74 results about "Class activation mapping" patented technology

The invention discloses a remote sensing image pseudo label generation method based on prototype perception learning, and a weak supervision change detection model training and detection method, and belongs to the field of remote sensing image change detection. The invention aims to solve the problem that a category activation graph is incomplete in existing remote sensing image weak supervision change detection and the problem that an obtained detection model is unstable in change recognition. According to the method, feature maps of different scales before and after change are extracted from remote sensing images before and after change through a pseudo label generation model, change features are obtained, a class activation mapping map and a class activation mapping map of an unchanged class are obtained, and the class activation mapping map is segmented into response area masks through a threshold value and used for performing weighted aggregation on fusion features to obtain a class prototype; generating an enhanced class activation mapping graph based on the fusion feature and the class prototype, and selecting the class with the highest similarity as a pseudo tag of the pixel; and training based on the pseudo label data of the pixels to obtain a change detection model, wherein the model is used for remote sensing image change detection.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

The invention relates to the technical field of fault diagnosis, in particular to a few-label self-supervised learning fault diagnosis method based on an interpretable neural network, and the method comprises the steps: collecting time domain data of a sensor of an aero-engine under different health conditions, carrying out the standardization processing, and dividing the time domain data into a pre-training set, a training set, a verification set and a test set; performing fast Fourier transform and data enhancement on the time domain data in the pre-training set to obtain frequency domain data and enhanced time domain data; constructing a pre-training framework, and performing pre-training to convergence based on the frequency domain data and the enhanced time domain data to obtain a pre-trained time encoder; constructing a fault diagnosis model based on the pre-trained time encoder, and performing iterative training to convergence by using the training set to obtain a trained fault diagnosis model; and performing fault diagnosis on the test set by using the trained fault diagnosis model, and performing visual interpretation on the diagnosis process of the fault diagnosis model by using a gradient weighting class activation mapping technology.

The invention relates to the technical field of artificial intelligence, in particular to a class activation mapping interpretability method fusing a spatial perturbation mechanism, which comprises the following steps: a first stage: capturing contribution of each channel to prediction, and obtaining a channel attention weight of a deep learning model through a gradient method; in the second stage, disturbance analysis is introduced, and space importance weights given to image samples by the model are obtained; and a third stage: fusing the interpretation information of the two view angles through weighted integration, and generating an interpretation saliency map of the region-level granularity. According to the invention, the dual positioning capability of an interpretable technology in a channel domain and a space domain can be improved.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses an SAR image weak supervision semantic segmentation method based on decision optimization pseudo label generation, and the method comprises the following steps: 1, training a multi-label classification network, and obtaining class activation mapping results of different layers of a neural network and pixel-level prediction pseudo labels; 2, fusing different levels of class activation mapping results to obtain a class activation mapping result with higher confidence; step 3, forming a decision by utilizing the relation between different layers of pseudo labels on the pixels, and extracting multi-layer decision pixel relation information; 4, constructing a relation network among pixels, and obtaining a centroid displacement vector and boundary features of each pixel in the image; 5, training a relation network between pixels by using the extracted pixel relation information to obtain a pixel semantic affinity matrix; step 6, performing pseudo tag optimization by using the pixel semantic affinity matrix; and step 7, training a semantic segmentation network by using the optimized pixel-level pseudo labels, and completing SAR image weak supervision semantic segmentation. According to the method provided by the invention, the high-quality polarized SAR image pixel-level pseudo tag can be generated, and pixel-level semantic segmentation of the SAR image under the condition of image-level annotation weak supervision is realized.

The invention discloses a hospital terminal threat behavior recognition method based on interpretable deep learning, and particularly relates to the technical field of network security, threat intelligence analysis and interpretable artificial intelligence. According to the method, a multi-modal hospital terminal operation data set fusing time sequence images, logs and other data is constructed, and different modal data are effectively integrated by adopting a multi-modal data fusion method; integrating a dynamic attention mechanism into a long short-term memory network model for adaptively extracting key features in a terminal user behavior sequence; and a double-layer interpretable framework based on gradient weighted class activation mapping and a local interpretable model irrelevant interpretation technology is introduced, so that high-precision recognition and interpretable analysis of the threat behavior of the hospital terminal user are realized. According to the method, complex threat behaviors in the hospital terminal can be efficiently and accurately identified, the limitation of a traditional method in the aspects of data diversity, interpretability and hidden threat detection is overcome, and the transparency and efficiency of threat analysis are enhanced.

