76results about How to "Improve the evaluation effect" patented technology

The invention relates to the technical field of pipeline data processing and health state evaluation, and discloses a pipeline interior detection method and system based on multi-sensor fusion. The method comprises the following steps: performing health index calculation on pipeline state evaluation data to obtain a pipeline health state index, and performing risk prediction on the pipeline health state index to obtain a risk level; performing pipeline defect feature recognition to obtain defect feature data, and performing multi-category classification processing on the defect feature data to obtain defect type data; performing accurate position analysis on the defect type data to obtain defect space distribution data, performing structural integrity evaluation to obtain a defect severity quantitative data set, performing multi-criterion decision analysis on the defect severity quantitative data set and risk levels to obtain defect repair priority ranking data, and performing defect repair priority ranking on the defect repair priority ranking data; and performing intelligent repair scheme generation on the defect repair priority ranking data to obtain repair suggestion data. According to the invention, the state of the pipeline can be evaluated more comprehensively and accurately.

The invention discloses an omnibearing risk early warning monitoring method and system based on intelligent wearable equipment, and relates to the technical field of health management, and the method comprises the steps: collecting and compressing multi-modal data through the intelligent wearable equipment, transmitting the compressed data to a central database, and carrying out the data restoration; multi-modal data analysis is carried out on the restored multi-modal data, comprehensive features are extracted, and a logic regression model is constructed to evaluate the health score of the user. According to the invention, the intelligent wearable device collects and compresses the multi-modal data, and transmits the compressed data to the central database for data restoration; performing multi-modal data analysis on the restored multi-modal data, extracting comprehensive features, and constructing a logic regression model to evaluate health scores of the users; the data processing and transmission efficiency is improved, the accuracy and response speed of health monitoring are improved, and the accuracy of data analysis is enhanced.

The invention discloses an integrated circuit junction temperature and thermal resistance evaluation system and method based on a digital twinning technology, and the method comprises the steps: extracting a temperature cloud picture from a simulation calculation result, and extracting a thermal flow line diagram from the temperature cloud picture based on a heat dissipation path, carrying out discrete sampling on the heat flow line diagram by using a deep learning data analysis and coding technology, and then carrying out embedded coding on each piece of sampled heat flow point data (a flow point position and a heat flow point temperature); in this way, the rectangular frame position data of the hot spot region is automatically identified according to the graph walk significant aggregation representation between the data embedding coding features of each heat flow point. Through the mode, the influence of a fine structure (such as a connecting wire, a welding spot and the like) on a heat transfer path can be captured, the complex heat dissipation requirement can be better met, and it is ensured that the heat dissipation requirement is effectively met.

The invention provides an underwater image enhancement quality evaluation method based on quality perception domain adaptation. The method comprises the following steps: S1, acquiring a plurality of original underwater images to generate a training set and a test set; s2, constructing an underwater image enhancement quality evaluation network by using a deep learning framework; s3, training the underwater image enhancement quality evaluation network by using the training set, calculating a root-mean-square error between a prediction quality score and a label corresponding to each first enhancement image, and obtaining network loss in combination with binary cross entropy loss; s4, repeating the step S3 for multiple rounds to obtain an underwater image enhancement quality evaluation network model; and S5, testing each second enhanced image in the test set by using the underwater image enhancement quality evaluation network model to obtain a corresponding predicted quality score as an underwater image enhancement quality evaluation result. The method has the beneficial effects that the quality of the underwater enhanced image can be efficiently and accurately predicted.

The invention discloses an automatic control method for tipping paper printing based on image detection, and relates to the technical field of tipping paper printing, and the method specifically comprises the following steps: obtaining surface feature information of each sub-region in a wrinkling region in a tipping paper printing process in real time, and analyzing the surface feature information after obtaining, respectively generating a light intensity anomaly index and a tension instability index of each sub-region; and constructing a wrinkling influence evaluation model for the light intensity anomaly index and the tension instability index of each generated sub-region, respectively generating a comprehensive evaluation coefficient of each sub-region, performing analysis after generation, and evaluating the influence degree of each sub-region in the wrinkling region on the generated printing alignment parameter. And dividing each sub-region in the wrinkling region into a non-influence region, a low-influence region and a high-influence region according to an evaluation result. According to the invention, the problem of insufficient characteristic identification and evaluation of the wrinkling area is solved, and intelligent improvement of printing alignment precision and finished product quality is realized.

