Intelligent data processing method for forestry ecological monitoring

Abstract

The invention discloses an intelligent data processing method for forestry ecological monitoring, and relates to the technical field of forestry ecological monitoring, and the method comprises the steps: S1, obtaining multi-source image data of a forest region, S2, carrying out the preprocessing of the image data based on an environment perception model, correcting the image overexposure or underexposure caused by smog and flame, and carrying out the processing of the image overexposure or underexposure. The method comprises the steps of S1, acquiring image data, S3, extracting features in the image data, S4, analyzing the features by using a deep learning model and identifying potential fire risks, S5, generating corresponding early warning information and sending the information to a monitoring center in real time, and storing a processing result and the early warning information. According to the intelligent data processing method for forestry ecological monitoring, the problem of image overexposure or underexposure caused by the high dynamic range of smoke and fire light in forest fire monitoring in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry ecological monitoring, and in particular to an intelligent data processing method for forestry ecological monitoring. Background Art

[0002] In modern forestry ecological monitoring, forest fire monitoring is an important part of ecological protection. Existing monitoring methods usually rely on image sensors and related algorithms to achieve early detection and dynamic monitoring of fire occurrence. However, due to the complexity of the monitoring environment and the characteristics of fire, existing technologies have significant deficiencies in processing high dynamic range images.

[0003] Specifically, the presence of smoke and fire in forest fire monitoring will cause the image sensor to be unable to correctly balance the light intensity, resulting in the problem of overexposure or underexposure of the image. This phenomenon makes it difficult for the monitoring system to accurately extract key fire features, which in turn affects the fire recognition accuracy and monitoring efficiency. At the same time, traditional image processing algorithms are usually less adaptable to dynamic lighting changes, and have limited processing capabilities in complex lighting environments, making it difficult to meet the needs of real-time monitoring. In addition, existing solutions to the problem of overexposure or underexposure of images mostly rely on hardware upgrades, which greatly increases the cost of the monitoring system and limits its promotion and application in areas with limited resources. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent data processing method for forestry ecological monitoring to solve the problem of overexposure or underexposure of images caused by the high dynamic range of smoke and fire light in forest fire monitoring in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent data processing method for forestry ecological monitoring, the method comprising:

[0006] S1. Obtain multi-source image data of forest areas;

[0007] S2. Preprocess the image data based on the environmental perception model to correct the overexposure or underexposure of the image caused by smoke and fire, including analyzing the brightness distribution of the smoke and fire areas in the image, determining the initial brightness value, contrast enhancing the brightness, and compressing the brightness of the overexposed area. The specific formula for contrast enhancement is: C = k[A] n ;

[0008] Among them, C represents the enhanced contrast, k represents the contrast adjustment ratio parameter, A represents the initial brightness value of the image, and n represents the exponential parameter of the adjustment intensity;

[0009] S3, extracting features from image data;

[0010] S4. Use the deep learning model to analyze the features and identify potential fire risks, including simulating the error dynamics of the deep learning model and optimizing the analysis process. The specific formula is:

[0011] dN / dt=rN-aNP, dP / dt=bNP-mP;

[0012] Wherein, N represents the number of fire risk characteristics, P represents the number of misclassifications, r represents the growth rate of fire risk characteristics, a represents the suppression rate of misclassification on fire characteristics, b represents the rate at which fire characteristics promote misclassification, m represents the natural reduction rate of misclassification, and t represents time;

[0013] Output fire risk level and risk area location;

[0014] S5. Generate corresponding warning information and send it to the monitoring center in real time, and store the processing results and warning information.

[0015] Preferably, the S1 comprises:

[0016] Collect multi-source image data, including drone images, satellite remote sensing images, and ground monitoring data, calculate the value index of each image source based on its resolution and timeliness, use the resource allocation model to determine the weight of each data source, and generate a multi-source comprehensive image. The specific formula is:

[0017] Among them, R i In the fusion of multi-source image data, the weight of the i-th image source, V i represents the value index of the i-th data source, g represents the number of image data sources used in forest monitoring, V j It represents the value index of the j-th data source, i represents the number of the current image data source, and j represents a number among all image data sources.

