Real-time forest fire monitoring and early warning device and method based on combined acoustic, optical and thermal sensing

Abstract

The present invention discloses a real-time monitoring and early warning device and method for forest fires with combined acoustic, optical and thermal sensing, which relates to the technical field of fire monitoring and early warning. The method comprises: monitoring forest status information in real time through the combined acoustic, optical and thermal sensing equipment on the distributed tower, extracting the status information of a certain tower point and inputting it into the fire analysis device to obtain the fire analysis result. When the result shows that there are signs of fire (meeting the predetermined conditions), the support verification mechanism is enabled to calculate the comprehensive support. If the support reaches the preset threshold, the early warning signal is activated and a fire warning is issued for the tower point. It solves the technical problem that the existing forest fire monitoring and early warning technology has shortcomings in terms of single data dimension and improvement of result credibility, which makes it difficult to timely and accurately identify early fire conditions. By enriching the data dimension and enhancing the credibility of the results, the technical effect of more timely and accurate identification of early fire conditions is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of fire monitoring and early warning, and in particular to a real-time forest fire monitoring and early warning device and method using combined acoustic, optical and thermal sensing. Background Art

[0002] With global climate change and the increase in extreme weather events, the frequency and severity of forest fires are increasing. Traditional forest fire monitoring relies heavily on single-type sensors or manual patrols. This approach struggles to capture early fire characteristics in environments with complex terrain and dense vegetation, leading to increasingly prominent issues such as information lag and inaccurate identification. Furthermore, the volatile forest environment and the uneven spatial and temporal distribution of fire signals exacerbate the difficulty of monitoring and early warning. Therefore, with the ever-increasing demand for fire prevention, effectively addressing monitoring difficulties and uncertainties, and reducing the interference of environmental factors on data collection and analysis, have become pressing challenges.

[0003] Among the current relevant technologies, forest fire monitoring and early warning technologies have shortcomings in terms of single data dimensions and improving the credibility of results, which leads to technical problems such as difficulty in timely and accurate identification of early fire conditions. Summary of the Invention

[0004] This application solves the technical problem that existing forest fire monitoring and early warning technologies have shortcomings in terms of single data dimension and improved result credibility, which makes it difficult to identify early fire conditions in a timely and accurate manner, by providing a real-time forest fire monitoring and early warning device and method with combined sound, light and heat perception.

[0005] This application provides a real-time forest fire monitoring and early warning device using combined acoustic, optical, and thermal sensing, including:

[0006] A target real-time status information monitoring module, the target real-time status information monitoring module is used to obtain the target real-time status information of the target forest through dynamic monitoring of the sound, light and heat combined sensing device, wherein the sound, light and heat combined sensing device is mounted on the distributed tower of the target forest; a first status information extraction module, the first status information extraction module is used to extract the first status information corresponding to the first tower point in the target real-time status information, and the first tower point refers to any one of the distributed towers; an output data acquisition module, the output data acquisition module is used to use the first status information as input data of a fire analysis device, and through the fire analysis device Output data is obtained, wherein the output data includes a first fire assessment result of the first tower point; a predetermined support verification mechanism reading module, wherein the predetermined support verification mechanism reading module is used to read a predetermined support verification mechanism when the first fire assessment result meets the predetermined verification constraint; a first comprehensive support degree acquisition module, wherein the first comprehensive support degree acquisition module is used to obtain a first comprehensive support degree of the first fire assessment result according to the predetermined support verification mechanism; a fire warning module, wherein the fire warning module is used to activate a warning signal to issue a fire warning to the first tower point of the target forest when the first comprehensive support degree reaches a predetermined support degree threshold.

[0007] This application provides a real-time forest fire monitoring and early warning method using combined acoustic, optical, and thermal sensing, including:

[0008] The target real-time status information of the target forest is obtained through dynamic monitoring by an acoustic, optical and thermal combined sensing device, wherein the acoustic, optical and thermal combined sensing device is mounted on a distributed tower of the target forest; the first status information corresponding to the first tower point in the target real-time status information is extracted, and the first tower point refers to any one of the distributed towers; the first status information is used as input data of a fire analysis device, and output data is obtained through the fire analysis device, wherein the output data includes a first fire analysis result of the first tower point; when the first fire analysis result meets a predetermined verification constraint, a predetermined support verification mechanism is read; a first comprehensive support degree of the first fire analysis result is obtained according to the predetermined support verification mechanism; when the first comprehensive support degree reaches a predetermined support degree threshold, an early warning signal is activated to issue a fire early warning to the first tower point of the target forest.

[0009] The real-time monitoring and early warning device and method for forest fires using the combined acoustic, optical and thermal sensing proposed in this application first monitors forest status information in real time through the combined acoustic, optical and thermal sensing equipment on the distributed towers, extracts the status information of a certain tower point from it and inputs it into the fire analysis device to obtain the fire analysis results. When the results show signs of fire (meeting the predetermined conditions), the support verification mechanism is enabled to calculate the comprehensive support. If the support reaches the preset threshold, the early warning signal is activated and a fire warning is issued for the tower point. By enriching the data dimensions and enhancing the credibility of the results, the technical effect of more timely and accurate identification of early fire conditions is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact sequence. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A schematic diagram of the structure of a real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing provided in an embodiment of the present application;

[0012] Figure 2 A flow chart of the real-time forest fire monitoring and early warning method using combined acoustic, optical and thermal sensing provided in an embodiment of the present application.

[0013] Explanation of the accompanying symbols: target real-time status information monitoring module 10, first status information extraction module 20, output data acquisition module 30, predetermined support verification mechanism reading module 40, first comprehensive support degree acquisition module 50, fire warning module 60. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms “first\second” involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The embodiment of the present application provides a real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing, such as Figure 1 As shown, the device includes:

[0018] The target real-time status information monitoring module 10 is used to dynamically monitor and obtain real-time status information of the target forest using combined acoustic, optical, and thermal sensing devices, which are installed on distributed towers within the target forest. Specifically, within the target forest fire monitoring system, the target real-time status information monitoring module 10 achieves dynamic monitoring tasks based on the combined acoustic, optical, and thermal sensing devices distributed across the towers. The process is as follows: First, the tower site is determined based on terrain, vegetation, meteorological data, and historical fire data, and the equipment is installed. Acoustic, optical, and temperature sensors are calibrated. For acoustic monitoring, high-sampling-rate signals are collected and subjected to time-frequency analysis to identify sound. Ambient sound and fire-related abnormal sounds are distinguished by frequency range. Feature parameters are extracted to locate the sound source, and machine learning is used for classification. For optical monitoring, infrared and visible light images are collected and pre-processed using a deep learning model to identify visual features and fire parameters such as smoke, flames, and vegetation discoloration. Temperature monitoring uses matrix-based data acquisition to construct a three-dimensional temperature field. Meteorological elements are integrated to simulate heat exchange, locate abnormal areas, set thresholds for early warning, and generate and visualize data sets. The back-end data center receives multi-source data, processes it based on fusion algorithms after spatiotemporal registration, and combines it with the geographic information system to generate visual comprehensive status information, providing accurate data support for forest fire prevention and control, building a complete monitoring architecture, and improving fire warning efficiency.

[0019] The first state information extraction module 20 is used to extract the first state information corresponding to the first tower point in the target real-time state information. The first tower point refers to any one of the distributed towers. Specifically, the first state information extraction module 20 extracts the first tower point-specific information from the target real-time state information. First, the received data is preprocessed to unify the acoustic, optical, and temperature data formats and remove noise. Acoustic feature extraction uses MFCC and LPC to extract spectral characteristics, and HMM to mine time series variations. Based on the acoustic propagation model, attenuation is compensated to capture the acoustic characteristics of the fire. For optical feature extraction, image segmentation and CNN are used to analyze the characteristics of smoke, flames, and vegetation discoloration. Temperature feature extraction relies on a time series analysis model to decompose regularities and anomalies, using a spatial interpolation algorithm to infer the temperature field distribution. Based on the laws of thermodynamics, thermophysical parameters are calculated to assess thermal hazards. Finally, adaptive weighted fusion is performed to dynamically adjust the weights of each feature based on its contribution. Standardization and normalization are then performed to generate accurate first state information, providing a decision-making basis for fire assessment and enhancing the effectiveness of forest fire monitoring and early warning.

