235results about How to "Effective prediction" patented technology

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention discloses an electric power optical communication network alarm analysis method and system based on a time-space diagram neural network. The method comprises the following steps: collecting data of an electric power optical communication network, preprocessing the collected data, and modeling the preprocessed data into a graph structure; extracting and learning the features of the nodes in the graph structure by using a space-time graph neural network, and generating space-time embedded representation of the nodes in combination with space feature extraction and time feature extraction; classifying the nodes based on the spatio-temporal embedding representation of the nodes to obtain prediction labels of devices corresponding to the nodes; and according to the prediction label of the equipment corresponding to the node, carrying out analysis and influence evaluation on the alarm condition in the power optical communication network. According to the method, the electric power optical communication network is represented as the graph structure containing the space-time information, the space-time dependence of the electric power optical communication network can be effectively captured based on the space-time graph neural network, the space relationship in the graph structure and the dynamic change in the time sequence are processed at the same time, and the accuracy and timeliness of alarm analysis are improved.

The invention discloses a thermal deformation prediction and optimization method and system in metal plastic composite product processing, and the method comprises the steps: building a composite product geometric model according to the material characteristics and preset boundary conditions of a composite product; according to the geometric model of the composite product, a finite element analysis method is used, a preset time step length and a nonlinear solver are selected for coupling solving of a thermal field and a mechanical field; the change data of the temperature field and the stress field along with time and thermomechanical circulation are analyzed through simulation, accumulated deformation and interface stress data of the material in circulation of the preset number of times are monitored, and a simulation prediction result is generated; and according to the simulation prediction result, the heating and cooling rate and the mold loading path are optimized. The thermal deformation prediction and optimization method in metal plastic composite product processing can be used for solving based on multi-physics field coupling in combination with a nonlinear finite element.

The invention relates to the technical field of geological disaster prediction, in particular to a field geological exploration monitoring system based on remote sensing data, which comprises a data acquisition module, a data preprocessing module, a multi-source data fusion module, a deep learning model module and an early warning module. The data acquisition module, the data preprocessing module, the multi-source data fusion module, the deep learning model module and the early warning module are connected in sequence. Through sequential connection of the modules, the system realizes closed loop of data from acquisition, analysis and early warning, and ensures integrity, real-time performance and accuracy of the data. The data preprocessing module eliminates the interference of source data while ensuring the data quality, and the fusion module improves the synergistic effect of multi-source data through an intelligent algorithm. The high-precision prediction of the deep learning model module ensures the timeliness and effectiveness of disaster early warning. The early warning module responds in time to help related departments to do disaster prevention and reduction work, and risks caused by geological disasters are effectively reduced.

The invention provides a full face tunnel boring machine tunnel surrounding rock strength intelligent prediction model training method and equipment. A model capable of effectively predicting coal mine tunnel TBM tunneling tunnel surrounding rock point load strength can be trained. The method comprises the following steps: acquiring first original data corresponding to a target heading machine; removing data of the target heading machine in a stop section and an empty pushing section in the first original data to obtain second original data; processing an abnormal value in the second original data to obtain target operation data; feature parameters in the target operation data are extracted; performing normalization processing on the feature parameters to obtain a model training data set; a Stacking algorithm model is constructed according to a base learner and a meta learner, the base learner comprises a plurality of regression trees, and the output result of the base learner is used as the input of the meta learner; and training the Stacking algorithm model based on the model training data set so as to obtain the prediction model for the surrounding rock strength of the tunneling roadway of the full-face tunneling machine.

