485results about How to "Achieve forecast" patented technology

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention discloses an intelligent garden monitoring management method and system, and relates to the technical field of intelligent garden management, and the method comprises the steps: collecting multi-source data through deploying a plurality of sensors, and carrying out the preprocessing of the data; performing feature extraction and multi-modal data fusion on the data based on the preprocessed data; performing time sequence correlation analysis on the fused feature vectors, constructing an intelligent prediction model based on a time sequence analysis result, and predicting the garden environment state; according to the time sequence analysis and prediction result, intelligent decision rules for different environment states are generated; making a specific control strategy based on the intelligent decision rule, and optimizing the control strategy; and according to the optimized control strategy, executing automatic control, introducing a self-adaptive control mechanism, and automatically adjusting the control strategy. Through the innovative technical scheme, intelligent management of the garden environment is achieved, and the growth efficiency of plants and the overall ecological environment quality of the garden are remarkably improved.

The invention provides a storage battery pack performance evaluation method and system based on big data, and relates to the technical field of storage battery performance evaluation, and the method comprises the steps: obtaining the charging and discharging data of a to-be-evaluated storage battery under different working conditions, carrying out the denoising processing of the charging and discharging data, carrying out the adaptive normalization processing, and obtaining the standard charging and discharging data, equalization processing is carried out, time domain features and frequency domain features are extracted respectively, and high-dimensional feature vectors are obtained through combination; performing dimension reduction operation on the high-dimensional feature vector to obtain low-dimensional embedded representation, generating an initial data set, dividing the initial data set into a training data set and a test data set, constructing an initial prediction model and performing hyper-parameter optimization to obtain a first prediction model; and acquiring real-time charging and discharging data, constructing a real-time feature vector, adding the real-time feature vector into the first prediction model to generate a real-time performance parameter, filtering the real-time performance parameter, constructing a health state prediction model for prediction, and obtaining a comprehensive performance evaluation result.

The invention relates to the field of production fault analysis, in particular to a fault monitoring analysis method and system for lubricating oil production. The method comprises the following steps: acquiring a multi-dimensional working monitoring parameter and an omnibearing working image; performing abnormal outlier filtering processing on the multi-dimensional working monitoring parameters, and performing time sequence evolution simulation to generate time sequence state evolution data; performing continuous frame optical flow motion analysis on the omnibearing working image, performing dynamic parameter mapping based on time sequence state evolution data, and constructing a dynamic production twinborn model; obtaining a preset production standard demand log; carrying out dynamic process deviation detection based on a preset production standard demand log dynamic production twinborn model, and carrying out production fault analysis so as to obtain stage production fault data; performing potential fault traceability speculation on the staged production fault data to obtain an abnormal fault potential node; and constructing a potential fault mark twinborn model based on the abnormal fault potential nodes. According to the invention, efficient and accurate fault analysis of lubricating oil production is realized.

The invention discloses a multi-scale hydrodynamic digital twin model based on a coupling algorithm, which ensures the accuracy and consistency of the model on the overall and local levels through effective information exchange and integration among different scales. The consistency of macroscopic and microscopic variables is ensured on a boundary layer through bridging coupling of different scale models and a Lagrangian multiplier method, and physical behaviors of a complex system are simulated more accurately. Besides, through parameter estimation and data fusion, the model can integrate and utilize real-time data, so that dynamic optimization and prediction are realized, and the practicability and flexibility of the model are greatly enhanced. Therefore, the construction of the multi-scale digital twinborn model not only enhances the description capability of the model to a complex system, but also improves the adaptability and prediction precision of the model in practical application, and provides a more efficient and dynamic method for accurately simulating and predicting behaviors of the complex system.

The invention relates to an urban inland inundation water depth prediction method considering geographical similarity, and aims to introduce a geographical similarity theory into urban inland inundation modeling and analyze a geographical modeling mechanism contained in an urban inland inundation process under a rainfall scenario. Constructing a geographic configuration data set; preprocessing data, and constructing a geographic similarity calculation unit; based on the raster data, a hydrological hydrodynamic model is adopted to simulate the earth surface confluence production process of the research area, and an urban inland inundation water depth data set is constructed; based on the urban inland inundation data set and a geographic similarity calculation unit, an automatic machine learning model library is adopted to construct a geographic similarity model; performing hyper-parameter optimization, and optimizing the geographic similarity model; and calculating the predicted water depth. The method has the beneficial effects that the geographic factors are integrated, the geographic similarity theory is introduced into urban inland inundation modeling, and the accuracy and timeliness of an urban inland inundation simulation result are improved through automatic machine learning.