The invention relates to the technical field of medical monitoring, in particular to an intelligent old people health monitoring system, which comprises a heart rate module for collecting heart rate signals, identifying local peak values in the signals to detect heartbeat, measuring time intervals between continuous peak values to determine heart beat intervals, and calculating differences between adjacent intervals to evaluate variability. According to the method, through synchronous analysis of multi-dimensional data of the comprehensive heart rate, the skin conductance and the facial expression, the comprehensive understanding and evaluation ability of the health state of the old people is enhanced. The complex relationship between the emotion change and the physiological response is accurately captured and analyzed by integrating multi-source data without singly depending on any physiological index. Through the method, the health warning signal can be identified in the subtle change, and early warning can be carried out in time. Besides, environmental factors such as illumination, music and fragrance can be dynamically adjusted according to the mood and health state monitored in real time, so that more personalized and optimized environmental response is provided.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention discloses a multi-agent interpretable text psychological health level assessment method and system, and the method comprises the steps: training an original agent model according to a multi-field data set and a psychological dialogue assessment data set, and obtaining a dialogue reference model and an assessment reference model; according to the two reference models, respectively processing the user question data and the multi-round dialogue data to obtain a multi-dimensional dialogue feedback data set and an evaluation feedback data set; according to the multi-dimensional dialogue feedback data set and the evaluation feedback data set, training the two reference models to obtain a dialogue effect evaluation model and an evaluation effect evaluation model; according to the two evaluation models and the psychological dialogue evaluation data set, training the two reference models to obtain a target dialogue model and a target evaluation model; according to the target dialogue model and the target evaluation model, mental health level evaluation is completed. According to the invention, through the multi-dimensional feedback mechanism training model and the cooperative work of multiple models, the interpretability and confidence of the psychological health assessment system are improved.

The invention discloses a transient power angle stability evaluation method, system and device for a power system and a medium. The method comprises the following steps: S1, generating a simulation result of an operation scene by using a time domain simulation function of power system simulation software as a sample, and incorporating the simulation result into a sample set; s2, sample data processing is carried out, bus types in the power system are marked, sample labels are marked, and the power system and simulation results generated in the step S1 are converted into graph structure data; s3, dividing the sample set into a training set, a verification set and a test set; s4, constructing a heterogeneous space-time neural network model; s5, training the heterogeneous space-time neural network model; and S6, inputting data of an actual to-be-evaluated scene into the trained model for evaluation to obtain a stability result. According to the method, the bus type and the heterogeneous space-time neural network model are marked, the difference of transient characteristics of different types of equipment in the power system is considered, and high-precision evaluation of transient power angle stability under the double-high power system is realized.

The invention relates to a point cloud quality assessment method based on depth ordinal regression. The method comprises a feature fusion step based on a symmetric cross-modal attention mechanism and an ordered regression step of point cloud quality assessment. In the feature fusion step, point cloud features are extracted through farthest point sampling and a K nearest neighbor algorithm, point cloud projection is performed to generate an image, image features are extracted, the two features are fused by using a cross-modal symmetric attention mechanism, and global feature representation is generated; in the step of ordered regression, a depth ordinal number loss function is designed to optimize network parameters, the prediction probability is converted into a continuous point cloud quality score by using ordered information of the quality score, a step effect is prevented, and an evaluation result is closer to a true value. Through feature fusion and ordered regression, the accuracy and stability of point cloud quality evaluation are effectively improved, and the evaluation result is closer to a true value.

The invention provides an urban flood disaster risk assessment method based on crowd sensing, a medium and equipment, and the method comprises the steps: collecting flood disaster text data on a social platform, carrying out the preprocessing, and constructing a flood disaster text data set; extracting flood disaster common words from the flood disaster text data set, classifying the flood disaster common words according to subject terms, and constructing a disaster descriptive lexicon based on a co-occurrence network; designing a crowdsourcing questionnaire based on the disaster descriptive word bank, issuing the crowdsourcing questionnaire to a target user group, and collecting flood disaster feedback data and geographic information fed back by the target user group; performing spatial distribution on the collected flood disaster feedback data according to geographic information; and carrying out risk assessment by combining two types of indexes which respectively represent the flood disaster situation and the flood disaster influence area. According to the invention, real-time monitoring and accurate assessment of urban flood disasters are realized, and the accuracy and real-time performance of flood disaster risk assessment are improved.