[0018] Preferably, S3 includes:

[0019] The gradient value of the image is input into the fitness function to generate the characteristic fitness distribution. The flame edge and smoke contour area are located according to the output of the fitness function, and the shape, size and concentration characteristics of the area are extracted. The specific formula of the fitness function is:

[0020]

[0021] Among them, F(x) represents feature fitness, x represents the image gradient value, and e represents a natural constant.

[0022] Preferably, S5 includes:

[0023] Generate early warning information based on the fire analysis results, encrypt the early warning information, send the encrypted information to the monitoring center through the wireless communication network, and store it in the cloud for subsequent review and analysis. The specific formula for encrypting the early warning information is: D = M ⊕ K;

[0024] Among them, D represents the ciphertext information generated after the encryption operation, M represents the unencrypted warning information, K represents the key used in the encryption and decryption process, and ⊕ represents the bitwise XOR operation.

[0025] Preferably, S2 also includes performing image data correction based on a local histogram, applying a grayscale stretching algorithm to smoke and fire areas, optimizing image quality using an adaptive contrast enhancement method, and removing image noise through a noise filter.

[0026] Preferably, the image data correction based on the local histogram includes dividing the image data into a plurality of non-overlapping sub-regions, calculating a histogram for each sub-region, and applying a histogram equalization formula H(i)=G×(n1+n2+...+n i ) / E, where H(i) represents the cumulative histogram, G is a constant, and n1 to n i Represents the number of pixels in the histogram, E is the total number of pixels, and the sub-regions after histogram equalization are merged into complete image data.

[0027] Preferably, calculating the histogram for each sub-region includes extracting the pixel value range [a, b] of the sub-region, calculating the frequency of each pixel value, and mapping the pixel value range [a, b] to [0, 255] using linear interpolation. The specific formula is: f(x) = 255×(y-a) / (b-a), where f(x) is the mapped pixel value, y is the original pixel value, and a and b are the minimum and maximum pixel values, respectively.

[0028] Preferably, the image data in S1 includes multispectral images, thermal infrared images and visible light images.

[0029] Preferably, the features in the image data in S3 include texture features, shape features and spectral features.

[0030] Preferably, the warning information generated in S5 includes the fire level and the location of the risk area.

[0031] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0032] The intelligent data processing method for forestry ecological monitoring obtains multi-source image data of the forest area, pre-processes the image data based on the environmental perception model, corrects the overexposure or underexposure of the image caused by smoke and fire, extracts features in the image data, analyzes the features using a deep learning model, identifies potential fire risks, generates corresponding warning information and sends it to a monitoring center in real time, stores the processing results and warning information, and can achieve more accurate fire feature extraction in a complex lighting environment, improve the recognition accuracy of the fire monitoring system, and the improved image processing technology can quickly respond to dynamic lighting changes, adapt to the changeable monitoring environment of forest fires, meet real-time monitoring needs, reduce the hardware requirements and operating costs of the system, make it easier to promote and apply in areas with limited resources, and can output optimized monitoring images in real time, effectively reduce data processing delays, and improve the overall efficiency of fire monitoring. The problem of image overexposure or underexposure caused by the high dynamic range of smoke and fire in forest fire monitoring in the prior art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] like Figure 1 As shown, the present invention provides a technical solution: an intelligent data processing method for forestry ecological monitoring, the method comprising:

[0036] S1. Obtain multi-source image data of forest areas;

[0037] S2. Preprocess the image data based on the environmental perception model to correct the overexposure or underexposure of the image caused by smoke and fire, including analyzing the brightness distribution of the smoke and fire areas in the image, determining the initial brightness value, contrast enhancing the brightness, and compressing the brightness of the overexposed area. The specific formula for contrast enhancement is: C = k[A] n ;

[0038] Among them, C represents the enhanced contrast, k represents the contrast adjustment ratio parameter, A represents the initial brightness value of the image, and n represents the exponential parameter of the adjustment intensity;

[0039] S3, extracting features from image data;

[0040] S4. Use the deep learning model to analyze the features and identify potential fire risks, including simulating the error dynamics of the deep learning model and optimizing the analysis process. The specific formula is:

[0041] dN / dt=rN-aNP, dP / dt=bNP-mP;

[0042] Wherein, N represents the number of fire risk characteristics, P represents the number of misclassifications, r represents the growth rate of fire risk characteristics, a represents the suppression rate of misclassification on fire characteristics, b represents the rate at which fire characteristics promote misclassification, m represents the natural reduction rate of misclassification, and t represents time;

[0043] Output fire risk level and risk area location;

[0044] S5. Generate corresponding warning information and send it to the monitoring center in real time, and store the processing results and warning information.

