An ecological conservation forest purification efficiency automatic monitoring system based on image recognition

Abstract

This invention belongs to the field of ecological environment monitoring technology, specifically an automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition. It includes modules for multispectral image acquisition, atmospheric aerosol optical interference decoupling, purification kinetic semantic feature extraction, in-situ causal mapping calibration, and efficiency assessment feedback. By restoring true features through optical degradation compensation, using convolutional neural networks to identify microscopic anomalies on the leaf surface, and combining this with a sensor array to perform in-situ correction, it achieves accurate perception and dynamic calibration of forest purification efficiency, effectively ensuring high precision in the physical semantic support and quantitative assessment of evaluation results even under extreme environments.

Description

Technical Field

[0001] This invention belongs to the field of ecological environment monitoring technology, specifically an automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition. Background Technology

[0002] With the advancement of global ecological governance, the core functions of ecological conservation forests, such as air purification and carbon sequestration, have become the focus of research. Smart forestry and the Internet of Things for the environment are driving the transformation of monitoring towards visual perception and non-contact methods. Existing systems can reflect the growth status of trees and infer pollutant reduction capacity by extracting features such as canopy geometry, but they have deep technical defects. Existing monitoring logic suffers from the defect of disconnecting optical semantic features from physical and biochemical indicators. Traditional image recognition focuses on macroscopic structural parameters of the canopy, treating forests only as geometric entities. It has not established a coupling model between optical reflection and pollutant flux changes, ignoring the real-time activity changes of forest trees in purifying pollutants, and thus cannot provide insight into the true purification dynamics evolution within the forest.

[0003] Existing systems suffer from systemic bottlenecks due to environmental interference. Under extreme conditions such as heavy pollution, atmospheric aerosols cause optical degradation of images and loss of key semantic details, causing the model to fall into monitoring blind spots. Furthermore, there is a lack of in-situ real-time calibration mechanisms. Offline chemical sampling and online image recognition are separated by spatiotemporal barriers, making it impossible to achieve a leap from visual perception to functional quantification. The evaluation results have large errors. The core challenges are to solve the problems of the disconnect between optical and biochemical processes, decoupling from environmental interference, and establishing in-situ causal mapping.

[0004] Therefore, the present invention provides an automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by the present invention to solve its technical problem is as follows: The present invention provides an automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition, which includes a multispectral high-resolution image acquisition module, an atmospheric aerosol optical interference decoupling module, a purification dynamics semantic feature extraction module, an in-situ causal mapping and calibration module, and a purification efficiency quantitative assessment and feedback module. The multispectral high-resolution image acquisition module establishes a high-speed data transmission link with the atmospheric aerosol optical interference decoupling module to provide the system with a low-level optical image sequence with physical consistency throughout the time period. The atmospheric aerosol optical interference decoupling module is logically coupled with the purification dynamics semantic feature extraction module to achieve deep reconstruction of optical degradation features under extreme conditions. The purification dynamics semantic feature extraction module uses feature recognition technology to map the extracted image semantics to the in-situ causal mapping and calibration module; the in-situ causal mapping and calibration module performs in-situ correction on the evaluation logic of the purification efficiency quantitative assessment and feedback module according to real-time air physical indicators, and finally outputs a forest purification efficiency quantitative assessment result with absolute determinism.

[0007] Preferably, the multispectral high-resolution image acquisition module is deployed at key geographical locations in the ecological conservation forest area to perform high-frequency optical capture of forest canopy, leaf microstructure and vegetation cover. The module integrates a photosensitive element with multi-band narrowband filtering function to perform multi-dimensional optical sampling in the visible to near-infrared spectral range. This module acquires raw image data packages of forests under different lighting angles and weather conditions by setting specific spatial and temporal resolution parameters. To ensure the authenticity of optical reflectance energy distribution, the module integrates automatic exposure control logic and spectral radiometric calibration unit. It can adjust the aperture and shutter speed combination and photosensitive gain in real time according to the dynamic changes in ambient background brightness, thereby eliminating pixel semantic distortion caused by overexposure or underexposure. The image data acquired by this module contains high-dimensional spectral reflectance features and spatial texture features, providing a physical benchmark for subsequent purification efficiency evaluation.