The invention discloses a multi-model-based mine resource reserve dynamic management system and a construction method, and belongs to the technical field of mine resource management, and the system comprises a model updating data module which is used for designing a data import tool or interface; the basic information module is used for recording and managing key information; the mine right management module is used for recording and managing mine right, environment and policy and regulation information of the mine; the geological exploration management module is used for recording and managing geological exploration activities; the reserve management module is used for generating a reserve report and dynamically updating reserve statistics; the resource planning management module is used for designing, adjusting and monitoring long-term and short-term planning of resources; the system management module is used for maintaining system operation and storing user and authority information; the three-dimensional large screen module is used for displaying a three-dimensional mineral resource model and data; the problems of insufficient real-time performance, low manual operation efficiency, lack of dynamic monitoring capability, insufficient intelligent degree, limited visualization degree and the like in mine resource reserve management are solved.

The invention relates to the field of vehicle battery control, in particular to an anti-theft starting battery control method and system based on multi-source data. The method comprises the following steps: acquiring real-time vehicle multi-source monitoring data; performing in-vehicle unmanned inference on the real-time vehicle multi-source monitoring data, generating an in-vehicle unmanned signal, and entering a vehicle unmanned mode; when the vehicle is in the vehicle unmanned mode, obtaining vehicle component voltage state data; performing minimum-voltage continuous power supply based on the vehicle component voltage state data, and acquiring vehicle real-time anti-theft state data; and vehicle starting voltage real-time monitoring is carried out according to the vehicle real-time anti-theft state data, sudden change recognition is carried out, and starting voltage sudden change data are extracted. Through multi-source data analysis, when it is monitored that the vehicle is stolen, battery output is cut off, power is continuously supplied in other time, the safety anti-theft performance of the vehicle is improved, and daily vehicle data are protected.

The invention discloses a surgical operation assessment method and system based on multi-modal characterization and causal reasoning, relates to the technical field of artificial intelligence, and deeply studies surgical skill analysis and assessment based on deep learning on a common surgical scene. From two perspectives of data and an algorithm framework, two core contents are provided to improve the evaluation performance of surgical skills. Firstly, several new technologies are developed, and observation data, including videos, tracks and languages, of three different modes can be provided in a common minimally invasive surgery. On the basis, a cross-modal contrast learning strategy is designed, and discriminative features can be learned from data of three different modalities. And secondly, a unified framework for performing operation action recognition and skill evaluation in a combined manner is provided, and a prediction structure based on causal reasoning is designed to perform modeling on the causal relationship between the two tasks, so that action recognition and skill evaluation with higher performance are realized.

The invention provides a slope monitoring system based on a physical model, and relates to the technical field of slope physical test analysis, and the IPC classification number belongs to G01N. According to the slope monitoring system based on the physical model, the monitoring and evaluation capability of a risk area is remarkably improved by measuring soil types, geological material characteristics and plant root system state data included in ground materials; the system combines historical landslide fault data and risk area division, can dynamically generate a warning threshold value, and ensures real-time prediction of slope instability. In addition, aiming at comprehensive analysis of the plant root system state, the system can quantify the influence of the root system on the slope stability by generating a root system evaluation index; the threshold calibration unit provides a dynamic warning threshold adjustment strategy to ensure that the stable state of the slope is reflected in real time; meanwhile, the system can achieve more accurate risk assessment by generating an unstable landslide evaluation index, thereby improving the efficiency and reliability of slope monitoring.