The invention relates to the technical field of textile printing and dyeing setting machines, and provides a data acquisition method and system for a temperature controller of a setting machine. The method comprises the following specific implementation steps: firstly, calculating an initial first temperature sampling frequency in a preheating stage through an environment temperature and a target temperature; then, calculating the temperature amplification in the temperature control area, predicting a temperature amplification vector at the next sampling moment in combination with a temperature state estimation equation, and controlling a first temperature sampling frequency in real time through a temperature sampling adaptive controller; thirdly, the initial second temperature sampling frequency of the shaping stage is calculated, and the second temperature sampling frequency is automatically adjusted according to the temperature change of the temperature control area and the heating power change rate through a self-adaptive fuzzy controller; and finally, acquiring temperature data in the temperature control area according to the temperature acquisition frequency controlled in real time, and judging whether to send out early warning or not. According to the invention, the sampling frequency can be dynamically adjusted according to the real-time temperature data in the preheating and setting stages of the setting machine, so that an efficient and accurate temperature control process is realized.

The invention provides a boiler master control system, a pressure adjusting method, electronic equipment and a storage medium. The system comprises a signal acquisition module, a differential control module, a pressure deviation adjusting loop and an instruction dynamic compensation loop. Compared with the prior art, according to the embodiment of the invention, the differential action intensity is calculated by utilizing the load change signal, the control valve action signal and the main steam pressure signal, so that the first coal quantity adjustment signal is accurately generated. Load change and pressure fluctuation can be responded in time, it is ensured that the coal quantity is adjusted more accurately, and boiler efficiency fluctuation or fuel waste caused by too much or too little coal quantity is avoided. The pressure deviation adjusting loop performs adjustment based on the deviation of the main steam pressure signal, so that the stability of coal quantity control is further improved; the instruction dynamic compensation loop can effectively predict and adjust the coal quantity according to the dynamic change of the boiler load, the parameters of the unit heat energy storage and the boiler capacity input rate so as to adapt to the dynamic change of the boiler load and capacity.

The invention discloses a short-time traffic flow prediction method and system based on a multi-feature map, and relates to the technical field of traffic flow prediction, and the method comprises the steps: respectively constructing an occupancy rate feature map, an average speed feature map and a traffic flow feature map through three kinds of data, including traffic flow, occupancy rate and average speed data, collected by a road sensor at the same time; node feature information of three feature maps is aggregated by using a GCN, supervised learning training of occupancy features and average speed features is realized by using an improved Transform encoder, and feature interaction fusion of obtained feature vectors of occupancy and average speed is realized in a multi-feature fusion traffic flow prediction model. The occupancy and average speed features are utilized to assist a multi-feature fusion traffic flow prediction model to better capture traffic flow features, and more accurate traffic flow prediction is provided.

The invention discloses a Leymus chinensis drought resistance character related SNP molecular marker and application, the molecular marker is selected from one of SNP1, SNP2, SNP3 and SNP4, a molecular marker combination is formed by combining four markers of SNP1, SNP2, SNP3 and SNP4, the molecular marker combination is C, GC, T and ATG homozygous genotypes according to the sequence of SNP1-SNP4, that is, when the molecular marker combination is CC-GGCC-TT-AATTGG genotype combination, the corresponding Leymus chinensis has the strongest drought resistance. According to the molecular marker related to the drought-resistant character of the leymus chinensis and the combination of the molecular marker, the drought-resistant character of the leymus chinensis can be accurately evaluated at the molecular level, a drought-resistant seed source is accurately and efficiently screened at the seedling stage of the leymus chinensis, and the breeding period of the leymus chinensis is shortened.

The invention discloses a small-scale greenhouse gas emission prediction method based on a pre-training model, and belongs to the field of greenhouse gas emission prediction. According to the method, a greenhouse gas monitoring micro station is arranged in a park, data such as greenhouse gas concentration and meteorological factors are collected in real time, and high-precision traceability of a greenhouse gas emission source and accurate accounting of greenhouse gas emission can be achieved in combination with a high-precision meteorological numerical value mode, a backward trajectory tracking algorithm, a receptor model and a diffusion model; the greenhouse gas emission amount is predicted in real time by calling the pre-training model, and the prediction efficiency and accuracy are remarkably improved. Different from a mode that a traditional model depends on a large amount of historical data training, the pre-training model can be finely adjusted on the basis of a small amount of existing data, so that effective learning and prediction are realized. According to the method, a large amount of time and computing resources are saved, the environmental change can be quickly responded, and real-time and accurate emission prediction is provided.