The invention belongs to the field of disaster early warning, and provides a disaster early warning method and system based on key target detection and image segmentation, and the method comprises the steps: obtaining the real-time image data of a site, and carrying out the preprocessing of an image; processing the preprocessed image through a YOLOv5 target detection algorithm, detecting a key target in a disaster site, and outputting a category label and a bounding box of the target; performing pixel-by-pixel segmentation processing on a bounding box region output by target detection by using a U-Net image segmentation algorithm to generate an accurate segmentation mask of the target; and inputting the image segmentation mask, historical disaster data, meteorological data and geographic information system data into an LSTM prediction model, carrying out multi-source data fusion and time sequence analysis, and outputting dynamic prediction information and early warning levels of disasters. According to the invention, real-time monitoring and early warning of disasters are realized, the response speed of the early warning system is obviously improved, more comprehensive disaster early warning information is provided, high precision of disaster prediction is ensured, and the phenomena of false alarm and missing alarm are reduced.

The invention discloses an underground personnel unsafe behavior rapid analysis and recognition method based on machine vision, and relates to the technical field of intelligent safety monitoring, and the method comprises the steps: configuring an environment sensing unit to monitor underground environment parameters in real time, and dynamically adjusting the threshold parameter of an image recognition and posture estimation model according to the monitoring data; a behavior prediction model is constructed, the behavior prediction model is input based on the time sequence data to predict the future unsafe behavior trend of the underground personnel, and early warning is achieved; visual, sound and other non-visual sensor data are integrated, a multi-modal fusion recognition system is constructed, and the recognition capability in a complex environment is improved through synchronous processing and fusion of modal data; the real-time behaviors of the underground personnel are comprehensively analyzed, and once unsafe behaviors are recognized, an early warning mechanism is started immediately; model parameters are dynamically adjusted through the environment sensing unit, and the adaptability of the system to environment changes is enhanced.

The invention provides a machine learning water quality monitoring model based on a random forest algorithm. The machine learning water quality monitoring model comprises the following steps: A, preprocessing water quality monitoring data; b, selecting features related to a water quality monitoring target from the preprocessed data; c, constructing a random forest model; d, training the constructed random forest model; e, predicting the trained random forest model; f, evaluating the predicted random forest model; g, parameters of the random forest model are adjusted according to the evaluation result for optimization, through historical data training, the model can automatically recognize and predict water quality abnormal events, and pollution can be found and prevented as soon as possible. The random forest algorithm can train a plurality of decision trees at the same time to form a forest, and random sampling and random feature selection are performed on a data set, so that the accuracy and robustness of the model are improved.

The invention discloses a new energy automobile charging cable aging performance test method and system, and relates to the technical field of cable aging detection, when the system operates, through comprehensive application of an environment acquisition module, a data processing module, a performance test module and an evaluation module, accurate evaluation and prediction of the cable aging performance are realized, and the cable aging performance is accurately evaluated and predicted. The environment acquisition module collects cable test data under temperature control, humidity control and water immersion conditions in real time, and the data processing module performs cleaning, standardization and feature extraction on the data to form a high-quality feature data set X. And the performance test module performs multi-model prediction by using a random forest, a gradient boosting tree and a deep neural network model, performs fitting through a meta-model, obtains a prediction result # imgabs0 # of a Stacking model, matches the prediction result # imgabs0 # with a preset evaluation threshold L, generates a cable aging performance test evaluation scheme, and performs cable aging performance test evaluation. The problems that a traditional method is slow in response, low in prediction precision and poor in adaptability are solved.