The invention relates to the technical field of network security, and discloses a virus monitoring and early warning method based on network traffic deep analysis, which comprises the following steps: collecting network traffic data in real time through a deep packet inspection technology, extracting traffic features, and constructing a traffic feature sequence based on a time window; establishing an anomaly detection model based on the traffic feature sequence, and identifying an anomaly mode deviating from normal behavior distribution; according to an abnormal behavior detection result, constructing a dynamic model of an equipment infection state, and quantifying an equipment infection risk; and constructing a propagation graph model in combination with an equipment interaction relationship, and simulating a diffusion path and a propagation trend of the virus in the network based on propagation dynamics. According to the method, hidden behaviors of unknown viruses and variant viruses can be accurately monitored, a virus propagation path and an influence range are dynamically predicted, optimal allocation of defense resources is realized in combination with a game theory and linear programming, and the real-time performance of virus monitoring and defense is improved.

The invention relates to the technical field of structure safety detection, and discloses a digital twinning-based port wharf structure safety evaluation system and method, and the system comprises a model building module which is configured to build a digital twinning model according to the actual structure of a port wharf; the sensor assembly is arranged in a to-be-monitored area of the port wharf structure; the acquisition module is configured to acquire structure information and environment information; the structure evaluation module is configured to calculate a structure evaluation initial value through the pressure value and the vibration value when the structure is loaded, and adjust the structure evaluation initial value according to the inclination angle influence value to obtain a structure evaluation final value; the environment evaluation module is configured to calculate an environment evaluation initial value through the water level value and adjust the environment evaluation initial value according to the wind power influence value to obtain an environment evaluation final value; and the grade evaluation module is configured to divide the structure safety evaluation grade according to the structure safety evaluation value. According to the invention, the structure safety monitoring and evaluation capability is improved, and the evaluation precision and efficiency are high.

The present invention relates to the technical field of pipeline data processing and health status assessment, and discloses a pipeline internal detection method and system based on multi-sensor fusion. The method includes: calculating the health index of pipeline status assessment data to obtain pipeline health status indicators, performing risk prediction on pipeline health status indicators to obtain risk levels; performing pipeline defect feature identification to obtain defect feature data, performing multi-category classification processing on defect feature data to obtain defect type data; performing precise position analysis on defect type data to obtain defect spatial distribution data and perform structural integrity assessment to obtain defect severity quantification data set; performing multi-criteria decision analysis on defect severity quantification data set and risk level to obtain defect repair priority ranking data, performing intelligent repair plan generation on defect repair priority ranking data to obtain repair suggestion data. The present invention can achieve a more comprehensive and accurate assessment of pipeline status.

The invention belongs to the technical field of data analysis, discloses a real-time vibration data acquisition and analysis method and a related system, and adopts an LSTM (Long Short Term Memory) neural network to capture time sequence characteristics in vibration signals and reserve time domain information, so that the change of the running state of a generator set can be reflected more accurately. Through the combination of the LSTM neural network and the K-means clustering algorithm, the model can deeply learn complex features of vibration signals and distinguish different modes, thereby improving the recognition precision of abnormal data. The K-means clustering can classify the signals in the data processing process, so that the trained LSTM model is more accurate and effective. The LSTM neural network has the capability of processing long sequence data, can adaptively analyze different vibration state signals in multiple modes, is not limited to the operation state of a specific mode, and thus is suitable for a complex generator set operation environment.

The invention provides a general bypass reconciliation and early warning system based on big data, which belongs to the technical field of information, adopts a layered architecture design and comprises a data acquisition layer, a data processing layer, an intelligent reconciliation layer, an early warning decision-making layer and a user interaction layer. All the layers carry out efficient data exchange and instruction transmission through standardized interfaces, it is ensured that all function modules of the system can work cooperatively, and automatic account checking and abnormal early warning of cross-system and cross-platform transaction data are achieved.

The invention relates to the technical field of knowledge maps, and discloses a multi-source information fused knowledge map link prediction method, which comprises the following steps of: training a relational graph converted from a knowledge map by using a graph auto-encoder to obtain entity features and relational features with higher quality, and filtering low-correlation neighborhood entities from a semantic angle and a spatial angle; then, soft prompting is applied to encoding of semantic features, hard prompting is applied to encoding of high-quality path features, and joint training is carried out; performing preprocessing and feature extraction on scores corresponding to the structure modeling model and the text modeling model, and performing weighted summation on candidate entities through weights; and when a specific link prediction task is executed, sorting the obtained scores, and completing the link prediction task according to a score sorting result. According to the method, the over-smoothing problem of an existing structural modeling model is relieved, the calculation cost is reduced, meanwhile, entity fuzziness and space limitation of an existing text modeling model are made up, and a knowledge graph link prediction task can be better completed.