The invention discloses a method and device for predicting the transmembrane capability of a substance molecule, terminal equipment and a storage medium, and the method comprises the steps: obtaining a target molecular substance, and determining a molecular structure diagram corresponding to the target molecular substance; according to a pre-trained prediction model, determining a permeation free energy prediction value of the target molecular substance; according to the prediction model, multilevel feature extraction is carried out on a molecular structure diagram of sample substance molecules by utilizing a graph neural network model, contribution values of atoms in the sample substance molecules to transmembrane behaviors are evaluated through a gradient weighted class activation mapping method, and permeation free energy prediction values corresponding to the sample substance molecules are output. The graph neural network is trained to obtain the prediction model, and the prediction model is adopted to predict the transmembrane capability of the target molecular substance, so that intelligent screening of the transmembrane capability of a large-scale drug molecular library can be realized, the prediction efficiency and flux are remarkably improved, and intelligent prediction of the transmembrane free energy of substance small molecules is realized.

The invention discloses a fault diagnosis method, system, equipment and medium for a wind power gear box, and relates to the technical field of fault detection, and the method comprises the steps: collecting a vibration signal of the wind power gear box, and obtaining a time-frequency diagram of the vibration signal; capsule feature tensors in the time-frequency graph are obtained, a time-frequency two-dimensional network is formed in space, and corresponding query tensors are extracted from a time axis and a frequency axis of the time-frequency two-dimensional network by adopting different convolution kernels; dot product matching is carried out on the corresponding query tensors and a preset shared key value pair, attention distribution of each query tensor is obtained, and aggregation output in the directions of the time axis and the frequency axis is fused through the attention distribution; and performing class activation mapping on the aggregated output, determining a fault category of the wind power gear box, generating a class activation graph focused on the key time-frequency region through the fault category, and displaying a discrimination basis. The method can focus on a key time-frequency region with clear physical significance, and obviously improves the discrimination transparency and engineering credibility of the model.

The invention discloses a model decision interpretability method fusing integral gradient and class activation mapping, and belongs to the technical field of deep learning interpretability. Aiming at the problems of gradient noise interference, insufficient space positioning and the like existing in an existing single interpretability method, the method is optimized through five key steps: firstly, extracting a feature map set of the last convolutional layer of a deep learning model; secondly, calculating an integral gradient of the feature map to a target category based on a path integral idea; thirdly, obtaining a feature map weight through global average pooling; then, weighted summation is carried out, and an initial attribution thermodynamic diagram is generated through ReLU activation; and finally, a high-resolution thermodynamic diagram is obtained in combination with guided gradient optimization. According to the method, gradient stability of Integrating Gradients and spatial positioning advantages of Grad-CAM are fused, invariance and sensitivity axioms are realized, model adaptability is maintained, accuracy and robustness of interpretation results are remarkably improved, and the method is suitable for key fields such as signal processing and image classification.

The invention discloses a multi-modal health knowledge generation and integration system, which is applied to the technical field of fusion and application of health data, and comprises a multi-modal data acquisition and preprocessing module used for acquiring multi-modal health data and preprocessing the multi-modal health data; the multi-level fusion framework design sub-module comprises data-level fusion based on a time alignment algorithm, feature-level fusion based on a Transform cross-modal attention mechanism and decision-making-level fusion based on an ensemble learning algorithm; and the intelligent knowledge generation sub-module comprises health knowledge graph construction based on a graph neural network, personalized health risk report and intervention scheme generation based on a conditional generative adversarial network, visual image feature contribution degree based on gradient weighted class activation mapping and text data key factor analysis based on an SHAP value. According to the method, the bottleneck of a data island is fundamentally broken through, and the generation quality and the application value of health knowledge are improved.