[0045] The core of this implementation method is to achieve intelligent monitoring of forest fire risks through the acquisition, preprocessing, feature extraction and deep learning analysis of multi-source image data. Through the image correction technology based on the environmental perception model, the image distortion problem caused by smoke and fire is solved, ensuring the accuracy of subsequent feature extraction. Further, through the error dynamic suppression mechanism of the deep learning model, combined with the mathematical formula to optimize the analysis process, the fire risk characteristics can be accurately identified, and the risk level and risk area location can be predicted. Finally, combined with the generated warning information, real-time feedback is given to the monitoring center to achieve risk monitoring and decision support. Through contrast enhancement and deep learning analysis, the recognition accuracy and sensitivity of fire risk characteristics are improved, ensuring the reliability of the prediction results. The optimized dynamic suppression mechanism shortens the time of risk feature analysis and warning generation, improves data processing efficiency, generates and transmits warning information in real time, effectively reduces forest fire risks, enhances the protection capability of the ecological monitoring system, reduces the ecological damage caused by fire, and provides technical support for the sustainable management of forest resources.

[0046] S1 includes collecting multi-source image data, including drone images, satellite remote sensing images and ground monitoring data, calculating the value index of each image source according to its resolution and timeliness, using the resource allocation model to determine the weight of each data source, and generating a multi-source comprehensive image. The specific formula is:

[0047] Among them, R i In the fusion of multi-source image data, the weight of the i-th image source, V i represents the value index of the i-th data source, g represents the number of image data sources used in forest monitoring, V jIt represents the value index of the j-th data source, i represents the number of the current image data source, and j represents a number among all image data sources.

[0048] In this implementation, the fusion and optimization of multi-source images are achieved through the comprehensive utilization of drone images, satellite remote sensing images and ground monitoring data. Each image data source has different resolutions and timeliness, so the value index V is introduced for quantitative evaluation. According to the resource allocation model, the weight formula R i Weighted calculations are performed on each image source to generate a comprehensive image. This method effectively integrates the information advantages of multiple image data sources and provides more accurate and real-time forestry ecological monitoring data. Through weight allocation based on value indicators, the advantages of different image sources are maximized, and the comprehensive quality and accuracy of image data are significantly improved. Combined with drone images, satellite remote sensing images and ground monitoring data, multi-dimensional monitoring capabilities of large-scale forest areas are achieved. Timeliness indicators are introduced to ensure the priority of key data sources, optimize real-time requirements, and quickly respond to changes in forest ecological conditions. The resource allocation model is suitable for different forest monitoring scenarios, and the allocation strategy can be flexibly adjusted according to specific needs.

[0049] S3 includes inputting the gradient value of the image into the fitness function, generating a characteristic fitness distribution, locating the flame edge and smoke contour area according to the output of the fitness function, and extracting the shape, size and concentration characteristics of the area. The specific formula of the fitness function is:

[0050]

[0051] Among them, F(x) represents feature fitness, x represents the image gradient value, and e represents a natural constant.

[0052] This embodiment realizes the calculation of feature fitness by extracting the image gradient value and inputting the fitness function F(x). The fitness function can effectively distinguish the flame edge and the smoke contour area. Specifically, the larger the gradient value x, the closer F(x) is to 1, indicating that the regional features are more significant; the smaller the gradient value, the closer F(x) is to 0, indicating that the regional features are not significant. Using this fitness function, the shape, size and waviness characteristics of the flame and smoke areas can be accurately extracted, thereby providing reliable data support for subsequent fire risk analysis. Through the fitness function based on the gradient value, the positioning accuracy of the flame edge and smoke contour is improved, and the influence of noise interference on feature extraction is avoided. The fitness function is simple in form and low in calculation cost. It is suitable for real-time processing of large-scale forest monitoring data. Through the extraction of shape, size and waviness characteristics, it provides multi-dimensional reliable data support for fire risk assessment. The fitness function can adjust the input parameters according to different application scenarios to ensure that the method has wide adaptability.