The invention provides an electrical equipment fault diagnosis early warning system, and relates to the technical field of equipment fault diagnosis, and the system comprises a multi-sensor module which is used for obtaining electrical, thermal, mechanical and electromagnetic parameters of electrical equipment; the acquisition processing module is used for summarizing the acquired various parameters and carrying out preprocessing and feature extraction; the fault diagnosis module is used for analyzing indexes of electrical characteristics, thermal characteristics, mechanical characteristics and electromagnetic characteristics of the electrical equipment, fusing the indexes based on the type of the equipment and diagnosing the fault condition of the equipment; the early warning module is used for generating a fault trend image of the electrical equipment based on the fusion result; the man-machine interaction module is used for generating a corresponding signal based on the fault condition and the trend image; through the reasonable design and the processing mode based on the equipment working type, the method has good adaptability and universality to different electrical equipment and complex working conditions, overcomes the limitation of the prior art, improves the diagnosis accuracy, and provides time for maintenance personnel to avoid fault deterioration.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention discloses a near-field dynamics-based modeling method for an engineering cement-based composite material under an impact load, and the method comprises the steps: constructing an engineering cement-based composite material full-discrete model, and discretizing a cement matrix and fibers in the engineering cement-based composite material full-discrete model into a plurality of space material points; constructing an impact crack propagation model based on near-field dynamics through the plurality of space material points; correcting the impact crack propagation model in combination with a failure criterion of a model strain rate effect to obtain a corrected impact crack propagation model; and analyzing the engineering cement-based composite material under the impact load through the corrected impact crack propagation model. The method can effectively predict and analyze the mechanical behavior and damage problem of the ECC under the impact load.

The invention discloses a depth map prediction method and device based on phase-guided Transform-CNN (Convolutional Neural Network) dual-path fusion. The method comprises the following steps: acquiring a to-be-processed stripe image; frequency characteristics related to phase information are extracted from the fringe image through a Fourier model, and long-distance and global frequency characteristics are acquired by using a Transform model; extracting detail features from the stripe image by using a CNN model; and carrying out cross fusion on the long-distance and global frequency features and the detail features, and carrying out multi-scale feature enhancement to obtain a depth map of the stripe image. The PG-FTCNet can effectively predict phase information on a complex surface, and meanwhile, the prediction precision of the depth map is remarkably improved. According to the method, a single fringe image in fringe projection profilometry serves as input, phase unwrapping and depth map prediction are achieved at the same time, an efficient end-to-end calculation mode is reserved while the result interpretability is improved, and a reliable and practical solution is provided for a three-dimensional reconstruction task of FPP.

According to the pSS prediction model establishment method based on the multiple clinical examination indexes and the pSS prediction system, multiple machine learning methods and feature screening algorithms are integrated, a new data processing and analysis framework is provided, prediction model establishment of pSS patients is achieved, and the prediction efficiency of the pSS patients is improved. And the contribution degree of important features to model decision is revealed.

The invention relates to the technical field of electric power data analysis, in particular to an electric power data analysis method and device based on graph calculation, and the method comprises the steps: constructing an electric power network graph, carrying out the deep learning processing of multi-dimensional and heterogeneous data, and analyzing a dynamic change prediction trend; power dispatching is optimized in combination with reinforcement learning, an optimal power dispatching decision is matched with minimization of energy loss as a target, the technical problems that high-dimensional data and heterogeneous data are complex to process, the change trend of the power system cannot be accurately predicted and an effective dispatching decision is lacked in the operation process of the power system are solved, and the power system can be effectively dispatched according to the real-time power grid operation state and the prediction trend. According to the method, the aim of self-adaptive strategy optimization learning is achieved, power dispatching flexibility is greatly improved, energy loss minimization is taken as the aim, an optimal power dispatching decision is accurately matched through continuous iterative optimization, power system data analysis efficiency is improved, and strategies are adjusted to cope with continuously changing supply and demand conditions and power grid operation conditions; and the overall stability and reliability of the power grid are improved to the greatest extent.