The invention provides an improved multi-label image classification method based on multi-scale double dynamic graph convolution. The invention discloses the multi-label image classification method based on the improved multi-scale double dynamic graph convolution. The invention provides an improved multi-scale double dynamic graph convolution multi-label image classification method for solving the problems that traditional class activation mapping contains noise and the convolution effect becomes poor due to the high expansion rate of cavity convolution. The method comprises the following steps: firstly, introducing depth separable convolution to replace cavity convolution, and redesigning a combination mode of different scale features to obtain improved multi-scale features; secondly, a weight suppression mechanism is introduced to weaken class activation of irrelevant classes, and the class activation is multiplied by the improved multi-scale features to generate a new content awareness vector; and finally, inputting the content perception vector into a double-graph convolution fusion embedded network for final classification. Experimental results show that the precision of the provided model is improved on MS-COCO and VOC2007 data sets, and it is proved that the improved model has better performance.

This invention relates to a heatmap evaluation method and system based on class activation mapping and dynamic weights, comprising: acquiring crop images; inputting the crop images into a pre-trained deep learning model to output crop detection results; processing the crop detection results into a heatmap based on class activation mapping, and performing noise reduction processing on the heatmap to obtain a heatmap without background interference; performing color space conversion on the heatmap without background interference, and calculating the color difference between each pixel and a reference color, wherein the reference color includes a preset reference color and corresponding weight based on the heatmap; assigning a weight value to each pixel based on the color difference between each pixel and the reference color, and calculating an average temperature weight value; and completing a quantitative evaluation of the heatmap based on the average temperature weight value. This invention provides a new interpretive dimension and quantitative standard for the application of deep learning models in agriculture.

The invention belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a weakly supervised brain tumor segmentation method which comprises the following steps: acquiring a low-level glioma segmentation data set of a cancer genome map, and randomly dividing the data set into a training set and a test set according to patient division; based on the image data in the data set, carrying out transfer learning by using a pre-trained ResNet50 as a backbone network; a global optimization mechanism of an SZIO algorithm is utilized to perform adaptive optimization and intelligent screening on features extracted based on a traditional class activation mapping technology, and fine fusion and semantic enhancement of multi-level features are realized; and performing fine adjustment on the ResNet50 backbone network at a preset initial learning rate. According to the method, the performance breakthrough of weakly supervised brain tumor segmentation can be realized, the practicability, robustness and clinical transformation potential are relatively high, and an efficient and reliable intelligent segmentation solution is provided for the field of medical image analysis.

This invention discloses a deep learning-based weight estimation method, relating to the field of computer vision technology. By collecting side-view depth image data of a single sheep, and using sheep detection and sheep body semantic segmentation models to augment the collected data, a LiteHRNet network model is used as the backbone network of the weight estimation model to obtain semantic information at different levels from the sheep depth image data. Class activation mapping is used to visualize different head attention regions for weight estimation. Finally, a weight estimation model with small estimation error and concentrated sheep region attention is selected. This method can efficiently estimate sheep weight, thereby saving time and labor costs for livestock workers, and provides a research direction in deep learning for weight estimation of other similar animals.

The application discloses a method and system for predicting protein drug binding sites based on a multi-modal dynamic graph, comprising: obtaining amino acid sequence data and three-dimensional structure data of a protein, and generating evolutionary conservation features, structure graph topology features and sequence features respectively; inputting the features into a multi-modal fusion encoder to generate first fusion features; inputting the first fusion features into a prediction decoder to generate initial prediction probabilities; iteratively updating the structure graph topology features according to a preset three-graph update rule according to the initial prediction probabilities and residue dynamic communication scores; re-inputting the updated structure graph topology features into the multi-modal fusion encoder and the prediction decoder, and repeatedly executing until a preset iteration number is reached, to generate final prediction probabilities; and generating a residue importance heat map and outputting a prediction report based on the final prediction probabilities through a gradient weighted class activation mapping algorithm. The application realizes the cooperative optimization of prediction and graph structure, and significantly improves the accuracy of binding site prediction.