[0008] Preferably, the atmospheric aerosol optical interference decoupling module performs deep defocus correction and signal-to-noise ratio enhancement on image signals to address the optical scattering effect caused by high concentrations of fine particulate matter during heavily polluted weather. This module has a built-in atmospheric optical transfer function model, which analyzes the dark channel features and contrast attenuation trajectory in the original image to inversely deduce the statistical characteristics of atmospheric aerosol distribution. The decoupling logic of this module is to use the difference in optical transmittance in specific bands to decouple and separate the optical reflection signal of the forest canopy from the scattering noise of the atmospheric background in both the spatial and frequency domains. This module performs subpixel-level edge reconstruction and color restoration of damaged leaf edge features, leaf color saturation features, and canopy geometric features by executing an optical degradation compensation algorithm. This allows the system to restore the true purification dynamics of the forest interior even when near-surface visibility is extremely low. This decoupling process eliminates the negative feedback interference of the atmospheric environment on image semantics, ensuring the continuity of data capture by the monitoring system under extreme pollution conditions.

[0009] Preferably, the purification dynamics semantic feature extraction module performs nonlinear extraction of forest biochemical activity features based on image recognition technology. This module constructs a multi-layer convolutional neural network architecture to perform deep semantic segmentation on the decoupled optical image. The core of this module's recognition lies not only in extracting the macroscopic geometric morphology, vegetation cover, and leaf area index of the forest canopy, but also in identifying the microscopic physical anomalies on the leaf surface through image analysis. This module utilizes feature recognition technology to perform quantitative analysis on the visual semantics of leaf surface particulate load, stomatal opening and closing behavior, and photosynthetic fluorescence spectral response of leaves. The module establishes a coupling model between optical reflection energy distribution and pollutant flux changes, transforming pixel-level hue, saturation, and brightness components into physical semantic vectors reflecting the tree's purification activity. Through feature recognition of subtle shifts in leaf surface texture and color, the module achieves visual perception mapping of dynamic biochemical processes such as fine particulate adsorption and gaseous pollutant absorption, thereby penetrating static visual appearances and gaining insight into the true purification dynamics evolution within the forest.

[0010] Preferably, the in-situ causal mapping and calibration module establishes a real-time causal closed loop between image feature recognition results and real air quality physical indicators. This module is electrically connected to a randomly deployed chemical sensor array in the forest area to obtain real-time on-site physical parameters such as fine particulate matter concentration, sulfur dioxide content, and nitrogen oxide flux inside and at the edge of the forest area. The core logic of this module is to execute an in-situ real-time calibration mechanism, and construct a nonlinear mapping matrix between image semantics and physical indicators by analyzing the influence weight of instantaneous air quality indicators on image quality features. When the purification dynamics semantic feature extraction module outputs a certain visual feature vector, the module performs online correction on the interpretation weight of the vector based on the real-time measured pollutant reduction concentration. By eliminating the spatiotemporal barrier between image recognition results and real biochemical indicators, the module elevates image recognition from subjective experience description to an objective physical quantification level, ensuring that the evaluation results have physical semantic support when facing highly dynamic environmental evolution.

[0011] Preferably, the purification efficiency quantitative assessment and feedback module integrates the semantic vector of the purification dynamics semantic feature extraction module and the calibration factor of the in-situ causal mapping and calibration module to perform a comprehensive quantitative assessment of the purification efficiency of ecological conservation forests. This module has a built-in purification efficiency evolution dynamics model, which calculates the instantaneous purification rate and daily cumulative purification amount of forest areas under different pollution loads by integrating meteorological parameters (such as wind speed, humidity, and temperature) and forest structure parameters. The assessment logic of this module outputs a report on the contribution of forests to atmospheric pollutant reduction through the logical fusion of multi-source features. This module has a closed-loop feedback function, which can adjust the decoupling parameters of the atmospheric aerosol optical interference decoupling module and the training weights of the feature extraction module in reverse according to the accuracy feedback of the evaluation results, thereby realizing the self-evolution and accuracy convergence of the system throughout its entire life cycle.