[0020] In one possible implementation, the first state information extraction module further includes an acoustic, optical, and thermal combined sensing device component unit. The acoustic, optical, and thermal combined sensing device component unit includes an acoustic sensor, an optical sensor, and a temperature sensor. Specifically, the acoustic, optical, and temperature combined sensing device component unit integrates acoustic, optical, and temperature sensors to form a multimodal monitoring architecture. The acoustic sensor, based on acoustic principles, captures sound wave vibrations using a highly sensitive microphone array or piezoelectric element. Its design parameters are adapted to the complex acoustic environment of the forest, accurately sensing sound wave frequency, amplitude, and phase information. The optical sensor integrates infrared thermal imaging and visible light imaging technologies, focusing light with an optical lens, filtering light with a filter, and detecting changes in light intensity with a detector to achieve multispectral image acquisition and accurately image objects based on their thermal radiation and optical reflectance properties. The temperature sensor uses a thermistor, thermocouple, or infrared temperature probe to sense temperature based on the thermoelectric effect and thermistor characteristics. Calibration and compensation are performed to improve measurement accuracy. A three-dimensional thermal field monitoring network is formed at multiple points around the tower. These three sensors work together to provide comprehensive, multi-dimensional data support for forest state monitoring.

[0021] The first sound information monitoring unit is configured to obtain first sound information from the first tower location using the acoustic sensor. Specifically, the first sound information monitoring unit drives the acoustic sensor to conduct sound monitoring at the first tower location. Based on a preset sampling frequency and quantization accuracy standard, the sensor converts the acoustic pressure signal into a digital audio sequence, establishing the foundation for the digitization of the sound signal. Fast Fourier Transform (FFT) technology is used to analyze the audio spectrum structure, accurately extracting frequency components and amplitude distribution characteristics. Time-frequency analysis methods are used to capture the dynamic changes of the signal over time and identify characteristic patterns of potential abnormal acoustic events. Signal processing techniques such as wavelet transform and Hilbert-Huang transform are used to decompose complex signals, extract transient and non-stationary characteristic components, and effectively capture key information such as the high-frequency crackling sound caused by burning trees and the low-frequency interference sound generated by surging airflow. Based on the physical model of sound propagation, the sensor compensates for signal loss caused by factors such as distance attenuation, environmental absorption, and scattering, restoring the acoustic parameters of the sound source to generate the first sound information, providing key acoustic dimension data support for subsequent fire identification.

[0022] An image recognition and analysis unit is configured to perform image recognition and analysis on the first infrared image of the first tower point using the optical sensor to obtain first optical information. Specifically, the image recognition and analysis unit controls the optical sensor to capture the first infrared image based on the first tower point and performs deep image analysis. Preprocessing techniques such as grayscale stretching and histogram equalization are used to enhance image contrast and detail recognition, thereby optimizing image quality. A target detection model is constructed based on a deep learning convolutional neural network (CNN) architecture and is deeply trained and optimized using a dataset of labeled fire image samples. The model automatically learns and accurately identifies the hazy texture characteristics of smoke, the irregular shape characteristics of flames, and the characteristic patterns of thermal discoloration of vegetation. It accurately locates, classifies, and delineates target areas in the image, and outputs structured information such as target category probability, location coordinates, and size parameters. An image segmentation algorithm is used to analyze the smoke concentration distribution gradient, the structural details of the flame combustion layer, and the proportion of damaged vegetation area, thereby comprehensively capturing the fire information contained in the image. The multi-dimensional analysis results are integrated and quantified into first optical information, providing image data support for accurate fire situation assessment.

[0023] The first temperature information acquisition unit is used to obtain the first temperature information of the first tower point through the temperature sensor monitoring. Specifically, the first temperature information acquisition unit monitors the dynamic changes of the temperature field in real time at the first tower point based on the temperature sensor network. Based on the principle of thermal balance, the sensor measures the surface temperature of air, soil and vegetation, and constructs a continuous temperature time series data set after analog-to-digital conversion and data filtering. Statistical analysis techniques are used to obtain the variation characteristics of the temperature mean, extreme value, standard deviation, etc. in the data. The spatial distribution characteristics of the temperature field are analyzed based on the thermodynamic gradient theory. With the help of spatial interpolation methods such as Kriging interpolation and spline interpolation, the data gaps in the monitoring blind spots are filled. A continuous temperature contour map is drawn to accurately display the distribution pattern of heat flow direction and intensity. Combined with real-time meteorological data and coupled with the heat exchange physical model, the environmental heat balance relationship and the changes in heat conduction flux are quantified, the root cause of abnormal temperature fluctuations is found, and the first temperature information is extracted to form a solid foundation of key thermal data for in-depth insight into fire thermal hazards.

[0024] A variation weighted processing unit is configured to sequentially perform variation weighted processing on the first sound information, the first optical information, and the first temperature information to obtain a first sound coefficient, a first optical coefficient, and a first temperature coefficient, respectively. Specifically, the variation weighted processing unit performs weighted processing on the first sound information, the first optical information, and the first temperature information. Based on the dynamic changes in the importance of different information sources to fire characterization over time, space, and environmental factors, a multi-dimensional comprehensive evaluation index system is constructed, encompassing information discernibility, reliability, and fire relevance. In the information discernibility dimension, signal-to-noise ratio and feature significance quantitative indicators are analyzed; in the reliability dimension, sensor stability and data consistency assessment parameters are analyzed; and in the fire relevance dimension, the correlation between each information feature and key factors such as fire spread rate and intensity change is deeply analyzed. Subsequently, using weight determination methods such as the Analytic Hierarchy Process and the Entropy Weight Method, weight coefficients for each information source are calculated based on the evaluation index system, and the weight configuration is dynamically adjusted and optimized based on real-time monitoring data. For example, if acoustic information is highly sensitive in the early stages of a fire, the weight of acoustic information is appropriately increased. In the developing stage of a fire, where optical features are critical, the optical information coefficient is weighted more heavily. After weighted calculation and normalization processing, the first sound coefficient, the first optical coefficient and the first temperature coefficient are accurately generated, providing an information basis for accurate fire judgment.

[0025] The first state information composition unit is used to form the first state information by combining the first sound coefficient, the first optical coefficient and the first temperature coefficient. Specifically, the first state information composition unit performs data fusion on the first sound coefficient, the first optical coefficient and the first temperature coefficient. According to the predefined coefficient weight distribution combination rule, a multi-dimensional state vector is constructed, and each component of the vector accurately quantifies the contribution of acoustic, optical and temperature dimensional information to the fire situation. Standardization processing technology is used to unify the dimensions of information in each dimension, effectively eliminating the impact caused by magnitude differences, systematically organizing and integrating multivariate information, and finally forming accurate and normalized first state information. It provides a data basis for accurate decision-making, improves the intelligent decision-making ability of the forest fire monitoring system, and optimizes the formulation and implementation of fire prevention and control strategies.

[0026] In one possible implementation, the image recognition and analysis unit further includes a first infrared pixel acquisition subunit, configured to acquire a first infrared pixel in the first infrared image using the optical sensor. Specifically, the subunit first interacts with the optical sensor's hardware driver, technical manual, and pre-calibration data to acquire acquisition parameters specific to the first tower location, such as lens focal length, aperture, detector sensitivity, and infrared band filter characteristics. The sensor is then driven to acquire an infrared image, with the lens focused on the forest area. The infrared radiation excites the detector's photosensor, generating an electrical signal. The signal undergoes low-noise amplification and filtering by a readout circuit to remove noise and distortion. The signal is then converted to a digital signal by an analog-to-digital converter at a preset sampling frequency and quantization accuracy. This signal is then formed into a two-dimensional pixel matrix according to pixel array rules, yielding the first infrared pixel. The entire process strictly adheres to sensor specifications such as pixel resolution, response band, and sensitivity, ensuring that the pixels accurately reflect differences in infrared radiation intensity, effectively capturing fire-related bands and providing high-quality data for subsequent optical analysis. This establishes a precise conversion channel from infrared radiation to digital images, meeting the front-end data quality requirements for fire thermal radiation monitoring and laying a foundation for accurate image information for fire monitoring.