The invention relates to the technical field of artificial intelligence, and provides a CNN-BiGRU model sap flow prediction method based on an attention mechanism, and the method comprises the steps: S1-S3, collecting trunk sap flow data and environment data, decomposing the trunk sap flow data into N intrinsic model functions through a CEEMDAN model, removing the noise in the complex high-dimensional data, and carrying out the prediction of the sap flow through a CNN-BiGRU model. And feature value extraction is carried out after the data normalization process to form a CNN-BiGRU-SA model, the CNN is used for extracting spatial features, the BiGRU captures time sequence features, and the SA enhances attention of the model to important features and correlation mining of global variables. The invention further discloses a system, the method and the system can effectively process and predict the trunk liquid flow, the feature extraction capability in the trunk liquid flow data and the environment data is enhanced, the data processing amount and noise interference in the data processing process are reduced, and the accuracy and the efficiency of model prediction are greatly improved.

The invention relates to the technical field of flood prediction, in particular to a non-consistent regional flood frequency analysis method based on the Bayesian theory. The method comprises the following steps: acquiring flood sequence data, meteorological data and drainage basin characteristic data of hydrological stations in a drainage basin; performing regional flood dynamic change analysis on the flood sequence data and the meteorological data to generate regional flood time sequence dynamic change data; on the basis of the regional flood time sequence dynamic change data, hydrological station flood characteristic parameters in the drainage basin are extracted from the drainage basin characteristic data, and hydrological station flood characteristic parameters in the region are obtained; and performing flood distribution parameter regression coefficient prior information derivation on the flood characteristic parameters of the hydrological stations in the region to obtain a maximum likelihood estimation value of a single hydrological station distribution parameter. According to the method, the Bayesian theory, the spatial correlation analysis and the spectral analysis are introduced, so that the accuracy and the applicability of flood frequency analysis based on the Bayesian theory are improved.

The invention discloses an aromatherapy intelligent margin calculation method based on vehicle body data dynamic perception. The method comprises the following steps: S1, generating an initial data set; s2, inputting the initial data set into a multi-channel variational encoder, and outputting hierarchical feature representation; s3, inputting the hierarchical feature representation into a spatio-temporal context sensing module to generate spatio-temporal feature representation; s4, inputting the spatial-temporal feature representation into a heterogeneous feature mapping layer to generate a unified data representation; s5, inputting the unified data representation into a dynamic prediction regulation and control module, and generating an optimized feature representation; s6, inputting the optimized feature representation into an aromatherapy residual amount prediction model, and performing residual amount prediction by using a Gaussian mixture process; s7, inputting an aromatherapy residue prediction result into a feedback integrator, and carrying out self-supervised learning; and S8, integrating to a vehicle-mounted system, displaying an aromatherapy allowance state and sending a personalized notice. According to the method, accurate prediction and calculation of the vehicle-mounted aromatherapy allowance are realized by utilizing multi-channel variational coding, spatio-temporal context perception, a Gaussian process and the like.

The invention relates to the technical field of software risk assessment, and particularly discloses a software risk assessment system and method based on a stage problem set, and the system comprises a risk analysis module, a risk assessment module, a coping strategy module, a risk tracking module, a decision support module, a data management module, and a user management module. The risk analysis module is used for analyzing risk problems collected in the current development stage and discovering potential risk problems; the risk assessment module is used for carrying out risk assessment according to the identified risk problems and determining the severity and possibility of each risk problem; according to the method, the risk degree of each risk problem is accurately evaluated by optimizing the pseudo-timing algorithm, so that the timeliness and effectiveness of risk response are ensured; the real-time risk tracking and data visualization function enhances decision support, and ensures that project risks are continuously monitored and adjusted, so that the accuracy of risk prediction is improved, and the flexibility and optimization capability of a risk coping strategy are enhanced.