[0012] Preferably, in the specific implementation of the image recognition-based automated monitoring system for the purification efficiency of ecological conservation forests, the multispectral high-resolution image acquisition module adopts a CMOS imaging unit with corrosion-resistant packaging, which can resist the chemical erosion of high humidity and acidic particulate matter in forest areas. The atmospheric aerosol optical interference decoupling module introduces a compensation operator based on a physical optical scattering model when performing decoupling calculations. By performing differentiated transmittance compensation on canopy pixels at different depth levels, it ensures the characteristic consistency of forest purification efficiency in three-dimensional space. The purification dynamics semantic feature extraction module uses sub-pixel displacement detection technology to capture the instantaneous pulsation of leaf micromorphology when identifying stomatal opening and closing states, thereby achieving visual quantification of the real-time respiration intensity of the forest.

[0013] Preferably, the monitoring system also integrates a forest microclimate regulation assessment logic. This logic assesses the reduction efficiency of ecological conservation forests on the local heat island effect by identifying the near-surface water vapor distribution characteristics caused by vegetation transpiration in images and combining it with thermal infrared band data in the spectrum. The assessment results, together with air purification efficiency indicators, constitute a comprehensive index of forest ecosystem services. During the automated monitoring process, the monitoring system can analyze the evolution of forest purification efficiency with the changing growing seasons by establishing a time-series-based feature memory mechanism, providing long-term decision-making basis for smart forestry management.

[0014] Preferably, the in-situ causal mapping and calibration module adopts a dynamic calibration algorithm based on the least squares method. When the meteorological conditions in the forest area change drastically, causing sudden noise in the optical signal, the algorithm can force the image recognition logic to return to the physical real domain according to the physical reading of the chemical sensor, thereby effectively suppressing the evaluation drift caused by optical anomalies.

[0015] The beneficial effects of this invention are as follows: 1. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition described in this invention effectively overcomes the technical bottleneck of the nonlinear disconnect between optical signals and physical and biochemical processes by constructing a purification dynamics semantic feature extraction module. This feature enables the system to go beyond a superficial description of static physical extensions such as forest height and canopy, and instead deeply perceive the dynamic changes in the adsorption and biochemical activity of fine particulate matter inside the forest canopy through image recognition technology, achieving a logical leap from visual perception to functional quantification.

[0016] 1. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition described in this invention significantly enhances the robustness of the system in extreme environments by setting up an atmospheric aerosol optical interference decoupling module. By using an atmospheric optical transfer function model to perform deep defocus correction on the image, it solves the systemic problem of feature loss in heavy pollution weather and low visibility conditions in existing technologies, ensuring that the system still has a high signal-to-noise ratio data acquisition capability even when the forest purification load is at its highest.

[0017] 2. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition described in this invention eliminates the spatiotemporal mismatch between offline chemical sampling and online image recognition by introducing an in-situ causal mapping and calibration mechanism. Through real-time correction of physical parameters, the system achieves in-situ real-time calibration of the evaluation results, greatly reducing the error redundancy caused by empirical semantic interpretation.

[0018] 3. The image recognition-based automated monitoring system for the purification efficiency of ecological conservation forests described in this invention constructs a fully automated closed loop from perception to calibration, evaluation, and feedback through a multi-module logical coupling architecture. This feature not only improves monitoring efficiency but also ensures that the monitoring system can adapt to ecological conservation forests in different geographical areas and with different tree species combinations through continuous parameter optimization and weight correction.