[0053] S5 includes generating early warning information according to the fire analysis results, encrypting the early warning information, sending the encrypted information to the monitoring center through the wireless communication network, and storing it in the cloud for subsequent review and analysis. The specific formula for encrypting the early warning information is: D = M ⊕ K;

[0054] Among them, D represents the ciphertext information generated after the encryption operation, M represents the unencrypted warning information, K represents the key used in the encryption and decryption process, and ⊕ represents the bitwise XOR operation.

[0055] In this embodiment, the warning information generated by the fire analysis result is processed as content to be sent. In order to ensure the security and privacy of the warning information, the bitwise XOR operation is used for encryption. The unencrypted warning information M is subjected to a bitwise XOR operation with the key K to generate a ciphertext information D. This ciphertext information is sent to the monitoring center via a wireless communication network and stored in the cloud at the same time. The encryption method is based on a simple and efficient XOR operation, which ensures that the original information can be restored only when the key is known, thereby improving the security of data transmission and storage. The confidentiality of the warning information is protected by an encryption mechanism to avoid potential risks caused by information leakage during wireless network transmission. The encrypted information is sent to the monitoring center in real time and stored in the cloud to ensure that the warning information can respond immediately and provide reliable data support for subsequent analysis. The bitwise XOR operation encryption is used, which has high computational efficiency and is suitable for the encryption requirements of large-scale warning information. The key K can be dynamically updated to further improve the system's anti-attack capability and adapt to different communication and storage security requirements.

[0056] S2 also includes image data correction based on local histogram, grayscale stretching algorithm for smoke and fire areas, adaptive contrast enhancement method to optimize image quality, and noise filter to remove image noise.

[0057] When correcting the image data, this embodiment first analyzes the grayscale distribution characteristics of the image based on the local histogram to correct the image quality degradation caused by local overexposure or underexposure. For smoke and fire areas, the grayscale stretching algorithm is applied to expand the grayscale range and enhance the detail expression of the image. Subsequently, the adaptive contrast enhancement method is used to improve the clarity and layering of the image by adjusting the contrast differences in different areas. Finally, the high-frequency noise and artifacts in the image are removed by the noise filter to ensure that the image data quality is suitable for subsequent feature extraction and risk analysis processing. The brightness uniformity and detail expression of the image are improved, and the visualization effect of the smoke and fire areas is optimized. The application of the adaptive contrast enhancement method improves the layering of the image and provides a clearer data basis for the subsequent extraction of fire features. The use of the noise filter effectively removes random noise and artifacts in the image, ensuring the stability and reliability of the image data. This method can flexibly respond to image data under different lighting conditions and imaging equipment, and is suitable for a variety of monitoring environments.

[0058] Image data correction based on local histogram includes dividing the image data into multiple non-overlapping sub-regions, calculating the histogram for each sub-region, and applying the histogram equalization formula H(i)=G×(n1+n2+...+n i ) / E, where H(i) represents the cumulative histogram, G is a constant, and n1 to n i Represents the number of pixels in the histogram, E is the total number of pixels, and the sub-regions after histogram equalization are merged into complete image data.

[0059] In this embodiment, when the image data is corrected based on the local histogram, the whole image is first divided into several non-overlapping sub-regions, and each sub-region is processed independently. By calculating the histogram of each sub-region and applying the histogram equalization formula, the grayscale distribution is adjusted to enhance the contrast of the sub-region. In the formula, the cumulative histogram H(i) is calculated according to the cumulative ratio of pixels, and the adjusted grayscale is mapped to the target range by the constant G, thereby improving the local image quality. After the processing is completed, all the equalized sub-regions are re-merged into complete image data to ensure global brightness and detail consistency. By processing the sub-regions of the image separately, the grayscale contrast of the local area is significantly enhanced, and the brightness unevenness problem existing in the image is adapted. The equalized image is fully optimized in terms of brightness distribution and detail expression, which lays a good foundation for subsequent feature extraction and analysis. The image division and processing have high flexibility, and the sub-region size can be adjusted according to the image resolution or content complexity to meet different correction requirements. This method can be widely used in image correction under complex lighting environments, such as forestry monitoring data processing with smoke and fire interference.

[0060] Calculating the histogram for each sub-region includes extracting the pixel value range [a, b] of the sub-region, calculating the frequency of each pixel value, and using linear interpolation to map the pixel value range [a, b] to [0, 255]. The specific formula is: f(x) = 255×(y-a) / (b-a), where f(x) is the mapped pixel value, y is the original pixel value, and a and b are the minimum and maximum pixel values, respectively.