The invention discloses a real-time monitoring system for a power state of an intelligent socket, and relates to the technical field of socket monitoring, a region monitoring module generates an abnormal score for each use region and analyzes whether the use region needs to be stopped based on the abnormal score, a calculation module generates a weight for each sub-region after obtaining historical data of each sub-region, and the weight of each sub-region is calculated; performing weighted calculation on the abnormal scores of all the sub-regions to obtain the overall abnormal coefficient of the socket, analyzing the circuit operation score of the socket, and predicting the operation state development trend of the socket after the overall abnormal coefficient of the socket and the circuit operation score are comprehensively analyzed by a socket operation prediction module. And a corresponding management strategy is generated in combination with the current operation state of the socket. The monitoring system integrates the overall abnormal coefficient and the real-time circuit operation score, analyzes the development trend of the operation state of the socket, predicts the potential fault or abnormal state in advance, and effectively improves the evaluation capability of the system on the overall safety and stability of the socket, thereby better guaranteeing the safe operation of the socket.

The application discloses a kind of campus safety early warning method based on multi-modal fusion and AI vision, it is related to computer vision and intelligent security technology field, comprising the following steps: S1, access control passage perception dataset is constructed;S2, based on access control passage perception dataset identification passage object entering order and passage behavior characteristics;S3, facing relationship expression, in combination with gate occupancy state and authorized behavior record, matching relationship discrimination is carried out;S4, according to gate occupancy state and accompanying behavior characteristics, risk-driven evaluation is carried out;S5, according to risk response condition, linkage control behavior is executed and passage process record and strategy adjustment are completed.The problem that the same direction accompanying borrow line under the passage peak is not associated with the opening and closing rhythm of access control, leading to the problem that the trailing mixed in is difficult to identify in advance is solved.

The invention discloses an AI-based spoken language dialogue quality evaluation method, and relates to the technical field of artificial intelligence and natural language processing, and the method comprises the steps: receiving a spoken language dialogue audio stream, extracting text content information and acoustic rhythm information, carrying out the correlation fusion according to a timestamp, and generating a multi-mode dialogue data sequence; carrying out conjoint analysis on the dialogue logic relation graph and the dynamic memory bank, and calculating a dialogue structure consistency index to obtain a comprehensive quality evaluation score; and based on the comprehensive quality evaluation score and the dynamic memory library, positioning contradictory nodes and contexts in the dialogue logic relation graph, constructing a local consistency reconstruction task, and updating the memory enhancement neural network by using the local consistency reconstruction task. According to the method, the memory enhancement neural network is updated by using the local consistency reconstruction task, adaptive learning and sustainable evolution of the memory enhancement neural network based on actual dialogue contradictions are realized, and the consistency detection accuracy and the dynamic ability of adaptability are improved.

The application relates to the technical field of chemical exploration, in particular to a gradient detection method and system based on geochemical exploration technology. The method comprises the following steps: sampling a to-be-explored area to obtain sampling point information and a number; setting an abnormal flag bit through element content after screening the abnormal sampling points, and obtaining abnormal sampling points; presetting local sampling points for the abnormal sampling points, determining local correlation based on the similarity of element content and the stability of local abnormal flag bits; normalizing associated elements, forming a gradient of the abnormal sampling points and the local sampling points, and determining gradient correlation in combination with the spatial distance of the two; calculating a target area probability value through the local correlation and the gradient correlation, and obtaining a gold mine target area thermal map to provide data support for subsequent detection. The application improves the positioning accuracy of the gold mine target area.