[0019] 4. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition described in this invention achieves simultaneous monitoring of multiple dimensions of purification efficiency, such as fine particulate matter reduction, gaseous pollutant absorption, and microclimate regulation, through in-depth mining of the physical characteristics of forest canopy images. This image recognition-based all-time dynamic evaluation scheme provides irreplaceable digital decision support for the scientific planning and precise management of urban ecological barriers, and has made outstanding progress in improving the monitoring level of smart forestry in my country. Attached Figure Description

[0020] The invention will now be further described with reference to the accompanying drawings.

[0021] Figure 1 This is a structural block diagram of an automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition, as described in this invention. Detailed Implementation

[0022] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0023] like Figure 1As shown in the figure, an automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to an embodiment of the present invention includes a multispectral high-resolution image acquisition module, an atmospheric aerosol optical interference decoupling module, a purification dynamics semantic feature extraction module, an in-situ causal mapping and calibration module, and a purification efficiency quantitative assessment and feedback module. The above modules perform electrical connection and data interaction through high-speed industrial Ethernet or dedicated fiber optic links to ensure the determinism of signal transmission under the complex electromagnetic environment of forest areas.

[0024] The multispectral high-resolution image acquisition module, as the system's original sensing point, is deployed at key geographical locations in the ecological conservation forest area to perform high-frequency optical capture of forest canopy, leaf microstructure, and vegetation cover. In a specific engineering application scenario, the module is installed on the top of an observation tower that is five to ten meters higher than the average canopy height of the forest. The core imaging unit of the module adopts a CMOS imaging unit with corrosion-resistant packaging. The packaging process of this unit uses a special polytetrafluoroethylene coating and a tempered optical glass window, which can effectively resist electrochemical corrosion in the high humidity environment of the forest area and chemical erosion of acid rain particles. The multispectral high-resolution image acquisition module integrates a photosensitive element with multi-band narrowband filtering capabilities to perform multi-dimensional optical sampling in the visible to near-infrared spectral range (specifically covering wavelengths from 400 nanometers to 1100 nanometers). By setting specific spatial resolution parameters (such as sub-millimeter pixel resolution) and temporal resolution parameters (such as performing full-spectrum acquisition every 15 minutes), the module acquires raw image data packets of forests under different lighting angles and weather conditions. To ensure the authenticity of the optical reflection energy distribution, the module integrates automatic exposure control logic and spectral radiometric calibration unit. The automatic exposure control logic uses a built-in photoresistor and high-speed processing chip to adjust the aperture and shutter speed combination and photosensitive gain in real time according to the dynamic changes in ambient background brightness. Furthermore, the spectral radiometric calibration unit compensates for the quantum efficiency drift of the photosensitive element by scanning the built-in standard diffuse reflector once per hour, thereby eliminating pixel semantic distortion caused by overexposure, underexposure, or sensor aging. The image data finally acquired by this module contains high-dimensional spectral reflectance features and spatial texture features, providing a physically consistent underlying benchmark for subsequent purification efficiency evaluation.

[0025] The atmospheric aerosol optical interference decoupling module establishes a high-speed data transmission link with the multispectral high-resolution image acquisition module, undertaking the processing tasks of the original image sequence. Addressing the optical scattering effect caused by high concentrations of fine particulate matter during heavily polluted weather, this module performs deep defocusing correction and signal-to-noise ratio enhancement of the image signal. The module incorporates an atmospheric optical transfer function model based on Mie scattering theory and Rayleigh scattering law. By analyzing the dark channel characteristics and contrast attenuation trajectory in the original image, it inversely derives the statistical characteristics of atmospheric aerosol distribution. The decoupling logic of the atmospheric aerosol optical interference decoupling module lies in utilizing the difference in optical transmittance in specific bands (such as near-infrared and short-wave infrared bands) to decouple and separate the optical reflection signal of the forest canopy from the scattering noise of the atmospheric background in both the spatial and frequency domains. Specifically, this module introduces a compensation operator based on a physical optical scattering model by executing an optical degradation compensation algorithm. This compensation operator performs sub-pixel-level edge reconstruction and color restoration of damaged leaf edge features, leaf color saturation features, and canopy geometric features by performing differentiated transmittance compensation on canopy pixels at different depth levels. In actual operation, even at extremely low visibility times with near-surface visibility below 500 meters, this module can still restore the true purification dynamics evolution characteristics inside the forest by removing the defocusing blurring effect caused by aerosols. This decoupling process eliminates the negative feedback interference of the atmospheric environment on image semantics from the algorithm mechanism, ensuring the physical continuity and semantic accuracy of data capture by the monitoring system under extreme pollution conditions.