[0061] In this embodiment, in order to calculate the histogram of the sub-region of the image data, the range [a, b] of the pixel value is first extracted from each sub-region, that is, the minimum value a and the maximum value b of the pixel value in the region are determined. Then, the histogram is generated by statistically analyzing the frequency distribution of each pixel value. In order to enhance the contrast of the image, the original pixel value y is mapped to the target range [0, 255] by linear interpolation. In the formula, the mapped pixel value f(x) is calculated by the relative position of the pixel value, thereby realizing the linear stretching of the grayscale. This method effectively balances the grayscale distribution of the sub-region and improves the contrast and visualization effect of the image. By mapping the pixel value of the sub-region to the full grayscale range [0, 255], the uniformity and contrast of the grayscale distribution are enhanced, making the details easier to identify. The linear interpolation method is simple to calculate and can quickly process large-scale image data to meet the needs of real-time monitoring. The grayscale mapping is dynamically adjusted according to the pixel value range of each sub-region to adapt to the image correction needs under different lighting conditions. The mapped grayscale value distribution is more suitable for subsequent feature extraction and analysis algorithms, thereby improving the accuracy and reliability of the overall monitoring system.

[0062] The image data in S1 includes multispectral images, thermal infrared images, and visible light images. In this embodiment, the acquisition of forest image data includes three different types of images: multispectral images, thermal infrared images, and visible light images. Multispectral images: By capturing information from multiple spectral bands, it can reflect the spectral characteristics of vegetation in the forest area, help identify different vegetation types, monitor health status, and detect abnormal changes in vegetation. Thermal infrared images: used to perceive surface temperature distribution, especially in fire monitoring, can directly capture information in high-temperature areas, and provide reliable data for locating fire hotspots. Visible light images: capture visual information of the forest area, provide clear and intuitive images, and provide background and reference for other image types. The combination of the three image data not only provides rich multi-dimensional information, but also enhances the accuracy of fire monitoring and ecological assessment through comparison and fusion.

[0063] The features in the image data in S3 include texture features, shape features and spectral features. In this embodiment, the feature extraction of image data includes three main features: texture features, shape features and spectral features. Texture features: By calculating the distribution and change law of pixel values ​​in the image, the roughness, directionality and repeatability information of the area are obtained. For example, the texture features are extracted using gray level co-occurrence matrix (GLCM) or wavelet transform to identify the fine structure of vegetation or smoke areas. Shape features: Analyze the geometric contours and spatial distribution information in the image, such as the shape of the flame edge or smoke contour, and identify the geometric characteristics of the target by calculating parameters such as the perimeter, area, aspect ratio, etc. Spectral features: Based on the reflectance data of different bands in multispectral or hyperspectral images, the spectral response information of the target is extracted for analyzing vegetation health, smoke density and flame characteristics. These three features provide multi-dimensional support for fire risk assessment and ecological status monitoring through joint analysis, which helps to improve the accuracy and stability of the monitoring system.

[0064] The warning information generated in S5 includes the fire level and the location of the risk area. This embodiment generates warning information by integrating the results of the fire risk analysis, which includes two key contents: fire level and risk area location: Fire level: Based on the features extracted during the fire analysis process (such as flame intensity, smoke density, regional temperature, etc.), combined with the model calculation results, the severity of the fire is evaluated. Fire levels are usually divided into different levels (such as level I, level II, and level III), and each level corresponds to different fire intensities and degrees of harm, providing a clear reference for the monitoring center. Risk area location: By analyzing the geographical distribution information of fire features in the image, the specific area where the fire occurred is located. Using a geographic coordinate system (such as GPS or georeferencing of remote sensing images), the boundaries of the risk area are marked to provide accurate location data for emergency response and resource deployment. The generated warning information is displayed in a digital form and can be transmitted to the monitoring center in real time through a wireless communication network, and stored in the cloud for subsequent analysis and scheduling decisions.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent data processing method for forestry ecological monitoring, characterized in that: The method comprises: S1. Obtain multi-source image data of forest areas; S2. Preprocess the image data based on the environmental perception model to correct the overexposure or underexposure of the image caused by smoke and fire, including analyzing the brightness distribution of the smoke and fire areas in the image, determining the initial brightness value, contrast enhancing the brightness, and compressing the brightness of the overexposed area. The specific formula for contrast enhancement is: C = k[A] n ; Among them, C represents the enhanced contrast, k represents the contrast adjustment ratio parameter, A represents the initial brightness value of the image, and n represents the exponential parameter of the adjustment intensity; S3, extracting features from image data; S4. Use the deep learning model to analyze the features and identify potential fire risks, including simulating the error dynamics of the deep learning model and optimizing the analysis process. The specific formula is: dN / dt=rN-aNP, dP / dt=bNP-mP; Wherein, N represents the number of fire risk characteristics, P represents the number of misclassifications, r represents the growth rate of fire risk characteristics, a represents the suppression rate of misclassification on fire characteristics, b represents the rate at which fire characteristics promote misclassification, m represents the natural reduction rate of misclassification, and t represents time; Output fire risk level and risk area location; S5. Generate corresponding warning information and send it to the monitoring center in real time, and store the processing results and warning information.