[0027] The first light color parameter acquisition subunit is configured to analyze the first RGB corresponding to the first infrared pixel to obtain the first light color parameter. Specifically, after receiving the first infrared pixel data, the subunit performs analysis based on the optical sensor's radiation intensity-to-RGB conversion algorithm. This algorithm comprehensively analyzes the detector's spectral response characteristics, the material's optical constants, and ambient light interference to ensure the accuracy of the conversion to the RGB color space. High-precision spectral analysis technology is used to measure the proportion of radiation intensity in each band of the pixel. Combined with a calibrated color correction matrix, the values of the three color components are determined and their proportional relationships are calculated. These values are then compared with a standard color sample library and the color tendency characteristics are determined using algorithms such as the CIEDE2000 color difference formula. The absolute values of each component are calculated based on the absolute value of the pixel's radiation intensity and the detector's calibration parameters to construct the first light color parameter. This parameter reflects the interaction between the object's thermal radiation, the material's optical properties, and the ambient light. For example, pixels in the burned area of a forest fire have unique color combinations in the RGB space. This provides key color information for identifying the optical characteristics of the fire, improving the ability to extract fire-related features and providing color dimension information for fire status assessment.

[0028] The first light intensity parameter acquisition subunit is used to analyze the first infrared pixel corresponding to the first HSB to obtain the first light intensity parameter. Specifically, the light intensity analysis of the first infrared pixel is performed based on the HSB color model. First, according to the energy response curve of the detector calibrated by the blackbody radiation standard source and the Planck blackbody radiation law formula, the pixel radiation energy is accurately converted into the HSB brightness value. Based on the fact that the contrast of the local area affects the visual perception of light intensity, the contrast is measured using algorithms such as the grayscale co-occurrence matrix and the edge detection operator, and a contrast-brightness dynamic correction model is constructed. According to the contrast change, a brightness perception weight adjustment function is set to accurately correct the brightness value. At the same time, the saturation-brightness weighting function is determined by experimental data fitting and intelligent model optimization, and the brightness value is further corrected to obtain an accurate first light intensity parameter. The parameters can accurately capture the absolute value and dynamic changes of light intensity. In forest fire monitoring, they can monitor the fluctuations in flame radiation intensity and the attenuation gradient of smoke diffusion light intensity in real time, providing key light intensity quantitative parameters for fire monitoring, helping to judge the fire and smoke spread trends from the perspective of light intensity, improving the fire optical monitoring and evaluation system, and enhancing the data's ability to characterize fire evolution, providing a reliable basis for fire decision-making and enhancing the scientificity and effectiveness of monitoring.

[0029] The first optical information composition subunit is used to combine the first light color parameter and the first light intensity parameter to form the first optical information. Specifically, the first optical information composition subunit organically integrates the first light color parameter and the first light intensity parameter to construct the complete first optical information. Parameters are organized in a structured vector format, with the vector elements being the light color parameter (including the RGB ratio values and the absolute values of each component) and the light intensity parameter (including the HSB brightness value and the corrected value obtained through a complex correction process). To ensure high consistency and comparability of the optical information corresponding to different pixels, the Z-score normalization method is used to unify the parameters according to general data normalization theories and methods. The Z-score normalization method is based on statistical principles and converts the parameter values to values under a standard normal distribution by calculating the deviation of each parameter value from the mean and dividing it by the standard deviation. Validated by large-scale sample testing and in-depth theoretical derivation and optimization, this method can effectively eliminate the impact of factors such as pixel acquisition device performance differences, interference from complex environmental factors, and signal transmission noise on the accuracy and comparability of optical information, ensuring that all optical information can be accurately measured and compared using the same standard scale. The vector fully encapsulates the full-dimensional optical characteristics of the pixel, reflecting the fire optical characteristics of the first infrared pixel from the dual core perspectives of color vividness and light intensity level accuracy.

[0030] The output data acquisition module 30 is used to use the first state information as input data of the fire analysis device, and obtain output data through the fire analysis device, wherein the output data includes the first fire analysis result of the first tower point. Specifically, the output data acquisition module 30 receives the first state information from the first state information extraction module 20, checks and standardizes its format, and inputs it into the fire analysis device. The fire analysis device loads the fire discrimination model trained based on historical data. The input first state information is subjected to feature extraction, including acoustic spectrum and time-frequency features, optical image features, temperature field features, etc., and is normalized and weighted to form a comprehensive feature vector. According to the model's analysis logic decision, the probability of fire occurrence is calculated and compared with the threshold to determine the "fire" or "no fire" result, and at the same time generate corresponding auxiliary information. Finally, the output data acquisition module 30 packages and transmits the data including the first fire analysis result and auxiliary information to the subsequent modules according to the protocol, thereby improving the coordination and response efficiency of the forest fire prevention and control system and ensuring forest safety.

[0031] In a possible implementation, the output data acquisition module 30 further includes: a forest fire log acquisition unit, the forest fire log acquisition unit is used to obtain a forest fire log, and the forest fire log includes a first record and a second record. Specifically, the forest fire log acquisition unit interacts with data sources such as the forestry management department database, the fire monitoring station historical record library, and field survey archives to collect fire logs in specific forest areas, covering long-term fire-related information, including a first record and a second record. The first record focuses on detailed status information of the forest during past fire periods, such as meteorological, vegetation, topographical, and multi-source data collected by various monitoring equipment, forming first historical status information, and annotated with a first historical fire label; the second record focuses on recording the state of the forest during periods when no fire occurred, including meteorological, vegetation, topographical, and monitoring data, etc., constituting second historical status information and a second historical no-fire label.

[0032] The first data group forming unit is used to form a first data group based on the first historical state information and the first historical fire label in the first record. Specifically, after the first data group forming unit obtains the first historical state information and the first historical fire label in the first record, it performs the forming operation. First, the first historical state information is preprocessed: meteorological data is cleaned to remove outliers, interpolated to supplement missing values, and smoothed to reduce noise; vegetation cover data is corrected by cross-validation using satellite remote sensing and field surveys; topographic data is digitized and modeled using GIS technology; multi-source monitoring data is subjected to signal enhancement, feature extraction and normalization. After the preprocessing is completed, the processed information is paired with the fire label to form the first data group, and each sample contains comprehensive forest status information and labels during the fire period.

[0033] The second data group forming unit is used to form a second data group based on the second historical state information and the second historical no-fire label in the second record. Specifically, the second data group forming unit forms a data group based on the second historical state information and the second historical no-fire label of the second record. Its preprocessing steps are similar to those of the first data group: meteorological data is ensured in the same way to ensure quality; vegetation cover data is corrected with the help of satellite remote sensing and field surveys; topographic data is improved in accuracy using GIS technology; multi-source monitoring data is processed to be comparable. After preprocessing, the information is paired with the label to form a second data group. The samples represent the state of the forest when no fire occurs and the no-fire mark, providing high-quality negative samples for the fire analysis device. It helps the model learn non-fire feature patterns, accurately distinguish whether there is a fire or not, reduce the false alarm rate, provide comprehensive data support for the precise prevention and control of forest fires, ensure forest resources and ecological security, and enable it to accurately judge fires in complex forest environments and prevent and control them in a timely manner.

[0034] The fire assessment detector acquisition unit is configured to perform supervised learning on the first and second data sets after sample augmentation based on the principles of support vector machines to obtain the fire assessment detector. Specifically, the fire assessment detector acquisition unit uses the preprocessed first and second data sets to construct a fire assessment detector based on the principles of support vector machines. Feature engineering techniques are used to mine sample features: computational interaction and time series features from meteorological data; multiple features are extracted from vegetation cover data; computational complexity and coupling features from topographic data; and time-frequency domain features are extracted from multi-source monitoring data to enhance separability. Sample augmentation techniques are then used to expand the dataset through random perturbations and data synthesis to prevent overfitting. The augmented positive and negative sample data sets are then input into a support vector machine model for supervised learning. The hyperplane is optimized by adjusting the kernel function type and penalty parameter. After multiple rounds of iteration, training is terminated when optimal performance on the validation set is achieved, resulting in the fire assessment detector. The assessment detector can accurately determine the likelihood of fire based on forest status information, providing support for prevention and control decisions, improving the scientific nature and effectiveness of forest fire prevention and control, and playing a key role in forest ecosystems.

[0035] The predetermined support verification mechanism reading module 40 is used to read the predetermined support verification mechanism when the first fire analysis result meets the predetermined verification constraint. Specifically, after the first fire analysis result is generated, the predetermined support verification mechanism reading module 40 performs a verification process to monitor whether the result meets the preset "fire" verification constraint. If it meets the requirements, it will interact with a database or storage system that specifically stores the predetermined support verification mechanism to read the mechanism content completely and accurately according to the established procedures and protocols. This mechanism covers multi-level verification, including re-analysis of acoustic, optical, and temperature sensor raw data, review of fire analysis algorithm parameters and model structure, review of analysis logic reasoning, and introduction of external auxiliary information (such as surrounding weather station data, human activities in the forest, and lightning activity records, etc.) for cross-comparison. By implementing this mechanism, the risk of false alarms can be effectively reduced, the accuracy and reliability of fire judgment can be improved, and a reliable decision-making basis can be provided for subsequent emergency responses, reducing fire losses and ensuring the safety and stability of forest ecology.