[0026] The purification dynamics semantic feature extraction module is logically coupled with the atmospheric aerosol optical interference decoupling module to receive the repaired high-fidelity image sequence. This module performs nonlinear extraction of forest biochemical activity features based on image recognition technology. This module constructs a multi-layer convolutional neural network architecture (including deep convolutional layers, pooling layers and attention mechanism layers). Deep semantic segmentation of optical images: The core of this module is not only to extract static indicators such as the macroscopic geometric morphology of the forest canopy, vegetation cover and leaf area index, but also to identify microscopic physical anomalies on the leaf surface through high-order image analysis technology. Specifically, the purification dynamics semantic feature extraction module uses sub-pixel displacement detection technology and time series feature flow analysis to capture the instantaneous pulsation of leaf micromorphology when identifying the stomatal opening and closing state, thereby achieving visual quantification of the real-time breathing intensity of the forest. Furthermore, this module utilizes feature recognition technology to perform quantitative analysis on the pixel accumulation effect of leaf surface particulate matter load and the photosynthetic fluorescence spectral response of leaves. By establishing a coupling model between optical reflection energy distribution and pollutant flux changes, this module transforms the hue, saturation, and brightness components at the pixel level into physical semantic vectors that reflect the tree's purification activity. This transformation process can identify the visual roughness changes in leaf surface texture caused by the adsorption of fine particulate matter, thereby penetrating the static visual appearance and gaining insight into the true purification dynamics evolution process within the forest.

[0027] The in-situ causal mapping and calibration module acts as a physical anchor in the system logic. This module establishes a real-time causal closed loop between image feature recognition results and real air quality physical indicators. The in-situ causal mapping and calibration module is electrically connected to a randomly deployed chemical sensor array in the forest area to acquire real-time physical parameters such as fine particulate matter concentration (PM2.5 / PM10), sulfur dioxide content, nitrogen oxide flux, and ozone concentration inside and at the edge of the forest area. The core logic of this module is to execute an in-situ real-time calibration mechanism. By introducing a dynamic calibration algorithm based on the least squares method, the influence weight of real-time air quality indicators on image quality features is analyzed. Specifically, when the purification kinetics semantic feature extraction module outputs a certain visual feature vector (such as leaf color distortion feature), the in-situ causal mapping and calibration module calculates the causal correlation between the visual feature and the actual purification increment based on the real-time measured pollutant reduction concentration, and performs online correction on the interpretation weight of the vector. This module eliminates the spatiotemporal barrier between image recognition results and real biochemical indicators, elevating image recognition from subjective experience description to an objective physical quantification level. Furthermore, when drastic changes in forest weather conditions cause sudden noise in optical signals, this module can use physical readings to force the image recognition logic to return to the physical real domain, thereby effectively suppressing the evaluation drift caused by optical anomalies and completely breaking the semantic black box state of traditional image recognition systems in extreme environments.