The present invention provides a method and system for intelligent evaluation of structural safety during the operation of an undersea tunnel, which relates to the technical field of intelligent evaluation of structural safety of undersea tunnels. First, sensors are arranged at the vault, joints and support positions of the tunnel to collect strain, displacement, acceleration and temperature data; then, historical earthquake data of the area is collected and feature extracted for subsequent machine learning models, and the model is trained with historical earthquake feature vectors, with the maximum acceleration as the output, and a vibration damage index is assigned to each output data based on the expert scoring method; the stress data of the tunnel is obtained by analyzing the strain and temperature change data, and a stress data source is provided for subsequent model construction; the collected data is compared with a preset threshold, the health state probability is calculated and a damage assessment model is trained; finally, the relevant data is analyzed to generate a structural safety index of the undersea tunnel to judge the current operating state. This method aims to provide a scientific and effective evaluation basis for the safe operation of undersea tunnels.

The application discloses a kind of based on multi-task depth knowledge tracking method and device, wherein the method comprises: obtaining the question representation, the knowledge point representation and the answer correctness result of all time before current time;Input the question representation, the knowledge point representation and the answer correctness result to the knowledge tracking model after training for processing;And, in response to the processing of the knowledge tracking model, obtain the knowledge point answer correctness result of next time;Wherein, the knowledge tracking model is based on the specified knowledge tracking network, including the first auxiliary task component for predicting the knowledge point corresponding to the question of current time t and the basic knowledge tracking task component for predicting the knowledge point answer correctness result of next time t+1. The present application scheme simultaneously predicts the knowledge point input by student at current time and predicts the historical overall answer accuracy of student at each time step on the basis of the basic task of predicting the correctness of question to be answered by student, and improves the evaluation effect of doing question.

The invention provides a skeleton parameter estimation method and device based on multi-modal information fusion, and relates to the technical field of medical image analysis, and the method comprises the steps: obtaining the X-ray image data, depth image data, inertial measurement unit sensor data and electromyogram signal data of a human skeleton; performing three-dimensional reconstruction on the human skeleton according to the X-ray image data and the depth image data to obtain three-dimensional reconstruction data; performing data fusion on the X-ray image data, the inertial measurement unit sensor data, the electromyogram signal data and the three-dimensional reconstruction data to obtain multi-modal fusion data; establishing a skeleton parameter estimation model based on the multi-modal fusion data and a mapping relation between skeleton structure features and skeleton parameters; and performing skeleton parameter estimation by using the skeleton parameter estimation model. According to the method, accurate skeleton parameter estimation can be provided in multiple application scenes such as dynamic motion analysis, motion rehabilitation and intelligent wearable equipment.

The present invention relates to the field of civil engineering technology, and specifically to a tunnel concrete detection and analysis method and system, comprising the following steps: based on an image acquisition device, concrete image data of multiple areas of the tunnel are collected and processed, the location and distribution of cracks are identified, and crack location identification results are obtained. In the present invention, image acquisition parameters are adjusted by ambient lighting, and the crack location is accurately identified in combination with edge detection technology, thereby improving the accuracy and efficiency of crack detection, and crack expansion path prediction and structural stability analysis are performed through geometric feature analysis and classification of cracks. The complexity of cracks is evaluated using fractal dimensions to improve the ability to evaluate the impact on tunnel structural stability, and the corrosion process of concrete is accurately identified through data provided by chemical sensors and analysis of concrete structural damage, providing data support for maintenance decisions, and evaluating the durability of concrete in combination with environmental impacts, thereby improving the foresight and effectiveness of tunnel safety management.

This invention relates to the fields of data processing and remote sensing information processing technology, specifically to a remote sensing carbon source and sink monitoring and assessment method based on a multimodal hybrid expert model. The method includes: feature extraction from multi-source remote sensing data (hyperspectral, visible, and infrared); multimodal fusion of the extracted features; construction of a large-scale multimodal hybrid expert carbon source and sink model using a hybrid expert partitioning sparsity processing mechanism to detect and segment carbon source and sink changes, obtaining a carbon source and sink distribution map; assessment of annual carbon emissions / absorption rates for different types of land cover using a combination of source and sink distribution and ground measurement data; and calculation of regional carbon emissions and absorption based on land cover type area and annual carbon emissions / absorption rates to obtain carbon flux assessment results. This invention can be applied to carbon metering, regional carbon emission inventory compilation, and carbon neutrality effectiveness assessment, providing technical support for achieving the "dual carbon" goal and improving the effectiveness of remote sensing carbon source and sink monitoring and assessment.