The invention discloses a car insurance claim settlement method and system based on a multi-modal large model, and the method comprises the steps: collecting the multi-modal data of a historical car insurance claim settlement case, employing a large-scale pre-training image-text large model to carry out the multi-task joint training and field adaptation fine tuning, and achieving the joint semantic understanding of an accident record text and a vehicle damage picture; the method comprises the following steps of: analyzing VIN (Vehicle Identification Number), embedding structured information, introducing a part association graph as an external knowledge constraint, and completing efficient fine adjustment of a model by adopting a hierarchical parameter freezing and low-rank adaptation technology under the condition of a small number of samples; in the reasoning stage, a structured damage analysis report, a compensation amount predicted value and confidence evaluation are output, a damage area thermodynamic diagram is generated in combination with a gradient weighting class activation mapping technology, and a visual decision basis is provided. The technical problems that traditional artificial claim checking is low in efficiency, high in cost and large in result difference, and an existing AI technology is high in data threshold, poor in field adaptability and lack of interpretability are solved.

The present invention discloses a method and terminal device for evaluating adversarial robustness based on class activation maps. The method comprises adding perturbations to original samples using an adversarial attack algorithm to obtain adversarial samples; obtaining class activation maps of a deep learning model based on the original samples and class activation maps based on the adversarial samples; performing adversarial defense training on the deep learning model using the original samples and the adversarial samples to obtain a trained deep learning model; obtaining class activation maps of the trained deep learning model based on the original samples and class activation maps based on the adversarial samples; and evaluating the adversarial robustness of the deep learning model based on the class activation maps of the original samples and the adversarial samples before and after training. The method and terminal device of the present invention can mine implicit knowledge in the deep learning model, evaluate adversarial robustness from the perspective of the model itself rather than direct model results, and introduce key position information to enhance the interpretability of the evaluation results.

This invention discloses an unsupervised lesion segmentation method based on large model and pseudo-label learning, belonging to the field of image recognition. First, a "Visual-Language" large model (CLIP) is used to generate high-quality classification pseudo-labels, which are then used to supervise the training of an image classification network. During this process, we further extract the class activation map (CAM) of the classification network and introduce a data augmentation strategy based on dynamic occlusion to further improve the performance of the classification network. Subsequently, we utilize these more accurate class activation maps to generate guidance information for the segmentation large model (SAM), thereby obtaining high-quality segmentation pseudo-labels. Finally, this invention proposes a self-training strategy to train and continuously optimize the segmentation network using these pseudo-labels to achieve higher segmentation accuracy and efficiency.

The invention discloses a high-spatial-resolution remote sensing image solar cell panel identification method based on deep learning. The method comprises the following steps: (1) constructing a high-spatial-resolution RGB satellite image solar cell panel data set containing various complex scenes; (2) carrying out transfer learning twice on the basis of an Inception-v3 network architecture so as to optimize a pre-training weight; (3) realizing visualization and semantic segmentation of the solar cell panel through morphological processing and threshold optimization by using a class activation mapping (CAM) technology; and (4) verifying the recognition effect through evaluation indexes (such as accuracy and F1 score). According to the method, for complex scenes such as roof angles, shadows and sundry shielding, through multi-scale feature extraction of the network and data enhancement of the data set, the precision and robustness of solar cell panel recognition are remarkably improved, and reliable data support is provided for regional solar deployment and social and economic benefit analysis.

This invention discloses a weakly supervised open-pit mining site identification method based on adaptive SAM and automatic knowledge learning. The method includes: S1, collecting images of the study area and extracting three-band images using an input spectral adapter; extracting depth features using the feature extractor of the MobileSAM model; constructing a multi-scale perception and geometric adaptation module and obtaining image features by applying N² dilated convolutional units with different dilation rates to the depth features; S2, a scene classifier using predefined prototype features to guide image features in scene classification; then, a cue generator using class activation mapping to obtain a heatmap for scene classification and selecting a set of high-confidence cue points; and a pseudo-label generator constructing triplet contrast constraints; S3, the mask decoder of the MobileSAM model combining cue information and image features to perform cue decoding and obtain the open-pit mining site identification result. This invention combines a set of high-confidence cue points with image features for cue decoding to obtain efficient and high-precision open-pit mining site identification results.