[0036] The first comprehensive support acquisition module 50 is used to obtain the first comprehensive support of the first fire assessment result according to the predetermined support verification mechanism. Specifically, after the predetermined support verification mechanism is read, the first comprehensive support acquisition module 50 first organizes the verification dimensions of the mechanism. In the data integrity and accuracy verification, the acoustic, optical, and temperature sensor data are processed and analyzed separately, compared with the corresponding feature library, and the support score S1 is calculated based on the weight; the model algorithm reliability verification dimension is to evaluate the accuracy and stability indicators by running the model on a simulated data set, and give the support score S2 based on the historical performance; the logical reasoning rationality verification dimension is to reconstruct the logical deduction steps of the assessment, and summarize the rationality scores of each link to obtain S3; the external auxiliary information verification dimension is to integrate information such as meteorology, human activities, and lightning activities, and calculate S4 by weighted calculation. Finally, the first comprehensive support is obtained by summing up S1, S2, S3, and S4 according to their weights (w1, w2, w3, w4 and the sum is 1, determined according to the importance and reliability of each dimension), which provides a quantitative decision-making basis for subsequent fire judgment and measures, and improves the reliability and effectiveness of prevention and control decisions.

[0037] In one possible implementation, the first comprehensive support acquisition module 50 further includes: a first support verification mechanism extraction unit, configured to extract a first support verification mechanism from the predetermined support verification mechanism. Specifically, upon receiving the predetermined support verification mechanism, the first support verification mechanism extraction unit performs an extraction process. This mechanism is typically a complex, multi-dimensional set of rules and processes designed to verify fire assessment results. Based on a preset classification standard and indexing system, the first support verification mechanism is identified and extracted from the predetermined support verification mechanisms. The mechanism is based on a specific verification direction, such as a verification dimension based on geographic spatial relationships. Its core lies in utilizing the distribution characteristics of tower points in the forest and the geographic spatial patterns of fire spread to construct a set of targeted verification rules. This allows the fire assessment results of the first tower point to be further corroborated or corrected through information from surrounding tower points. This provides a key rule basis for improving the accuracy of fire assessments, ensures that the verification process has a clear direction and focus, and avoids ineffective and aimless verification operations, thereby efficiently utilizing limited resources and data and improving the reliability and stability of the entire fire monitoring system.

[0038] The first neighborhood determination unit is used to determine the first neighborhood of the first tower point according to the first support verification mechanism, and the first neighborhood includes multiple tower points. Specifically, the first neighborhood determination unit determines the first neighborhood of the first tower point after obtaining the first support verification mechanism. The determination method is based on factors such as geographical distance, topography, similarity of vegetation cover types or range of fire propagation risk: when geographical distance is the main factor, a radius is set to define the neighborhood; considering topography, points in the same valley, hillside or similar altitude slope are given priority; based on vegetation similarity, points with similar vegetation community distribution are selected. The neighborhood set constructed in this way is closely related to the first tower point, providing a targeted sample space for subsequent information collection and analysis, so that the surrounding point information can more effectively verify the fire analysis results of the first tower point, and enhance the persuasiveness and credibility of the verification.

[0039] An arbitrary tower point extraction unit, the arbitrary tower point extraction unit is used to randomly extract any tower point from the multiple tower points, and the arbitrary tower point corresponds to arbitrary status information. Specifically, after the arbitrary tower point extraction unit identifies multiple tower points in the first neighborhood, it uses a sampling method based on a random number generation algorithm to extract an arbitrary tower point. This algorithm generates a random index value within the neighborhood point set, and selects the corresponding point to ensure that each point has the same probability of being drawn, avoids human interference and fixed pattern interference, and ensures that the sampling is random and representative. During multiple verifications, different points can be drawn in different rounds, and diverse status information can be collected to fully reflect the overall situation of the neighborhood, providing rich comparative data for the analysis of fire analysis results, helping to discover abnormal problems and improve the accuracy and reliability of analysis.

[0040] Any fire assessment result analysis unit, the said any fire assessment result analysis unit is used to analyze the said any state information through the said fire assessment device to obtain any fire assessment result. Specifically, after extracting any tower point, the any fire assessment result analysis unit obtains its any state information and inputs it into the fire assessment device. The assessment device uses the pre-trained model algorithm to analyze the spectrum of the acoustic data, extract the time-frequency domain features, and judge the possibility of fire according to the acoustic model; uses image recognition and visual algorithms for optical data, combined with the feature library, to judge the signs of fire according to the smoke and flame characteristics; uses the heat propagation theory for temperature data, and infers the possibility of fire according to its value, change trend, and gradient. After comprehensive analysis of multi-source data and model calculation, the assessment result of "fire" or "no fire" is output, which provides a key basis for the calculation of the first support degree, verifies the assessment result of the first tower point from the perspective of surrounding points, and improves the intelligent analysis and decision-making capabilities of the monitoring system.

[0041] The first support acquisition unit is used to obtain the first support of the first fire assessment result according to the arbitrary fire assessment result. Specifically, the first support acquisition unit calculates the first support of the first fire assessment result after obtaining the arbitrary fire assessment result. First, a support evaluation model is constructed, and multiple factors are considered for quantification. If the two results are consistent (both are "there is a fire"), points are dynamically added according to the pre-set scoring rules, combined with the correlation between the surrounding points and the first tower point (such as close geographical distance, similar environmental characteristics, good data acquisition equipment) and data reliability factors; if they are inconsistent (one is "there is a fire" and the other is "no fire"), points are deducted according to the difference deduction rules, also based on the above factors. A specific value is calculated through this dynamic scoring mechanism, which intuitively reflects the degree of support for the first fire assessment result by the assessment result of any tower point, and provides a quantitative basis for evaluating its reliability.

[0042] The first comprehensive support acquisition unit is used to use the first support as the first comprehensive support. Specifically, the first comprehensive support acquisition unit outputs the first support as the first comprehensive support, and will first perform preprocessing and standardization operations on it. If the calculation scale and unit of the first support are different, it will be converted into a unified standard scale to facilitate comparison and analysis with other support indicators; at the same time, its numerical range will be checked and corrected to make it consistent with the expectations and logic of the evaluation system. After processing, the first comprehensive support can accurately reflect the comprehensive support level for the first fire assessment result based on the first support verification mechanism and the surrounding tower point information, and become an intuitive and reliable quantitative indicator for subsequent decision-making. Decision makers can judge the credibility of the first fire assessment result based on its numerical value and decide whether to take further action, such as launching a higher-level monitoring program, deploying fire-fighting resources, etc., so as to improve the efficiency and pertinence of forest fire prevention and control, minimize fire losses, and maintain forest ecological stability and safety.

[0043] In a possible implementation, the first comprehensive support acquisition unit further includes: a first mapping relationship forming subunit, which is used to form a first mapping relationship between the first tower point and the first fire assessment result. Specifically, the first mapping relationship forming unit constructs a first mapping relationship between the first tower point and the first fire assessment result. First, accurately locate and identify the first tower point, and clarify its geographical location, altitude, surrounding topography, covered forest area and vegetation type and other key information; then analyze the details of the first fire assessment result, such as the possibility of fire, fire level, spread direction and confidence index. Then, using the database management system, the geographical space of the point is bound to the environmental feature information and fire-related information to form a two-way mapping relationship. Through this relationship, information can be queried in both directions, providing a clear and efficient information association basis for fire monitoring, analysis and decision-making, making the information flow smoother and more accurate, which is conducive to understanding the fire situation, helping to take prevention and control measures in a timely manner, and enhancing the pertinence and timeliness of forest fire prevention and control.

