evolution parameter determination module is used to model the time series of meteorological fluctuations and vegetation differences using a long short-term memory network based on the fused dataset, and determine the risk evolution parameters.

[0110] The vegetation difference analysis module is used to trigger the vegetation difference analysis function when the risk evolution parameter exceeds a preset threshold. It extracts the vegetation type and water content distribution of a specific segment from the fused dataset, classifies the corresponding features through a random forest classifier, and obtains segment heterogeneity labels.

[0111] The risk transition probability calculation module is used to obtain the segment heterogeneity label and then perform spatiotemporal non-uniformity compensation and correction on historical data through the time series change tracking method to calculate the risk transition probability of each segment and then determine the potential critical point.

[0112] The dynamic early warning signal generation module is used to activate the differential early warning mechanism when the potential critical point is judged to be of high probability, and generate a targeted risk level map based on the segment heterogeneity label and risk transition probability to obtain a dynamic early warning signal.

[0113] The final risk assessment result determination module is used to iteratively verify the dynamic early warning signal using the real-time update stream in the fused dataset, adjust the risk level by combining the prediction output of the long short-term memory network, and determine the final risk prediction result.

[0114] The differentiated early warning information transmission module is used to transmit differentiated early warning information to the energy corridor management system based on the final risk assessment results. The refined risk judgment system is obtained by optimizing the model parameters based on the feedback loop of the random forest classifier.

[0115] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting forest fire risk in energy corridors based on multi-source data fusion, characterized in that, include: Data on wind speed, wind direction, temperature and humidity, and vegetation moisture content of the target energy corridor are collected, and spatiotemporal distribution records are extracted from the historical fire database to construct a fusion dataset. Based on the fusion dataset, a long short-term memory network is used to model the time series of meteorological fluctuations and vegetation differences to determine risk evolution parameters. If the risk evolution parameter exceeds the preset threshold, the vegetation difference analysis function is triggered to extract the vegetation type and water content distribution of a specific segment from the fused dataset, and the corresponding features are classified by a random forest classifier to obtain segment heterogeneity labels. After obtaining the segment heterogeneity labels, the historical data is corrected for spatiotemporal inhomogeneity through the time series change tracking method, the risk transition probability of each segment is calculated, and then the potential critical point is determined. If the potential critical point is judged to be of high probability, the differential early warning mechanism is activated, and a targeted risk level map is generated based on the segment heterogeneity label and risk transition probability to obtain a dynamic early warning signal. The dynamic early warning signal is iteratively verified using the real-time update stream from the fused dataset. The risk level is adjusted by combining the prediction output of the long short-term memory network to determine the final risk prediction result.

2. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 1, characterized in that, Constructing the fused dataset includes: Initial environmental datasets were constructed by collecting wind speed, wind direction, temperature and humidity values, and vegetation moisture content data of the target energy corridor using meteorological sensors and satellite remote sensing equipment. Based on the initial environmental dataset and combined with the spatiotemporal distribution records in historical fire records, the multi-source data is mapped into a unified grid system to complete the data alignment process and obtain the aligned grid dataset. For the aligned grid dataset, a standardization process is used to unify the units and adjust the dimensions of the wind speed data, wind direction data, temperature and humidity values, and vegetation moisture content data to obtain a standardized grid dataset. If the proportion of missing data fields in a standardized grid dataset exceeds a preset threshold, the data is completed by interpolation of neighboring grid cells to obtain the completed grid dataset, which is the fused dataset.

3. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 2, characterized in that, Determining the risk evolution parameters includes: Time-series data related to meteorological fluctuations and vegetation differences are extracted from the fused dataset. Through data segmentation, long-term trends and short-term changes are stored as independent data subsets to obtain a hierarchical time-series dataset. For the hierarchical time series dataset, a long short-term memory network is used to model short-term changes and long-term trends separately, capturing instantaneous features and evolution patterns to obtain the output results of the dynamic monitoring model; Based on the output of the dynamic monitoring model and combined with the distribution characteristics of environmental variables, the instantaneous characteristics of meteorological fluctuations and vegetation differences are weighted to determine the preliminary distribution of risk parameters. The initial distribution of risk parameters is mapped in a grid, and the evolution pattern of time series data is combined to identify potential high-risk areas. If the risk parameters of a certain high-risk area exceed the preset risk parameter threshold, it is marked as a key monitoring object, and a list of key areas is obtained. For the list of key areas, real-time meteorological fluctuations and vegetation differences are obtained. By comparing and analyzing with historical time-series data, it is determined whether short-term changes show abnormal trends and the dynamic risk level is determined. Based on the dynamic risk level and the long-term trend evolution pattern, the environmental variables of key monitoring objects are continuously tracked to determine the risk evolution parameters.

4. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 1, characterized in that, Obtaining the segment heterogeneity label includes: If the risk evolution parameter exceeds the evolution threshold, vegetation type and water content distribution data for a specific segment are extracted from the fused dataset to complete the preliminary data screening and obtain the segment basic dataset. For the aforementioned basic dataset, a random forest classifier is used to classify vegetation types and water content distribution, identify the heterogeneity characteristics of different segments, and obtain the classified segment heterogeneity labels.

5. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 1, characterized in that, Calculate the risk transition probability for each segment, and then determine potential critical points, including: After obtaining the segment heterogeneity labels, the historical data is corrected for spatiotemporal inhomogeneity using the time-series change tracking method. For the corrected data, the risk transition probability of each segment is calculated to determine a preliminary list of potential critical points. Based on the preliminary list of potential critical points, a preset threshold is used to filter the risk transition probability of each segment. If the risk transition probability of a certain segment exceeds the preset risk transition threshold, it is marked as a high-risk segment, thus obtaining a set of high-risk segments. For the set of high-risk sections, the time-series change records of the corresponding sections are extracted from historical data, the stability of the change trend is analyzed, it is determined whether there is continuous risk accumulation, and the risk accumulation assessment result is obtained. Based on the risk accumulation assessment results, if the continuous risk accumulation in certain sections reaches the preset standard, the latest environmental information of the corresponding sections will be obtained from external data sources to determine the degree of deviation between the current status and historical data. Based on the degree of deviation between the current status and historical data, the changing trends of each segment are analyzed by comparison, and a random forest classifier is used to prioritize segments whose deviation exceeds the preset range to obtain a list of key monitoring segments. Based on the list of key monitoring sections, real-time environmental dynamic data is obtained for the sections ranked first. Combined with the time-series changes of historical data, it is determined whether there are any signs of further deterioration and dynamic risk assessment results are obtained. Based on the dynamic risk assessment results, if signs of deterioration persist in certain sections, in-depth data mining will be conducted to analyze the specific driving factors of risk transitions and determine the final potential tipping point.

6. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 1, characterized in that, If the potential critical point is determined to be of high probability, the differential early warning mechanism is activated. Based on the segment heterogeneity label and risk transition probability, a targeted risk level map is generated, resulting in dynamic early warning signals, including: If the potential critical point is determined to be a high-probability event, the difference warning mechanism is activated, relevant information is extracted from the preset segment heterogeneity label database, and combined with risk transition probability data, preliminary risk level distribution information is generated through comparative analysis to obtain an initial distribution map. Based on the initial distribution map, for the segments whose risk level exceeds the preset level, historical time-series records are obtained, the matching degree between the segment heterogeneity labels and the current probability analysis results is analyzed, and the priority ranking of high-risk segments is determined. The high-risk sections are prioritized and filtered using a preset threshold. If the risk level of a certain section exceeds the preset threshold, the real-time environmental information of the corresponding section is obtained from an external data source to determine the dynamic trend. Based on the dynamic change trend, combined with historical time-series records and real-time environmental information, dynamic early warning signals that may be triggered are identified through logical comparison and analysis.

7. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 1, characterized in that, Determining the final risk prediction result includes: By acquiring the latest monitoring information from the real-time data stream, a preliminary comparison is made with the dynamic early warning signal to determine the degree of deviation from the historical records and obtain a preliminary verification result. Based on the preliminary verification result, a preset deviation threshold is used to filter the degree of deviation. If the deviation exceeds the threshold, relevant time segments are extracted from the data stream update to determine the triggering conditions of the abnormal signal. Based on the triggering conditions of the abnormal signal, the prediction results output of the Long Short-Term Memory Network are obtained, and compared and analyzed in conjunction with the characteristics of the current dynamic changes in risk to obtain a preliminary adjustment plan for the risk level. Based on the initial risk level adjustment plan, and combined with the signal verification logic, the persistence of abnormal signals is reconfirmed to determine whether they have a long-term impact and to determine the scope of application of the adjustment plan. If the scope of application of the adjustment plan meets the preset conditions, the dynamic changes of risk are tracked in real time through the latest segment in the data stream update to obtain the dynamically adjusted risk level; By dynamically adjusting the risk level and combining it with the rules for generating the final assessment conclusion, all relevant signals are comprehensively processed to obtain the final risk prediction result.

8. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 1, characterized in that, After obtaining the final risk prediction result, the following is also included: The final risk assessment results are used to transmit differentiated early warning information to the energy corridor management system, and the model parameters are optimized based on the feedback loop of the random forest classifier to obtain a refined risk judgment system.

9. The method for predicting forest fire risk in energy corridors based on multi-source data fusion according to claim 8, characterized in that, The refining risk assessment system is obtained by: Based on the final risk prediction results, differentiated early warning information is sent to the energy corridor management system to obtain the transmission status during the transmission process and determine the integrity of information transmission. Based on the integrity of the transmission status, the reception status of the energy corridor management system is compared. If there is any missing data, the backup channel is triggered to supplement the transmission and obtain a complete record of the early warning information. By recording complete early warning information, we can analyze the response of each key node in the energy corridor, obtain the processing time of the nodes for differentiated early warnings, and determine the response priority of each node. Based on the response priority of each node, the distribution order of early warning information is adjusted, and information is pushed to high-priority nodes using preset scheduling rules, and the response feedback after the push is determined. By analyzing the response feedback after the push and combining it with the feedback loop mechanism, we can optimize the direction of parameter adjustment of the model, obtain the adjusted parameter configuration, and obtain the basis for updating the model. Based on the model's update criteria, parameters are applied to the refined risk assessment system to obtain the system's adaptability in different scenarios and determine the system's applicable scope. Based on the system's scope of application, and targeting the long-term monitoring needs in energy corridors, a continuous early warning information distribution mechanism is constructed, the operational status of the distribution mechanism is obtained, and the final risk assessment strategy is determined.

10. A prediction system applied to the multi-source data fusion-based forest fire risk prediction method for energy corridors as described in any one of claims 1-9, characterized in that, include: The data fusion processing module is used to collect data on wind speed, wind direction, temperature and humidity, and vegetation moisture content of the target energy corridor, and extract spatiotemporal distribution records from the historical fire database to construct a fusion dataset. The risk evolution parameter determination module is used to model the time series of meteorological fluctuations and vegetation differences using a long short-term memory network based on the fused dataset, and determine the risk evolution parameters. The vegetation difference analysis module is used to trigger the vegetation difference analysis function when the risk evolution parameter exceeds a preset threshold. It extracts the vegetation type and water content distribution of a specific segment from the fused dataset, classifies the corresponding features through a random forest classifier, and obtains segment heterogeneity labels. The risk transition probability calculation module is used to obtain the segment heterogeneity label and then perform spatiotemporal non-uniformity compensation and correction on historical data through the time series change tracking method to calculate the risk transition probability of each segment and then determine the potential critical point. The dynamic early warning signal generation module is used to activate the differential early warning mechanism when the potential critical point is judged to be of high probability, and generate a targeted risk level map based on the segment heterogeneity label and risk transition probability to obtain a dynamic early warning signal. The final risk assessment result determination module is used to iteratively verify the dynamic early warning signal using the real-time update stream in the fused dataset, adjust the risk level by combining the prediction output of the long short-term memory network, and determine the final risk prediction result. The differentiated early warning information transmission module is used to transmit differentiated early warning information to the energy corridor management system based on the final risk assessment results. The refined risk judgment system is obtained by optimizing the model parameters based on the feedback loop of the random forest classifier.

Images