[0028] As the intelligent decision-making layer of the entire monitoring system, the purification efficiency quantitative assessment and feedback module integrates the semantic vector of the purification dynamics semantic feature extraction module and the calibration factor of the in-situ causal mapping and calibration module to perform a comprehensive quantitative assessment of the purification efficiency of ecological conservation forests. This module has a built-in purification efficiency evolution dynamics model, which integrates real-time acquired meteorological parameters (such as wind speed, humidity, temperature, and atmospheric pressure) and forest structure parameters to calculate the instantaneous purification rate and daily cumulative purification amount of the forest area under different pollution loads. Specifically, this module calculates the dry deposition rate of particulate matter in the atmosphere by the forest and the stomatal absorption flux of gaseous pollutants. Furthermore, the monitoring system also integrates a forest microclimate regulation assessment logic. This logic assesses the reduction efficiency of ecological conservation forests on the local heat island effect by identifying the near-surface water vapor distribution characteristics caused by vegetation transpiration in the image and combining it with thermal infrared band data in the spectrum. The assessment results are output as a report on the contribution of forests to atmospheric pollutant reduction and a comprehensive index of ecosystem services through logical fusion of multi-source characteristics. In addition, the module has a closed-loop feedback function, which can adjust the compensation operator parameters of the atmospheric aerosol optical interference decoupling module and the convolution kernel training weights of the feature extraction module in reverse according to the deviation between the evaluation results and the measured values, thereby realizing the self-evolution and accuracy convergence of the system throughout its entire life cycle.

[0029] In its implementation, the system also establishes a time-series-based feature memory mechanism to analyze the evolution of forest purification efficiency with the changing growing seasons. This mechanism can identify the differentiated purification capabilities of forests during the spring budding period, summer vigorous growth period, autumn shedding period, and winter dormancy period, providing long-term decision-making basis for smart forestry management. The hardware casing of the monitoring system is made of die-cast aluminum alloy that meets IP67 protection standards, and is equipped with a temperature control unit to ensure that the high-performance computing chip can maintain stable computing power output in severe cold or hot environments.

[0030] Example 1: The automated monitoring system of this invention was deployed and applied in an ecological conservation forest on the north side of a city. In this project, the system of this invention was deployed, which includes all five core modules and adopts a multispectral photosensitive unit (visible light, near infrared, thermal infrared). The system was installed on a monitoring platform at a height of 25 meters above the ground.

[0031] Acquisition parameters: spatial resolution 5mm / pixel, spectral channels covering 450nm, 550nm, 670nm, 850nm, and 1100nm.

[0032] Operating conditions: Encountering a severe smog weather lasting 72 hours, with visibility fluctuating between 300m and 800m.

[0033] Algorithm execution: The decoupling module performs physical compensation calculations every ten minutes; the extraction module synchronously identifies the opening and closing degree of the blade stomata; and the calibration module connects to five high-precision gas sensor monitoring points.

[0034] Comparative Example 1: Traditional forest monitoring systems based on RGB image recognition are deployed at the same locations and within the same time periods.

[0035] Acquisition parameters: Standard 4K resolution RGB camera.

[0036] Operating conditions: same heavy smog background.

[0037] Algorithm execution: It only uses a simple image dehazing enhancement algorithm and lacks decoupling of aerosol physical properties; the evaluation logic is based on empirical formulas and lacks in-situ physical index calibration.

[0038] Performance monitoring comparison data table of the examples and comparative examples

[0039] The above comparative data clearly shows that the series of technical effects produced by the present invention have significant certainty. In particular, under the extreme condition of heavy pollution weather, the traditional solution completely fails due to the large-scale degradation of optical signals, while the present invention, with the deep correction capability of the atmospheric aerosol optical interference decoupling module, can still produce canopy images with extremely high signal-to-noise ratio. The application of the in-situ causal mapping and calibration module ensures that the purification efficiency output by the system is no longer based on guesswork of pixel color, but on physical quantitative data supported by real air quality indicators.

[0040] Furthermore, in the extraction of semantic features of purification dynamics, this invention achieves non-destructive monitoring of plant physiological state by capturing microscopic anomalies in leaves through convolutional neural networks. For example, during periods of high incidence of photochemical smog, the system can identify stomatal closure feedback caused by ozone stress in leaves. After this semantic feature is captured by the purification efficiency quantitative assessment and feedback module, the evaluation weight of instantaneous purification efficiency is automatically corrected. This intelligent correction based on plant physiological feedback is unparalleled by any existing monitoring system that is simply based on image enhancement or statistical models.