[0044] The second mapping relationship forming subunit is used to form a second mapping relationship between the arbitrary tower point and the arbitrary fire assessment result. Specifically, the second mapping relationship forming unit constructs the second mapping relationship, and the process is similar to the formation of the first mapping relationship. First, the detailed information of the arbitrary tower point is collected, covering the geographical coordinates, surrounding environment characteristics, data acquisition equipment performance parameters, working status, etc., to fully grasp its basic information and monitoring capabilities. Then, an in-depth analysis of the arbitrary fire assessment results is performed, including assessment results, risk factors and corresponding quantitative evaluation indicators. The data association tool is used to closely associate the detailed information of the arbitrary tower point with the arbitrary fire assessment result to establish a second mapping relationship. This relationship records the fire assessment situation of each point, provides rich materials for comprehensive analysis, and compares the mapping relationships of different points to discover the spatial propagation trend of fire, regional risk differences and monitoring data, which helps to have a more comprehensive and in-depth understanding of the target forest fire situation.

[0045] The visual map construction subunit is responsible for constructing a target forest fire assessment visual map for the target forest based on the first and second mapping relationships. Specifically, the visual map construction unit constructs the target forest fire assessment visual map based on the first and second mapping relationships. First, the GIS data for the target forest is imported into the visualization software platform, creating a geospatial framework containing layer information such as terrain, rivers, roads, and vegetation, which serves as the background support for displaying fire assessment information. Next, based on the first mapping relationship, the first control tower point is marked with a specific icon at the corresponding location, using color, size, and flashing to display information related to the fire assessment results. For example, red indicates "fire" and light / dark colors indicate fire severity. Based on the second mapping relationship, multiple arbitrary control tower points and their assessment results are similarly visualized on the map, forming a comprehensive display layer. Furthermore, interactive features are added to the visual map: clicking an icon pops up a detailed information window, zooming in and out to view detailed areas using the mouse wheel, and dragging the map to view an overall overview. By constructing this visual graph, complex fire analysis data can be presented intuitively, making it easier for relevant personnel to quickly understand fire distribution, development trends, and tower monitoring status, providing efficient and accurate information for fire decision-making and improving the visual management level and decision-making efficiency of forest fire prevention and control.

[0046] In a possible implementation, the analysis and judgment visual map construction unit further includes: an initial forest fire analysis and judgment visual map construction subunit, and the initial forest fire analysis and judgment visual map construction subunit is used to construct an initial forest fire analysis and judgment visual map based on the first mapping relationship and the second mapping relationship. Specifically, the initial forest fire analysis and judgment visual map construction subunit constructs an initial forest fire analysis and judgment visual map based on the first mapping relationship and the second mapping relationship. First, the GIS data of the target forest (basic geographic information such as terrain, rivers, roads, and forest coverage) is obtained and imported into the visualization software platform to build a geographic space framework. Then, based on the first mapping relationship, the first tower point is presented on the map with a specific icon, and the corresponding first fire analysis and judgment result information is associated, and the relevant content is displayed with the help of color depth, icon size, flashing effect, etc. Then, according to the second mapping relationship, any tower point and its analysis and judgment results are marked in a similar manner, and the information layer is superimposed to construct a visual map. The visual graph integrates the correlation information between tower points and fire analysis results, providing a visual platform for blank point analysis and fire situation assessment, making it easier for managers to intuitively understand the situation, identify risk areas, assist in formulating prevention and control strategies, and improve the efficiency and accuracy of fire monitoring and response.

[0047] The blank point acquisition subunit is used to obtain the blank points in the initial forest fire assessment visual map and to form an interpolation reference set for the blank points. Specifically, the blank point acquisition subunit scans and analyzes the constructed initial forest fire assessment visual map, and accurately identifies the areas in the map that are not covered by the tower, i.e., the blank points, through image recognition and data analysis technology. Blank points lack direct fire monitoring data due to geographical restrictions, tower construction planning or other factors. For each blank point, the surrounding geographical environment information is collected, such as the relative position with the adjacent tower, terrain features (valleys, ridges, plains, etc.), vegetation type (coniferous forests, broad-leaved forests, bushes, etc.) and meteorological conditions (wind direction, wind speed, temperature, humidity, etc.), and blank points with similar characteristics are classified and grouped, and combined with the tower point information with monitoring data in the surrounding area, an interpolation reference set for each blank point is formed. The construction of the interpolation reference set aims to provide multi-source, relevant reference data for the estimation of subsequent blank point fire assessment results, make full use of known fire monitoring information in the surrounding area to infer the possibility of fire in the blank area, and improve the accuracy and reliability of fire situation judgment at the blank point by analyzing multiple factors, fill the blank areas of fire monitoring, make the fire monitoring of the entire forest more comprehensive and complete, reduce the risk of fire spread due to monitoring blind spots not being discovered in time, and enhance the comprehensiveness and effectiveness of forest fire prevention and control.

[0048] The first reference fire analysis result extraction subunit is used to extract the first reference fire analysis result of the first reference point in the interpolation reference set. Specifically, the first reference fire analysis result extraction subunit operates on the interpolation reference set of each blank point. The first reference point is selected from the interpolation reference set. The point is usually a tower point that is relatively close to the blank point in geographical space and whose fire monitoring data has high reliability and representativeness. By interacting with the database that stores the fire analysis results, the first reference fire analysis result of the first reference point is extracted. The result includes the fire judgment made by the point based on its monitoring data (such as fire, no fire) and related auxiliary information, such as the estimated probability of fire occurrence, fire development trend assessment, possible fire cause analysis (such as man-made fire source, natural fire source, etc.) and other detailed content. After extracting the information, it is used as the key basic data for estimating the subsequent fire assessment results of blank points. By analyzing the fire situation of the first reference point and combining its spatial relationship and environmental similarity with the blank point, the fire possibility of the blank point is inferred, providing a direct reference basis for the fire assessment of the blank point. In the absence of direct monitoring data, it is possible to use the effective information in the surrounding area to make reasonable speculations on the fire situation, improve the ability to grasp the entire forest fire situation, ensure the continuity and integrity of fire monitoring and prevention and control work, and reduce fire monitoring loopholes and misjudgment risks caused by missing data.

[0049] The first spatial distance acquisition subunit is used to obtain the first spatial distance between the first reference point and the blank point. Specifically, after determining the first reference point and the blank point, the first spatial distance acquisition subunit uses a geographic spatial analysis algorithm to calculate the first spatial distance between the two. This algorithm is based on the principles of geodesy and the geographic coordinate system, accurately measures the straight-line distance between the two points, and makes corrections considering the impact of terrain undulations on the actual distance. By obtaining high-precision geographic information data, including the terrain elevation model (DEM), the actual surface distance between the two points is calculated, rather than a simple plane straight-line distance, to ensure the accuracy and reliability of the distance calculation. The first spatial distance value is one of the important bases for the subsequent sorting and weight allocation of reference points, because the closer the spatial distance, the greater the impact of the fire analysis results of the reference point on the blank point, and the higher its relevance and reference value. Accurate calculation of spatial distance can reasonably reflect the closeness between different reference points and blank points, thereby providing precise spatial relationship data support for the construction of a scientific and reasonable blank point fire assessment model. When using the surrounding reference point information to infer the fire situation of the blank point, the influence of spatial factors can be considered more accurately, thereby improving the accuracy and credibility of the fire assessment results and providing strong technical support for the precise prevention and control of forest fires.

[0050] The reference descending list acquisition subunit is used to obtain a reference descending list of the first reference fire in descending order based on the first spatial distance. Specifically, the reference descending list acquisition subunit uses the first spatial distance as the key sorting indicator to perform a sorting operation on all reference points in the interpolation reference set. First, the spatial distance between each reference point and the blank point is obtained, and these reference points are arranged in order from smallest to largest distance. The sorted reference point list is reversed to obtain a descending list based on the first spatial distance, that is, a reference descending list. In this list, the reference point closest to the blank point is ranked first, and the reference point farthest away is ranked last. This sorting method allows the fire assessment results of reference points with closer distances and higher relevance to be given priority when determining the fire assessment results of the blank point in the subsequent determination, giving them greater weight and influence, while reference points with farther distances are relatively less important. By arranging in descending order based on spatial distance, a reasonable reference point weight distribution system was constructed, so that the fire assessment results of blank points can be more scientifically and accurately integrated with the information of multiple reference points in the surrounding area, and the spatial correlation can be fully utilized to improve the reliability of fire situation assessment, avoiding assessment errors caused by unreasonable weight distribution, providing an optimized data analysis framework for blank point fire assessment, and enhancing the ability to comprehensively monitor and accurately prevent and control forest fires.