The invention discloses a milk traceability method based on NFC, relates to the technical field of milk traceability, and comprises the following steps: combining an NFC tag with a block chain, performing encryption transmission on data of each link, storing through a distributed account book technology of the block chain, ensuring that the data is transparent and cannot be tampered, and based on the NFC tag, obtaining a milk traceability result; the monitoring data of each stage of milk production, processing and transportation is updated to the block chain, the traceability efficiency is significantly improved, manual participation is reduced, a deep learning model is introduced, prediction and identification of potential quality problems of milk are realized, enterprises are helped to take corrective measures in advance, loss is reduced, a risk early warning mechanism is provided for a supervision mechanism, and the risk of the supervision mechanism is reduced. The overall safety of the supply chain is improved, the consumer can check the complete traceability information of the product by using the NFC scanning device through the NFC label and the block chain technology, the sense of trust of the consumer to the product is greatly improved, and the credibility of the brand can be enhanced.

The invention provides a time sequence anomaly detection method based on time-frequency fusion, and belongs to the field of time sequence data anomaly detection. The invention provides a time sequence anomaly detection method based on time-frequency fusion aiming at the problems that potential correlation exists among variables in multivariable time sequence data and frequency domain characteristics are not considered in a traditional method. According to the method, time domain data and frequency domain data in two different modes are innovatively calculated through Spearman correlation coefficients to obtain a time domain similarity matrix and a frequency domain similarity matrix, then fusion is carried out in a point multiplication mode, and potential relations between different variables are mined by using a graph neural network. The time sequence diagram neural network is used for capturing the time dependence and the space relation at the same time, and the features of the time sequence data can be better extracted. The network model is applied to anomaly detection of multivariable time series data, the purpose of improving the accuracy rate can be achieved, and the effectiveness of the method is verified through experimental results.

The invention discloses a carbon emission prediction method. The method comprises the following steps: acquiring carbon emission data information of a target area; performing carbon emission data accounting of the target area based on the carbon emission offset data; constructing a carbon emission prediction model of the target area; and performing carbon emission prediction of the target area based on the set scene information by adopting the carbon emission prediction model. The invention also discloses a system for realizing the carbon emission prediction method. According to the carbon emission prediction method and system provided by the invention, through the acquisition of the historical data, the accounting of the carbon emission data and the construction and evaluation of the prediction model, the prediction of the carbon emission of the target object in the whole life cycle is realized, and the reliability is higher and the accuracy is better.

The invention discloses a single air conditioner load regulation potential evaluation method based on transfer learning and multi-task learning, and relates to the technical field of air conditioner management.The method comprises the steps that a regional association model is built based on air conditioner operation data in a general scene, and the regional association model is analyzed to obtain a cooperative regulation relation between air conditioner equipment; constructing an air conditioner load regulation potential evaluation model by using a deep residual neural network; a multi-task learning framework is formulated according to the cooperative adjustment relation between the air conditioning equipment, and the air conditioning load adjustment potential evaluation model in the general scene is optimized by using the multi-task learning framework; and adjusting the weight of the optimized air conditioner load regulation potential evaluation model in combination with a transfer learning technology to obtain an air conditioner load regulation potential evaluation model in a specific scene. According to the method, the adaptability and generalization ability of the air conditioner load regulation potential evaluation model are remarkably improved by constructing the regional correlation model and introducing self-supervised learning and adversarial training.

The invention discloses an optimal control method and system for beer fermentation, and relates to the technical field of beer fermentation, and the method specifically comprises the following steps: obtaining the gas exchange information of each region at the top of a fermentation tank in real time, analyzing the information after obtaining, predicting the obstruction degree of a bubble layer formed in each region at the top of the fermentation tank to oxygen, and determining whether the oxygen is obstructed or not; dividing each area at the top of the fermentation tank into a high-barrier area, a medium-barrier area and a low-barrier area according to the prediction result; on the basis of the division result of each region, constructing a bubble suppression and oxygen regulation mechanism, and respectively carrying out different oxygen supply and bubble dissipation regulation on the high-barrier region, the medium-barrier region and the low-barrier region; and in the process of adjusting each region by a bubble suppression and oxygen adjustment mechanism, acquiring gas exchange feedback information of each region in real time, and analyzing the acquired gas exchange feedback information. The problem that a bubble layer hinders oxygen penetration is solved, the fermentation efficiency is improved, and the beer quality is guaranteed.