2. The intelligent data processing method for forestry ecological monitoring according to claim 1, characterized in that: The S1 includes: Collect multi-source image data, including drone images, satellite remote sensing images, and ground monitoring data, calculate the value index of each image source based on its resolution and timeliness, use the resource allocation model to determine the weight of each data source, and generate a multi-source comprehensive image. The specific formula is: Among them, R i In the fusion of multi-source image data, the weight of the i-th image source, V i represents the value index of the i-th data source, g represents the number of image data sources used in forest monitoring, V j It represents the value index of the j-th data source, i represents the number of the current image data source, and j represents a number among all image data sources.

3. The intelligent data processing method for forestry ecological monitoring according to claim 1 is characterized in that: The S3 includes: The gradient value of the image is input into the fitness function to generate the characteristic fitness distribution. The flame edge and smoke contour area are located according to the output of the fitness function, and the shape, size and concentration characteristics of the area are extracted. The specific formula of the fitness function is: Among them, F(x) represents feature fitness, x represents the image gradient value, and e represents a natural constant.

4. The intelligent data processing method for forestry ecological monitoring according to claim 1 is characterized in that: The S5 includes: Generate early warning information based on the fire analysis results, encrypt the early warning information, send the encrypted information to the monitoring center through the wireless communication network, and store it in the cloud for subsequent review and analysis. The specific formula for encrypting the early warning information is: D = M ⊕ K; Among them, D represents the ciphertext information generated after the encryption operation, M represents the unencrypted warning information, K represents the key used in the encryption and decryption process, and ⊕ represents the bitwise XOR operation.

5. The intelligent data processing method for forestry ecological monitoring according to claim 1 is characterized in that: The S2 also includes performing image data correction based on a local histogram, applying a grayscale stretching algorithm to smoke and fire areas, optimizing image quality using an adaptive contrast enhancement method, and removing image noise through a noise filter.

6. The intelligent data processing method for forestry ecological monitoring according to claim 5 is characterized in that: The image data correction based on the local histogram includes dividing the image data into a plurality of non-overlapping sub-regions, calculating a histogram for each sub-region, and applying a histogram equalization formula H(i)=G×(n1+n2+...+n i ) / E, where H(i) represents the cumulative histogram, G is a constant, and n1 to n i Represents the number of pixels in the histogram, E is the total number of pixels, and the sub-regions after histogram equalization are merged into complete image data.

7. The intelligent data processing method for forestry ecological monitoring according to claim 6 is characterized by: The calculation of the histogram for each sub-region includes extracting the pixel value range [a, b] of the sub-region, calculating the frequency of each pixel value, and mapping the pixel value range [a, b] to [0, 255] using linear interpolation. The specific formula is: f(x) = 255×(y-a) / (b-a), where f(x) is the mapped pixel value, y is the original pixel value, and a and b are the minimum and maximum pixel values, respectively.

8. The intelligent data processing method for forestry ecological monitoring according to claim 1 is characterized by: The image data in S1 includes multispectral images, thermal infrared images and visible light images.

9. The intelligent data processing method for forestry ecological monitoring according to claim 1 is characterized in that: The features in the image data in S3 include texture features, shape features and spectral features.

10. The intelligent data processing method for forestry ecological monitoring according to claim 1, characterized in that: The warning information generated in S5 includes the fire level and the location of the risk area.

Images