[0051] The blank fire analysis result determination subunit is used to determine the blank fire analysis result of the blank point according to the reference fire analysis result of the predetermined ranking in the reference descending list, and the blank point and the blank fire analysis result form a third mapping relationship. Specifically, the blank fire analysis result determination subunit determines the blank fire determination result of the blank point based on the reference descending list. First, the reference points of the predetermined ranking are determined according to the preset rules. The predetermined ranking is usually determined based on actual experience and data analysis. Several reference points with high rankings are selected, such as the top 3 or top 5, because they are close to the blank points in space and have a higher correlation with the fire analysis results. The fire analysis results of the selected reference points are comprehensively analyzed and processed. The weighted average method can be used. The inverse of the spatial distance between the reference point and the blank point is used as the weight (the closer the distance, the greater the weight), and the fire probability values of the reference points are weighted and summed to obtain the estimated fire probability value of the blank point. For the judgment of whether the fire has occurred or not, the majority voting method can be used. That is, if the majority of the selected reference points are judged to have a fire, the blank point is preliminarily judged to have a fire, otherwise it is judged to have no fire. At the same time, combined with auxiliary information such as the fire development trend and fire cause of the reference point, the fire situation of the blank point is described and analyzed to form a complete blank fire assessment result. A third mapping relationship is established between the blank point and the blank fire assessment result and recorded in the database, so that the fire situation of the blank point can be reasonably inferred and evaluated, filling the gap in fire monitoring data, improving the comprehensive grasp of the fire situation of the entire target forest, improving the scientificity and accuracy of forest fire prevention and control work, and reducing fire risks and losses.

[0052] The target forest fire assessment visual map acquisition subunit is configured to render the third mapping relationship onto the initial forest fire assessment visual map, thereby obtaining the target forest fire assessment visual map. Specifically, the target forest fire assessment visual map acquisition subunit integrates the third mapping relationship into the initial forest fire assessment visual map and performs a rendering update. The visualization software platform first reads the layer and graphic element information of the initial visual map. Then, based on the third mapping relationship, the blank points are marked with specific icons (such as gray squares, with color and flashing effects dynamically adjusted based on the blank fire assessment results), and the corresponding detailed information is associated. Using graphics rendering technology, the blank point information is integrated with the original information to form a more complete and comprehensive target forest fire assessment visual map. This visual map can present the fire situation in the tower monitoring area and the blank points, using a reasonable method to display the fire risk situation. This provides relevant personnel with an intuitive and accurate display platform, helping them to fully understand the distribution and development trends of fires, more effectively formulate prevention and control strategies and allocate resources, improve the efficiency and effectiveness of forest fire prevention and control work, and ensure the safety and stability of forest resources and the ecological environment.

[0053] In one possible implementation, the first comprehensive support acquisition unit further includes a second support verification mechanism extraction subunit, which is configured to extract a second support verification mechanism from the predetermined support verification mechanism. Specifically, after receiving the predetermined support verification mechanism, the second support verification mechanism extraction subunit performs in-depth analysis and screening on it to extract the second support verification mechanism. This mechanism is typically a set of rules and processes based on time series analysis and historical data comparison, providing further verification support for the first fire assessment result from a historical perspective. It includes key elements such as specific time window settings, data screening conditions, and comparative analysis methods. For example, it specifies the time range for comparing historical data (such as the past year, a quarter, etc.), the criteria for selecting historical periods with similar environmental conditions for comparison, and the setting of comparison weights for different monitoring parameters (such as acoustics, optics, temperature, etc.). This provides precise direction and rule guidance for subsequent historical monitoring record acquisition and analysis, ensuring that the information extracted from the historical data is targeted and effective, avoiding the blind use of a large amount of irrelevant historical data, improving the efficiency and accuracy of the verification work, and laying the foundation for accurately evaluating the reliability of the first fire assessment result.

[0054] The historical monitoring record acquisition subunit is used to obtain the historical monitoring records of the first tower point according to the second support verification mechanism. Specifically, the historical monitoring record acquisition subunit interacts with the database storing historical monitoring data according to the time range, data screening conditions and other requirements set in the second support verification mechanism to obtain the historical monitoring records of the first tower point. The records cover the detailed data collected by the tower point through various sensors (acoustic, optical, temperature, etc.) during a specific time period in the past, including the sensor readings at each time point, the timestamp of data collection, the environmental conditions at that time (such as meteorological data such as temperature, humidity, wind speed, etc.), and any manual records (such as whether there are any abnormal observations, maintenance records, etc.). By screening and organizing data according to the requirements of the second support verification mechanism, it is ensured that the acquired historical monitoring records are comparable and relevant to the current fire assessment situation, providing a high-quality data source for subsequent year-on-year and month-on-month analysis, making it possible to mine useful information from historical data, discover potential fire occurrence patterns and trends, provide strong historical data support for judging the rationality of current fire assessment results, and enhance the reliability and stability of the fire monitoring and early warning system.

[0055] The first historical year-on-year slice extraction subunit is configured to perform year-on-year segmentation on the historical monitoring records to obtain a first segmentation result, and to extract a first historical year-on-year slice from the first segmentation result. Specifically, the first historical year-on-year slice extraction subunit performs year-on-year segmentation on the historical monitoring records to extract the first historical year-on-year slice. The year-on-year segmentation compares and analyzes the same time period, divides the historical monitoring records into multiple time periods based on the time range and granularity (e.g., quarterly) determined by the second support verification mechanism, and selects the historical data corresponding to the current fire assessment time period to obtain the first segmentation result. The monitoring parameter data related to the current fire assessment is then extracted from the result to form the first historical year-on-year slice. For example, if the current assessment is for a summer fire at the first tower point, the acoustic, optical, and temperature data for the point in the past several summers are extracted from the historical records to form a slice. Through year-on-year analysis, the changing trends and characteristics of historical monitoring data under similar conditions can be observed, providing a reference for judging the current fire possibility, helping to identify anomalies, improve the accuracy and scientific nature of fire assessment, promptly discover potential risks, and enhance the foresight and effectiveness of forest fire prevention and control.

[0056] The first historical year-on-year slice extraction subunit is configured to perform year-on-year segmentation on the historical monitoring record to obtain a second segmentation result, and extract the first historical year-on-year slice from the second segmentation result. Specifically, the first historical month-on-month slice extraction subunit uses the month-on-month segmentation method to process historical monitoring records to obtain the first historical month-on-month slice. The month-on-month segmentation is a method of comparing data from adjacent time periods to observe short-term change trends. The historical monitoring records are divided in sequence according to the month-on-month time step specified by the second support verification mechanism (such as one week, one month, etc.), and the data combination of multiple adjacent time periods is obtained to form a second segmentation result. Then, the data of the previous time period closest to the current fire assessment time is selected from it, and the monitoring parameter data related to the fire assessment is extracted to form the first historical month-on-month slice. For example, when the fire situation in a certain week in July is currently being assessed, the historical monitoring data of the last week of June is extracted as a slice. Through month-on-month analysis, the recent changes in historical data can be understood, especially whether there are any abnormal fluctuations in the monitoring data in the short term before the fire occurs, such as a sudden increase in temperature, an increase in abnormal acoustic signals, the appearance of optical smoke characteristics, etc., thereby providing timely and sensitive short-term data support for judging the possibility of the current fire, helping to capture early signs of fire, improve the timeliness and accuracy of fire warnings, and gain precious time and opportunities for early prevention and control of forest fires.

[0057] The first historical year-on-year fire label extraction subunit is used to extract the first historical year-on-year fire label in the first historical year-on-year slice and the first historical year-on-year fire label in the first historical year-on-year slice in sequence. Specifically, after obtaining the first historical year-on-year slice and the first historical year-on-year slice, the first historical year-on-year fire label extraction subunit deeply analyzes the data therein to extract the first historical year-on-year fire label and the first historical year-on-year fire label. The labels are marked according to whether the fire occurred and the severity of the fire in the historical monitoring data. For the first historical year-on-year slice, by querying historical fire record archives, fire fighting reports, manual observation records and other materials, it is determined whether there is a fire in the area where the first tower point is located in the same period of history. If it occurs, the corresponding labels such as small fire, medium fire, large fire, etc. are assigned as the first historical year-on-year fire label according to the scale, duration, and scope of influence of the fire; for the first historical year-on-year slice, a similar method is used to extract the fire occurrence and label from the historical data of the adjacent time period as the first historical year-on-year fire label. These fire labels provide key qualitative information for subsequent support calculations, can evaluate the rationality of current assessment results based on the actual historical fire situation, judge current fire risks by comparing historical regular characteristics, improve the accuracy and reliability of assessments, and provide a scientific and objective basis for forest fire prevention and control decisions.