[0041] In the specific algorithm implementation of the in-situ causal mapping and calibration module, this invention adopts the dynamic window weighted least squares method. This algorithm can identify instantaneous anomalies in chemical sensor data (such as sudden jumps in readings caused by small-scale local interference) and automatically remove these physical noise points by comparing them with the image recognition results laterally. Furthermore, by integrating data from the spectral radiometric calibration unit, the system ensures the comparability of image data across time dimensions under different seasons and light intensities, which is of irreplaceable value for analyzing the long-term purification evolution of ecological conservation forests.

[0042] When performing the final evaluation, the quantitative assessment and feedback module for purification efficiency also incorporates a fluid dynamics correction coefficient. Based on the wind speed vector fed back by the anemometer at the edge of the forest, this module can calculate the residence time of air pollutants within the forest stand, thereby more accurately calculating the forest's purification efficiency for passing airflows. This multi-source data fusion logic gives the final output purification efficiency report extremely high scientific authority and engineering applicability.

[0043] It should be noted that the multispectral high-resolution image acquisition module has adaptive defrosting and dust removal logic. It uses image recognition technology to monitor the cleanliness of the protective window in real time. When rain or snow obstruction or sand or dust is detected, it automatically activates the built-in high-pressure jet dust removal unit or electric heating defrosting unit. This hardware-level self-maintenance logic, combined with the algorithm-level self-evolution logic, ensures that the system can achieve truly long-term, all-weather automated monitoring in sparsely populated forest areas.

[0044] In summary, this invention, through the deep integration of optical, physical, biochemical, and computer vision technologies, constructs a logically rigorous and definitive automated monitoring system for the purification efficiency of ecological conservation forests. Through precise signal transfer and feedback mechanisms between modules, it overcomes the sensory blind spots and assessment drift of traditional monitoring systems under heavily polluted weather conditions.

[0045] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition, characterized in that, include: The multispectral image acquisition module is used to perform multi-dimensional optical sampling of the canopy and leaf structure of ecological conservation forests to acquire raw image data packets. The aerosol optical interference decoupling module establishes a data transmission link with the multispectral image acquisition module, and is used to perform defocus correction and signal separation of image signals using the atmospheric optical transfer function model to restore the dynamic evolution characteristics of purification. The purification dynamics semantic feature extraction module is used to perform semantic segmentation on the image output by the aerosol optical interference decoupling module based on image recognition technology, and extract the physical semantic vector reflecting the purification activity of vegetation. The in-situ causal mapping and calibration module is logically coupled to the purification dynamics semantic feature extraction module and connected to the chemical sensor array arranged in the forest area. It is used to perform in-situ correction of the physical semantic vector using real-time air physical indicators. The purification efficiency quantitative assessment and feedback module is used to integrate the calibrated physical semantic vector, meteorological parameters and forest structure parameters, output the forest purification efficiency quantitative assessment result, and adjust the operating parameters of the aerosol optical interference decoupling module and the purification dynamics semantic feature extraction module in reverse according to the assessment accuracy.

2. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The multispectral image acquisition module includes: The imaging unit integrates a photosensitive element with multi-band narrowband filtering function, and performs optical sampling in the spectral range of 400 nanometers to 1100 nanometers; The calibration unit, which incorporates a spectral radiometric calibration component and a standard diffuse reflector, is used to periodically compensate for the quantum efficiency drift of the photosensitive element. The exposure control unit integrates automatic exposure control logic, which adjusts the aperture and shutter speed combination and light sensitivity gain in real time according to the dynamic changes in ambient background brightness to eliminate pixel semantic distortion.

3. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The specific logic of the aerosol optical interference decoupling module when performing signal restoration includes: The dark channel features and contrast decay trajectory in the original image data packet were analyzed, and the statistical characteristics of atmospheric aerosol distribution were derived in reverse based on Mie scattering theory and Rayleigh scattering law. By utilizing the difference in optical transmittance in specific bands, the optical reflection signal of the forest canopy and the scattering noise of the atmospheric background are decoupled and separated in the spatial and frequency domains. A compensation operator based on a physical optical scattering model is introduced to perform differentiated transmittance compensation on canopy pixels at different depth levels, and to perform sub-pixel-level edge reconstruction and color restoration to restore leaf edge features, leaf color saturation features and canopy geometric features.

4. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The purification dynamics semantic feature extraction module performs the following steps for feature extraction: A multi-layer convolutional neural network architecture containing deep convolutional layers, pooling layers, and attention mechanism layers is constructed to perform deep semantic segmentation on decoupled optical images. Identify the macroscopic geometry of the forest canopy, vegetation cover, and leaf area index; Subpixel displacement detection technology is used to capture the instantaneous pulsation of leaf micromorphology in order to achieve visual quantification of the real-time breathing intensity of forests. Image analysis was used to identify the pixel accumulation effect of particulate load on leaf surface and the photosynthetic fluorescence spectral response of leaves.

5. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 4, characterized in that, The purification dynamics semantic feature extraction module also includes: The coupling modeling unit is used to establish a coupling model between the distribution of optical reflection energy and the change of pollutant flux, and to convert the hue, saturation and brightness components at the pixel level into the physical semantic vector. The feature recognition unit is used to capture the visual roughness changes of the leaf surface texture caused by the adsorption of fine particulate matter, thereby gaining insight into the evolution of purification dynamics within the forest.

6. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The specific calibration logic of the in-situ causality mapping and calibration module includes: The chemical sensor array is used to acquire physical parameters such as fine particulate matter concentration, sulfur dioxide content, nitrogen oxide flux, and ozone concentration in real time within and around the forest area. A dynamic calibration algorithm based on the least squares method is adopted to analyze the influence weight of real-time air quality indicators on image quality features and construct a nonlinear mapping matrix between image semantics and physical indicators. Based on the real-time measured pollutant reduction concentration, the interpretation weights of the physical semantic vector are corrected online. When sudden changes in weather conditions cause sudden noise in optical signals, the physical parameter readings are used to force the image recognition logic to return to the physical reality domain, thus suppressing evaluation drift.

7. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The quantitative assessment and feedback module for purification efficiency, when performing quantitative assessment, specifically includes the following process: The calibration factor, real-time meteorological parameters, and forest structure parameters output by the in-situ causal mapping and calibration module are integrated. Using the built-in purification efficiency evolution dynamics model, the dry deposition rate of particulate matter and the stomatal absorption flux of gaseous pollutants in forest areas under different pollution loads were calculated. Output a report on the contribution of forests to atmospheric pollutant reduction, and combine the deviation between the quantitative assessment results of forest purification efficiency and the measured values ​​to reverse correct the compensation operator parameters of the aerosol optical interference decoupling module and the convolution kernel training weights of the purification dynamics semantic feature extraction module.

8. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The monitoring system also integrates forest microclimate regulation assessment logic, the specific steps of which include: By identifying the distribution characteristics of near-surface water vapor caused by vegetation transpiration in images, and combining the thermal infrared band data in the spectrum, the near-surface energy balance parameters are calculated. The effectiveness of ecological conservation forests in reducing the local heat island effect will be assessed, and the assessment results will be used as a component of the comprehensive forest ecosystem service index in the final quantitative assessment of effectiveness.

9. The automated monitoring system for the purification efficiency of ecological conservation forests based on image recognition according to claim 1, characterized in that, The monitoring system establishes a time-series-based feature memory mechanism and executes the following evolutionary analysis logic: By analyzing historical characteristic data streams, we can extract the evolutionary pattern of forest purification efficiency with the changing growing seasons; Identify the differentiated purification capacity models of forests during the budding, vigorous growth, shedding, and dormant stages to provide a long-term decision-making basis for smart forestry management.

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