The invention relates to a method, equipment and medium for determining actual operation characteristics of a frequency converter unit, the frequency converter unit is applied to a water pump system, and the method comprises the steps of obtaining operation data of the frequency converter unit and historical frequency data of a water pump; based on the operation data, identifying a plurality of working condition modes through a clustering algorithm to obtain corresponding working condition data; for at least part of the working condition modes in the plurality of working condition modes, converting the corresponding working condition data into standard working condition data under a standard frequency based on the historical frequency data; aiming at the standard working condition data, performing optimization calculation on the operation characteristic curve of the frequency conversion unit in a preset rule by utilizing an optimization algorithm so as to calculate an actual operation characteristic curve of the frequency conversion unit; and obtaining the actual operation characteristics of the frequency conversion unit based on the actual operation characteristic curve. According to the invention, on-line monitoring of the operation characteristics of the frequency converter unit in actual work is realized, the monitoring is real and convenient, and an early warning effect on the operation risk and the degradation risk of the frequency converter unit is realized.

The invention provides an overhead line galloping early warning system and method based on a machine learning model, and relates to the technical field of electric power, and the method comprises the steps: setting a plurality of monitoring nodes on an overhead line; obtaining a weight coefficient of each node according to the galloping data of each node; obtaining a first risk of each node in a preset time according to the galloping data of each node and the corresponding environmental parameters; establishing a line dynamic prediction model through the weight coefficient of each node and the environment parameter of each node; predicting a line galloping risk in a preset time according to the line dynamic prediction model; and giving out galloping early warning and providing decision support according to a prediction result, the prediction result comprising a first risk and a line galloping risk, and obtaining a galloping early warning by introducing a weight distribution mechanism, establishing a dynamic prediction model, optimizing preset time setting, perfecting an early warning mechanism and other measures. And real-time monitoring, accurate prediction and timely early warning of the galloping risk of the overhead line are realized.

The invention discloses a method, a system and equipment for simulating a customer approval result based on historical data learning. The method comprises the steps of user data collection and data preprocessing; constructing and training a model; model evaluation: performing prediction accuracy evaluation on the trained model by using an independent test data set, and if an evaluation result is not good, adjusting and optimizing model parameters until the evaluation result meets requirements; adjusting a marketing strategy according to a simulation result; applying the strategy to a marketing operation scene, and applying the adjusted marketing strategy to actual marketing operation; effect monitoring and iterative optimization are carried out, feedback and iterative optimization are carried out on the model, the marketing strategy or the whole process according to the monitoring result, and the prediction accuracy and the marketing effect are continuously improved. According to the method, intelligent simulation and prediction of a complex approval process are realized, the accuracy and reliability of an approval result are greatly improved, a more personalized marketing strategy and service scheme are formulated, and the marketing effect and customer satisfaction are remarkably improved.

The invention provides an industrial robot safety monitoring method and system, and relates to the technical field of safety monitoring, and the method comprises the steps: arranging a multi-mode sensor array to collect the state information of a robot; performing empirical mode decomposition on the vibration signal to obtain an intrinsic mode function reflecting the abrasion degree of the robot transmission system; performing wavelet transform on the temperature signal to obtain a temperature feature set reflecting the heat dissipation condition of the robot; through knowledge extraction, knowledge representation and knowledge fusion technologies, semantic representation and linking are carried out on various knowledge in robot components, performance parameters, degradation modes and maintenance measures, and a management knowledge graph corresponding to the robot is constructed; and performing reasoning mining on the management knowledge graph based on a rule mining algorithm of path sorting, extracting degradation characteristics corresponding to each component of the robot in the management knowledge graph by mining a reasoning path formed by frequently co-occurring entities and relationships, and predicting a health state trend of the robot in combination with a pre-constructed time sequence prediction model.

The invention relates to the technical field of wind resource assessment and wind power plant optimization, and discloses a multi-source information fusion measurement system combining laser radar data and wind resource assessment, and the system comprises a data collection module which is used for collecting laser radar data, synthetic aperture radar data, numerical weather forecast data and ground weather station data; the data preprocessing module is used for carrying out noise filtering, time alignment, space alignment and data calibration on the collected data; the information fusion module is used for dynamically fusing the preprocessed multi-source data and outputting high-precision wind speed and turbulence characteristic distribution; and the wind field modeling and prediction module is used for constructing a dynamic three-dimensional wind field based on the fusion data and performing short-time wind speed prediction. Through multi-source data deep fusion, dynamic wind field modeling and intelligent optimization modules, the comprehensive effects of high-precision wind field evaluation, nonlinear wind speed prediction and fan layout global optimization are achieved.