[0058] The second support acquisition subunit is used to obtain the second support based on the first historical year-on-year fire label and the first historical month-on-month fire label. Specifically, the second support acquisition subunit calculates the second support based on the first historical year-on-year fire label and the first historical month-on-month fire label, constructs a support evaluation model, and comprehensively considers multiple factors to quantify its value. When the historical fire label is consistent with the current first fire assessment result, such as when fires occurred in the same historical period and adjacent time periods and the current assessment result is also "there is a fire", points are added to the basic score according to pre-set rules, and the amount of added points is dynamically adjusted according to factors such as the severity of the historical fire, the frequency of occurrence, and the similarity with the current environment; conversely, if no fire occurred in history and the current assessment is "there is a fire", it will be handled according to the difference deduction rules, and the deduction will also refer to the above factors. The second support value is calculated through a dynamic scoring mechanism based on historical tags. This value intuitively reflects the degree of support for the current analysis results from historical fire conditions. As an important quantitative basis for evaluating reliability, it verifies and improves the current analysis results from a historical perspective, reduces the risk of misjudgment, improves the accuracy and scientific nature of forest fire monitoring and early warning, and ensures the safety and stability of forest resources and the ecological environment.

[0059] The first comprehensive support acquisition subunit is used to replace the first support with the average of the second support and the first support as the first comprehensive support. Specifically, the first comprehensive support acquisition subunit uses the average of the second support and the first support as the new first comprehensive support. First, the first support based on the fire assessment results of the surrounding tower points and the second support just calculated based on the historical monitoring data are obtained, and then the arithmetic mean of the two is calculated, that is, (first support + second support) / 2, and this new value is used as the first comprehensive support. This is to integrate the support of the current surrounding environment information and historical experience data on the first fire assessment results, so that the first comprehensive support can more comprehensively and objectively reflect its reliability and credibility, avoid the limitations of single-factor judgment, and make full use of multi-source and multi-angle information to provide more scientific and accurate quantitative indicators for fire decision-making. Decision makers can use this value to more accurately judge the credibility of the first fire assessment results and decide whether to take further action.

[0060] The fire warning module 60 is used to activate the warning signal to issue a fire warning to the first tower point of the target forest when the first comprehensive support reaches the predetermined support threshold. Specifically, the fire warning module 60 monitors the first comprehensive support, and the predetermined support threshold is a key value preset based on historical data, fire characteristics and risk assessment model. After obtaining the new first comprehensive support, the module uses a precise algorithm to compare it with the threshold. If the threshold is reached or exceeded, it means that the multi-party data shows that the possibility of fire at the first tower point is high. The module selects an adaptive warning signal from the library (based on regional characteristics and risk levels, such as using high-loudness and other multi-channel signals in densely populated areas and long-distance persistent signals in remote areas) and sends it through connected communication and alarm equipment, accompanied by information such as the tower location, the quantitative value of the fire possibility and the fire forecast, which helps personnel and departments make quick decisions, reduce losses, ensure forest safety, and improve the timeliness, effectiveness, reliability and adaptability of prevention and control.

[0061] In the above, refer to Figure 1 The forest fire real-time monitoring and early warning device based on the combined sound, light and heat sensing according to the embodiment of the present invention is described in detail. Figure 2 A method for real-time forest fire monitoring and early warning using combined acoustic, optical, and thermal sensing according to an embodiment of the present invention is described.

[0062] Real-time forest fire monitoring and early warning methods based on combined acoustic, optical and thermal sensing, such as Figure 2 As shown, the method includes:

[0063] The target real-time status information of the target forest is obtained through dynamic monitoring by an acoustic, optical and thermal combined sensing device, wherein the acoustic, optical and thermal combined sensing device is mounted on a distributed tower of the target forest; the first status information corresponding to the first tower point in the target real-time status information is extracted, and the first tower point refers to any one of the distributed towers; the first status information is used as input data of a fire analysis device, and output data is obtained through the fire analysis device, wherein the output data includes a first fire analysis result of the first tower point; when the first fire analysis result meets a predetermined verification constraint, a predetermined support verification mechanism is read; a first comprehensive support degree of the first fire analysis result is obtained according to the predetermined support verification mechanism; when the first comprehensive support degree reaches a predetermined support degree threshold, an early warning signal is activated to issue a fire early warning to the first tower point of the target forest.

[0064] In a possible implementation, the real-time monitoring and early warning method for forest fires using combined acoustic, optical and thermal perception also includes: obtaining first sound information of the first tower point through the acoustic sensor; performing image recognition analysis on the first infrared image of the first tower point through the optical sensor to obtain first optical information; obtaining first temperature information of the first tower point through the temperature sensor; performing variation weighted processing on the first sound information, the first optical information and the first temperature information in turn to obtain a first sound coefficient, a first optical coefficient and a first temperature coefficient, respectively; the first sound coefficient, the first optical coefficient and the first temperature coefficient constitute the first state information.

[0065] In a possible implementation, the real-time monitoring and early warning method for forest fires using combined acoustic, optical and thermal perception also includes: obtaining a first infrared pixel in the first infrared image through the optical sensor; analyzing the first infrared pixel corresponding to the first RGB to obtain a first light color parameter; analyzing the first infrared pixel corresponding to the first HSB to obtain a first light intensity parameter; the first light color parameter and the first light intensity parameter constitute the first optical information.

[0066] In a possible implementation, the real-time monitoring and early warning method for forest fires using combined acoustic, optical and thermal perception also includes: obtaining a forest fire log, the forest fire log including a first record and a second record; forming a first data group based on the first historical status information and the first historical fire label in the first record; forming a second data group based on the second historical status information and the second historical no-fire label in the second record; and performing supervised learning on the first data group and the second data group after sample enhancement processing based on the support vector machine principle to obtain the fire analysis device.

[0067] In a possible implementation, the real-time monitoring and early warning method for forest fires using combined acoustic, optical and thermal perception also includes: extracting a first support verification mechanism from the predetermined support verification mechanism; determining a first neighborhood of the first tower point based on the first support verification mechanism, the first neighborhood including multiple tower points; randomly extracting any tower point from the multiple tower points, the arbitrary tower point corresponding to arbitrary status information; obtaining an arbitrary fire analysis result by analyzing the arbitrary status information through the fire analysis device; obtaining a first support degree of the first fire analysis result based on the arbitrary fire analysis result; and using the first support degree as the first comprehensive support degree.

[0068] In a possible implementation, the real-time monitoring and early warning method for forest fires using combined acoustic, optical and thermal perception also includes: forming a first mapping relationship between the first tower point and the first fire assessment result; forming a second mapping relationship between the arbitrary tower point and the arbitrary fire assessment result; and constructing a target forest fire assessment visual graph of the target forest based on the first mapping relationship and the second mapping relationship.

[0069] In a possible implementation, the real-time monitoring and early warning method for forest fires using combined acoustic, optical and thermal sensing also includes: constructing an initial forest fire assessment visual map based on the first mapping relationship and the second mapping relationship; obtaining blank points in the initial forest fire assessment visual map, and forming an interpolation reference set of the blank points; extracting a first reference fire assessment result of the first reference point in the interpolation reference set; obtaining a first spatial distance between the first reference point and the blank point; arranging in descending order based on the first spatial distance to obtain a reference descending list of the first reference fire; determining the blank fire assessment result of the blank point based on a predetermined ranking of the reference fire assessment results in the reference descending list, and forming a third mapping relationship between the blank point and the blank fire assessment result; rendering the third mapping relationship to the initial forest fire assessment visual map to obtain the target forest fire assessment visual map.

[0070] In a possible implementation, the real-time monitoring and early warning method for forest fires based on combined sound, light and heat perception also includes: extracting a second support verification mechanism from the predetermined support verification mechanism; obtaining historical monitoring records of the first tower point according to the second support verification mechanism; performing year-on-year segmentation on the historical monitoring records to obtain a first segmentation result, and extracting a first historical year-on-year slice from the first segmentation result; performing month-on-month segmentation on the historical monitoring records to obtain a second segmentation result, and extracting a first historical month-on-month slice from the second segmentation result; extracting in sequence the first historical year-on-year fire label from the first historical year-on-year slice and the first historical month-on-month fire label from the first historical month-on-month slice; obtaining a second support degree based on the first historical year-on-year fire label and the first historical month-on-month fire label; replacing the first support degree with the average of the second support degree and the first support degree as the first comprehensive support degree.