The application discloses a kind of weak supervision medical image segmentation method based on causality invariance, the application uses two-stage training process, the first stage is label generation, and the picture-level label is generated pixel-level pseudo-label by class activation mapping mechanism, solve the problem that existing algorithm highly depends on a large number of artificial marking pixel-level label.The second stage is segmentation model training, and a segmentation model is trained using the pixel-level pseudo-label output in the first stage, to provide effective segmentation results for the actual clinical application of medical image analysis.Furthermore, a multi-branch feature extraction network is designed in the label generation stage of the application, and a causality invariance conversion strategy is proposed, so that the model can better extract the features of the same target in different states.Meanwhile, the application designs a loss function for different branch networks to ensure the classification accuracy of the model.Finally, the application designs a causality invariance constraint loss between multiple branches to ensure the segmentation accuracy of the model.

The application discloses a kind of wind power gear box fault diagnosis method, system, equipment and medium, it is related to fault detection technical field, including steps: the vibration signal of wind power gear box is collected, and the time-frequency diagram of vibration signal is obtained;Capsule feature tensor in time-frequency diagram is obtained, and a time-frequency two-dimensional network is constituted in space, and different convolution kernels are used to extract corresponding query tensor in time axis and frequency axis of time-frequency two-dimensional network;The dot product matching of corresponding query tensor and preset shared key-value pair is carried out respectively, the attention distribution of each query tensor is obtained, and the aggregation output in time axis and frequency axis direction is fused by attention distribution;Class activation mapping is carried out to aggregation output, determines the fault category of wind power gear box, and generates class activation map focusing on key time-frequency region by fault category, shows discrimination basis.The application can focus on key time-frequency region with clear physical meaning, significantly improve the discrimination transparency and engineering credibility of model.

The application discloses a kind of weak supervision target positioning methods based on multi-modal, comprising the following steps: obtaining image dataset and is divided into training dataset and test dataset, the training dataset is composed of image and classification label;Adjust the width and height of image data in image dataset, and do normalization to image;Classification label corresponding to image data is generated text data related to category by template;Conformer classification network is constructed;CLIP Text Encoder text encoder and Liner linear layer are constructed;The classification label of image is converted into text data, with the aid of the powerful image-text matching capability of CLIP, the image-text matching target added in the training process, additional supervision information is introduced for the training of network model, to alleviate the problem of class activation mapping.In addition, the application uses Conformer as the classification network of training, and uses the byproduct attention matrix of network model itself to refine class activation mapping, further improves the positioning performance of network model.

The invention discloses a pump cavitation state intelligent identification method based on multi-scale fusion DCNN, and the method comprises the steps: (1) collecting noise signals when a centrifugal pump is in different cavitation states, and segmenting the noise signals into a plurality of noise signal samples in a non-overlapping manner; (2) acquiring a wavelet time-frequency diagram of each noise signal sample, constructing a time-frequency diagram data set, and dividing the time-frequency diagram data set into a training set, a verification set and a test set; (3) constructing a time-frequency enhanced wavelet fusion multi-scale interpretable DCNN model, wherein the model comprises a time-frequency attention module, a fusion multi-scale module and a fusion class activation mapping module; (4) training the constructed model based on the training set, and storing optimal parameters of the model based on the verification set; and (5) testing the model based on the test set, and outputting the predicted cavitation state and the class activation diagram corresponding to each sample. According to the method, high classification accuracy can be obtained in the cavitation state recognition task of the centrifugal pump, and high interpretability is achieved.