The invention discloses a CIM-based pipeline state intelligent monitoring method, system and equipment and a medium, belongs to the technical field of urban infrastructure management, and aims to solve the technical problem of how to realize comprehensive, real-time and intelligent monitoring and management of the pipeline state of an underground pipe network, automatically analyze potential risks and abnormities and improve the management efficiency of the underground pipe network. In order to improve the management efficiency and level of the pipeline state of the underground pipe network, the adopted technical scheme is as follows: constructing a data acquisition and transmission network: constructing the data acquisition and transmission network by deploying multiple types of sensors along the pipeline, and obtaining and transmitting the real-time operation data of the pipeline; preprocessing data and constructing a model: preprocessing the pipeline operation data, and constructing a precise three-dimensional model of the pipeline by using a CIM (Common Information Model) technology; and intelligent analysis and prediction: in combination with big data analysis and data mining technologies, deep analysis and prediction of pipeline operation data are realized, and potential risks are identified and early warned.

The invention relates to the field of mechanical and electrical product abrasion testing, and provides an online simulation method and system for mechanical and electrical product abrasion testing, and the method comprises the following steps: collecting historical multi-dimensional monitoring data in the operation process of a mechanical and electrical product from a historical database; establishing an initial mechanical and electrical product wear prediction model based on a Bayesian network and a convolutional neural network; training and testing an initial mechanical and electrical product wear prediction model through the historical multi-dimensional monitoring data to obtain a mechanical and electrical product wear online prediction model; and collecting electromechanical product operation multi-dimensional monitoring data in the electromechanical product operation process in real time, inputting the electromechanical product operation multi-dimensional monitoring data into the electromechanical product wear online prediction model, and outputting a wear prediction result of the electromechanical product. According to the method, historical multi-dimensional monitoring data is collected, the Bayesian network and the convolutional neural network are combined to establish an initial mechanical and electrical product wear prediction model, and online wear prediction is realized after the model is trained and tested, so that the wear condition of the mechanical and electrical product is monitored and predicted in real time, and the wear test accuracy and efficiency are improved.

The invention provides a river flow estimation method for deducing a river channel open area by using a height measurement satellite, which comprises the following steps of: extracting and preprocessing an optical image river width, preprocessing a radar image and extracting the river width; extracting a height measurement satellite preprocessing water level; inverting the river topography by the elevation boundary; inverting a riverway terrain by a remote sensing image; constructing a riverway terrain; and carrying out river flow inversion. According to the river flow estimation method for deducing the river channel open area by using the height measurement satellite, river flow prediction is realized by extracting river width and water level inversion river channel topography based on multi-source remote sensing satellite data and establishing a cross section-flow empirical formula.

The invention discloses a ship seawater cooling system parameter prediction method based on an HHWOA-TCN-BiGRU model, and the method comprises the steps: obtaining historical data of ship seawater cooling system state parameters with a time sequence, and carrying out the normalization processing, and obtaining input data; according to a TCN model and a BiGRU network model, constructing a TCN-BiGRU model for performing feature extraction and mapping on the input data to obtain prediction data; presetting a chaotic mapping sequence, constructing a self-adaptive weight strategy and a nonlinear convergence factor, and improving the whale optimization algorithm by using the chaotic mapping sequence, the nonlinear convergence factor and the self-adaptive weight strategy to obtain an improved whale optimization algorithm; performing parameter optimization on the number of neurons of the model and convolution kernel parameters by using an improved whale optimization algorithm to obtain an optimized TCN-BiGRU model, namely, an HHWOA-TCN-BiGRU model; the input data are input into the HHWOA-TCN-BiGRU model, and predicted data are obtained; the method can adapt to complex data characteristics, and can predict the parameters of the ship cooling water system in a short period in the future according to historical data.