[0071] The real-time monitoring and early warning device for forest fires with combined sound, light and heat perception provided in an embodiment of the present invention can execute the real-time monitoring and early warning method for forest fires with combined sound, light and heat perception provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0072] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0073] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing, characterized by: include: a target real-time status information monitoring module, the target real-time status information monitoring module being used to dynamically monitor and obtain target real-time status information of a target forest through an acoustic, optical, and thermal combined sensing device, wherein the acoustic, optical, and thermal combined sensing device is mounted on a distributed tower of the target forest; A first state information extraction module, the first state information extraction module is used to extract first state information corresponding to a first tower point in the target real-time state information, where the first tower point refers to any one of the distributed towers; an output data acquisition module, the output data acquisition module being configured to use the first state information as input data of a fire analysis device and obtain output data through the fire analysis device, wherein the output data includes a first fire analysis result at the first tower point; a predetermined support verification mechanism reading module, configured to read a predetermined support verification mechanism when the first fire analysis result meets a predetermined verification constraint; a first comprehensive support degree acquisition module, configured to obtain a first comprehensive support degree of the first fire assessment result according to the predetermined support verification mechanism; a fire warning module, configured to activate a warning signal to issue a fire warning to the first tower point in the target forest when the first comprehensive support reaches a predetermined support threshold; The first comprehensive support acquisition module includes: a first supported verification mechanism extraction unit, configured to extract a first supported verification mechanism from the predetermined supported verification mechanisms; a first neighborhood determining unit, configured to determine a first neighborhood of the first tower point according to the first supported verification mechanism, wherein the first neighborhood includes a plurality of tower points; An arbitrary tower point extraction unit, the arbitrary tower point extraction unit is used to randomly extract an arbitrary tower point from the multiple tower points, the arbitrary tower point corresponding to arbitrary status information; An arbitrary fire assessment result analysis unit, configured to analyze the arbitrary state information through the fire assessment device to obtain an arbitrary fire assessment result; a first support degree obtaining unit, configured to obtain a first support degree of the first fire assessment result according to the arbitrary fire assessment result; A first comprehensive support acquisition unit is configured to use the first support as the first comprehensive support.

2. The real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing according to claim 1 is characterized in that: The acoustic, optical and thermal joint sensing device includes an acoustic sensor, an optical sensor and a temperature sensor, and the first state information extraction module includes: a first sound information monitoring unit, configured to obtain first sound information of the first tower point by monitoring the acoustic sensor; an image recognition and analysis unit, configured to perform image recognition and analysis on the first infrared image of the first tower point through the optical sensor to obtain first optical information; a first temperature information acquiring unit, configured to obtain first temperature information of the first tower point through monitoring by the temperature sensor; a variation weighted processing unit, configured to sequentially perform variation weighted processing on the first sound information, the first optical information, and the first temperature information to obtain a first sound coefficient, a first optical coefficient, and a first temperature coefficient, respectively; The first state information composition unit is used for composing the first state information with the first sound coefficient, the first optical coefficient and the first temperature coefficient.

3. The real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing according to claim 2 is characterized in that: The image recognition and analysis unit includes: a first infrared pixel acquisition subunit, configured to acquire a first infrared pixel in the first infrared image through the optical sensor; a first light color parameter acquisition subunit, configured to analyze the first RGB corresponding to the first infrared pixel to obtain a first light color parameter; a first light intensity parameter acquisition subunit, configured to analyze the first infrared pixel corresponding to the first HSB to obtain a first light intensity parameter; The first optical information composition subunit is used for composing the first optical information with the first light color parameter and the first light intensity parameter.

4. The real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing according to claim 1 is characterized in that: The output data acquisition module includes: a forest fire log acquiring unit, the forest fire log acquiring unit being configured to acquire a forest fire log, the forest fire log comprising a first record and a second record; a first data group forming unit, configured to form a first data group according to the first historical state information and the first historical fire tag in the first record; a second data group forming unit, the second data group forming unit being configured to form a second data group according to the second historical state information and the second historical no-fire tag in the second record; A fire analysis device acquisition unit is used to perform supervised learning on the first data group and the second data group after sample enhancement processing based on the support vector machine principle to obtain the fire analysis device.

5. The real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing according to claim 1 is characterized in that: The first comprehensive support acquisition unit further includes: a first mapping relationship forming subunit, configured to form a first mapping relationship between the first control tower point and the first fire analysis result; a second mapping relationship forming subunit, the second mapping relationship forming subunit being used to form a second mapping relationship between the arbitrary tower point and the arbitrary fire analysis result; The analysis and judgment visual graph construction subunit is used to construct a target forest fire analysis and judgment visual graph of the target forest according to the first mapping relationship and the second mapping relationship.

6. The real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing according to claim 5 is characterized in that: The visual graph construction subunit includes: an initial forest fire assessment visual graph construction subunit, the initial forest fire assessment visual graph construction subunit being configured to construct an initial forest fire assessment visual graph according to the first mapping relationship and the second mapping relationship; A blank point acquisition subunit, the blank point acquisition subunit is used to acquire blank points in the initial forest fire analysis visual map and to form an interpolation reference set of the blank points; a first reference fire assessment result extraction subunit, the first reference fire assessment result extraction subunit being configured to extract a first reference fire assessment result at a first reference point in the interpolation reference set; a first spatial distance acquisition subunit, configured to acquire a first spatial distance between the first reference point and the blank point; a reference descending list obtaining subunit, the reference descending list obtaining subunit being configured to obtain a reference descending list of the first reference fires by arranging the first spatial distance in descending order; a blank fire analysis result determination subunit, the blank fire analysis result determination subunit being configured to determine a blank fire analysis result for the blank point according to the reference fire analysis results with a predetermined ranking in the reference descending list, and forming a third mapping relationship between the blank point and the blank fire analysis result; The target forest fire assessment visual graph acquisition subunit is configured to render the third mapping relationship to the initial forest fire assessment visual graph to obtain the target forest fire assessment visual graph.

7. The real-time forest fire monitoring and early warning device with combined acoustic, optical and thermal sensing according to claim 1 is characterized in that: The first comprehensive support acquisition unit further includes: a second supported verification mechanism extraction subunit, the second supported verification mechanism extraction subunit being configured to extract a second supported verification mechanism from the predetermined supported verification mechanisms; a historical monitoring record acquisition subunit, configured to acquire historical monitoring records of the first tower point according to the second supported verification mechanism; a first historical year-on-year slice extraction subunit, configured to perform year-on-year segmentation on the historical monitoring records to obtain a first segmentation result, and extract a first historical year-on-year slice from the first segmentation result; a first historical year-on-year slice extraction subunit, configured to perform year-on-year segmentation on the historical monitoring record to obtain a second segmentation result, and extract the first historical year-on-year slice from the second segmentation result; A first historical year-on-year fire label extraction subunit, the first historical year-on-year fire label extraction subunit is used to sequentially extract the first historical year-on-year fire label in the first historical year-on-year slice and the first historical year-on-year fire label in the first historical year-on-year slice; a second support obtaining subunit, configured to obtain a second support according to the first historical year-on-year fire label and the first historical month-on-month fire label; The first comprehensive support acquisition subunit is configured to replace the first support with an average of the second support and the first support as the first comprehensive support.

8. A real-time forest fire monitoring and early warning method based on combined acoustic, optical, and thermal sensing, characterized in that: The method is applied to the forest fire real-time monitoring and early warning device with combined acoustic, optical and thermal sensing according to any one of claims 1 to 7, and the method comprises: Dynamically monitoring the target forest through a combined acoustic, optical, and thermal sensing device to obtain real-time status information of the target forest, wherein the combined acoustic, optical, and thermal sensing device is mounted on a distributed tower of the target forest; Extracting first status information corresponding to a first tower point in the target real-time status information, where the first tower point refers to any one of the distributed towers; Using the first state information as input data of a fire analysis device, and obtaining output data through the fire analysis device, wherein the output data includes a first fire analysis result at the first tower point; When the first fire analysis result meets the predetermined verification constraint, reading the predetermined support verification mechanism; Obtaining a first comprehensive support degree of the first fire analysis result according to the predetermined support verification mechanism; When the first comprehensive support reaches a predetermined support threshold, an early warning signal is activated to issue a fire warning to the first tower point in the target forest.

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