The invention discloses an adversarial sample generation method and device based on a classification task, equipment and a medium. The method comprises the steps of obtaining basic biological characteristic data corresponding to a target classification task and task demand information including a task demand type and a target machine learning classification model; performing visual perception processing on the basic biological characteristic data based on a target machine learning classification model and a preset gradient weighting rule to generate a target gradient weighting class activation mapping graph; determining a target sample generation rule corresponding to the target classification task in a preset generation rule base based on the task demand type; and performing confrontation processing on the target gradient weighted class activation mapping graph based on a target sample generation rule to generate a target confrontation sample. Through the technical scheme of the invention, the adversarial sample with relatively strong migration ability in the physical world can be generated, and the attention degree of the classification model on the adversarial sample is improved.

The present invention discloses a method, system, device and storage medium for identifying solid waste, and relates to the field of semantic segmentation technology. The method comprises: inputting a target image into a first classification model to obtain a first class activation map and a second class activation map; calculating a first target class activation map of a background based on the first class activation map; calculating a second target class activation map of the background based on the first class activation map and the second class activation map; determining a target affinity label based on the first target class activation map, the first class activation map, the second class activation map and the second target class activation map; inputting the target image and the target affinity label into a solid waste pseudo-label generation model to obtain a solid waste pseudo-label, thereby training a fully supervised semantic segmentation neural network, obtaining a target solid waste recognition model, identifying solid waste in the target image, and improving the recognition accuracy of solid waste.

The application discloses an adversarial sample visual explanation method based on a class activation map, and comprises an adversarial sample generation stage, an adversarial sample corresponding to a normal sample is generated by adding disturbance to the normal sample through an adversarial attack algorithm; a feature extraction stage, an adversarial sample feature and a normal sample feature are obtained by inputting an adversarial sample picture and a normal sample picture into a trained deep learning model for feature extraction, and sample classification is performed on the adversarial sample and the normal sample; a feature saliency map generation stage, a gradient of a classification result of the adversarial sample to the adversarial sample feature and a gradient of a classification result of the normal sample to the normal sample feature are calculated; and the gradient difference obtained by calculating the normal sample and the adversarial sample is taken as a weight, and linearly weighted fusion is performed on the adversarial sample feature to obtain a final adversarial sample feature saliency map.

The invention discloses an earphone quality test data processing method and device applying deep learning and a medium, and relates to the technical field of deep learning application. According to the method, a double-flow attention deep neural network is constructed, time domain transient features and frequency domain texture features of audio signals are extracted and fused in parallel, and deep association among different modes is captured by using a cross attention mechanism; on the basis of a mahalanobis distance between a to-be-tested sample and a golden sample in a feature space and a model prediction entropy value, a quality confidence coefficient index containing an uncertainty penalty term is constructed, and refined hierarchical management and control of earphone quality are realized; and a class activation mapping algorithm is further combined to generate a visual thermodynamic diagram, and a specific frequency band causing sound quality abnormity is accurately positioned. According to the method, the problems that a traditional testing means is insufficient in complex non-linear defect recognition capability and the result is lack of interpretation are effectively solved, and the intelligent level of earphone production line quality inspection and the defect diagnosis efficiency are improved.

The invention discloses an intelligent identification method for a service trade-off cooperative relationship of a high and cold ecosystem, and the method comprises the steps: obtaining original multi-source heterogeneous data, and carrying out the preprocessing of the data, and obtaining a spatio-temporal data set; constructing a meta training task set based on the spatio-temporal data set so as to train a meta learner, generating corresponding optimal model parameters through the trained meta learner according to a future climate-land utilization scene, inputting the optimal model parameters into a corresponding model for chain simulation, and outputting future multi-time-sequence ecosystem service supply spatial distribution data; according to the space-time diagram convolution network model, obtaining a relation intensity space distribution diagram and further identifying a trade-off cooperative relation, generating a space activation thermodynamic diagram through a gradient weighting class activation mapping technology, and after the space activation thermodynamic diagram is fused with the relation intensity space distribution diagram, determining a key area space distribution diagram according to a quantitative decision rule. And performing rule matching in combination with the ecological management knowledge base, and determining a differentiated ecological management scheme. According to the invention, the recognition accuracy and efficiency can be improved.