The invention relates to the technical field of exception management, in particular to a digital twinning-based multi-level alarm management system and method, and the system comprises an information collection unit which is used for collecting the monitoring information of a target plant area; the digital twinborn body construction unit is used for performing bidirectional mapping on the geometric model, the physical model and the intelligent prediction model of the target plant area and constructing a digital twinborn body of the target plant area; the grid division unit is used for performing grid division on the digital twinborn body and establishing a position relation mapping table between the position of each object in the actual target factory area and the position of each object in the digital twinborn body; and the abnormality verification unit is used for judging the abnormal position of the actual target factory according to the abnormal information and the position of the grid where the abnormality occurs in the position relation mapping table, and performing verification according to the abnormal information and the real scene image where the abnormal position is located. According to the invention, the abnormal condition of the target factory can be monitored.

The invention provides a vegetation prediction model construction method, a vegetation prediction method, a vegetation prediction device and vegetation prediction equipment, and relates to the technical field of data processing. The vegetation prediction model construction method comprises the following steps: taking ecological environment data of a target area as an independent variable, taking vegetation spatial distribution data corresponding to the target area as a dependent variable, and training a first random forest model to obtain a vegetation spatial distribution prediction model; taking the output of the vegetation parameter spatial distribution prediction model and the period-by-period meteorological data as independent variables, taking the period-by-period vegetation feature change data as dependent variables, training a second random forest model, and obtaining a vegetation feature time change prediction model; and splicing the vegetation spatial distribution prediction model and the vegetation feature time change prediction model to obtain a vegetation prediction model. According to the technical scheme, the data utilization efficiency can be improved, the model training time is shortened, and meanwhile the adaptability of the vegetation prediction model and the accuracy of vegetation prediction are improved.

The invention provides an Elasticsearch cluster fault intelligent detection system, which relates to the technical field of big data storage and retrieval, and comprises a data acquisition module, an Elasticsearch cluster fault analysis module, an expert system module and a model optimization and self-learning module, the current log data and the monitoring index data of the Elasticsearch cluster are analyzed through a fault detection model of the Elasticsearch cluster fault analysis module, the prediction fault type of the Elasticsearch cluster is determined, and automatic detection and prediction are achieved; a deep learning algorithm and an expert rule base are fused, so that complex and dynamic fault scenes can be efficiently processed, the false report rate and the missing report rate are reduced, and the detection accuracy is improved; and through the model optimization and self-learning module, self-learning and dynamic optimization of the fault detection model are realized, and the operation and maintenance complexity and cost are reduced.

The invention relates to the technical field of SoC main control chips, and discloses a deep sleep wake-up control method and system for a multimode SoC main control chip. The method comprises the following steps: acquiring voltage and current data and temperature distribution data of each functional module of a multi-mode SoC main control chip under different environments and operation conditions EOC to obtain a chip monitoring data set; constructing a functional module topological graph and mapping the functional module topological graph into potential features to obtain deep sleep state criteria of each functional module; establishing a hierarchical Bayesian model, and identifying the state transition probability and EOC influence factors of each functional module to obtain a combination scheme of the deep sleep functional modules; determining a collaborative sleep control sequence; a hierarchical Bayesian model is input, a wake-up voltage and a clock sequence are generated, power consumption response of each function module is monitored in real time through a dynamic graph neural network, a safe wake-up control result is output, mutual interference among the function modules is reduced, and the overall power consumption management efficiency of a chip is improved.

The invention relates to the technical field of environmental science and information, in particular to a rural land space carbon emission assessment method, which collects real-time data through a distributed sensor network and an unmanned aerial vehicle remote sensing technology, analyzes and extracts key features by using an adaptive variational auto-encoder and topological data, and effectively identifies a land utilization mode. The graph neural network and the Bayesian optimization generative adversarial network are combined to dynamically evaluate and predict carbon emission, and uncertainty quantitative data are generated; a multi-objective genetic algorithm and a game theory model are used for optimizing land utilization layout, an optimization scheme is executed on a block chain through an intelligent contract and a multi-agent system, and automatic management and dynamic adjustment are achieved; according to the method, the accuracy and the real-time performance of rural land carbon emission evaluation are remarkably improved, and a scientific basis is provided for low-carbon sustainable development.