The invention discloses an underwater acoustic time-varying channel prediction method based on kernel recursive least squares, and relates to the technical field of ocean communication, and the method comprises the steps: obtaining channel sample data, and constructing a gain training sample and a time delay training sample; and constructing a multipath gain prediction model and a multipath time delay prediction model of any path p by using a kernel recursion least square method, and training corresponding prediction models. And utilizing the trained corresponding prediction model to predict and obtain a multipath gain prediction value and a multipath time delay prediction value of the prediction moment T according to the historical sample sequences of a plurality of continuous moments before the prediction moment T. And integrating the multipath gain prediction values and the multipath time delay prediction values of all paths at the prediction time T to obtain a channel prediction value of the underwater sound time-varying channel at the prediction time T. According to the method, real-time tracking prediction can be carried out on the underwater sound time-varying channel in a complex and changeable underwater environment, and performance improvement of an underwater sound communication system is facilitated.

The invention discloses a heavy metal contaminated soil ecological risk assessment method, and belongs to the technical field of pollution assessment, and the method specifically comprises the steps: dividing a to-be-detected contaminated region into a plurality of grids, collecting indexes in soil of each grid intersection point, and calculating a pollution risk score of each grid intersection point; interpolation data fitting is carried out in the grids through a radial basis function interpolation method, and a contour map of the pollution risk scoring map is generated; selecting a contour line with the maximum numerical value in the contour map, marking the contour line as an initial contour line, determining an initial connection point, sequentially searching an end point connection point, which is longest from the initial connection point of the current contour line, in next-level contour lines, connecting all connection lines, and marking an obtained line segment set as a heavy metal pollution path. Corresponding heavy metal pollution treatment measures are taken according to the soil pH value, the soil ventilation capacity and the soil water content distribution condition in the pollution path; according to the invention, the serious pollution area and the diffusion trend can be clearly identified.

The invention discloses a scheduling method and system based on a ship traffic flow prediction model, belongs to the technical field of multi-step double-route ships, and is used for solving the problems that in the operation process of an existing multi-step double-line type ship lock, due to the fact that a ship arrangement plan is unreasonable, the utilization efficiency of lock chambers is low, and the cost is low. And the overall operation efficiency and the collaboration of the passing of the fleet are difficult to improve. The method comprises the following steps: carrying out passing analysis on a ship queue of a wait-to-pass water area under a time factor and a queue length factor, and determining a ship queue state; performing dynamic and static data statistics on all ships in the wait-to-pass water area to obtain dynamic attributes and static attributes of each ship; performing interval division of ship flow on the ship queue in the waiting passing area to obtain an initial ship interval; performing target optimization scheduling processing on the ship flow in the ship interval to obtain a final interval ship flow; and a ship flow scheduling planning scheme based on the multi-step double-linear ship lock is generated.

The invention discloses a COD (Chemical Oxygen Demand) prediction method based on a two-channel convolution time sequence mechanism, which comprises the following steps of: acquiring COD original data for preprocessing to obtain COD input characteristics; constructing a prediction model, and importing data into a dual-channel convolution module; importing the first double-channel convolution output feature vector and the second double-channel convolution output feature vector into a double attention memory network to obtain a time sequence dependence feature; the time sequence dependence characteristics are imported into an output module, and predicted COD output is obtained; training the model, and in the training process, optimizing hyper-parameters of the model based on an improved pelican algorithm; and importing real-time COD original data into the trained model, and obtaining real-time COD output. According to the method, a high-efficiency effluent COD content prediction model of the sewage treatment plant is constructed by creatively fusing the two-channel convolution and the double-attention memory network.

The invention discloses an intelligent measuring and reporting device and method for insect pest conditions of vegetables in a facility, and the method comprises the steps: carrying out the color trapping of pests through employing a sticky card according to the color taxis principle of the pests in the daytime, and trapping the pests through employing light according to the phototaxis principle of the pests at night; shooting the insect sticking plate in different time periods to obtain image information, and uploading the image information to a cloud analysis server through a network for image recognition and judgment, data statistics and comprehensive analysis to obtain an insect condition result analysis report; the obtained insect condition result analysis report is issued to a terminal in real time for a user to obtain, and technical support is provided for subsequent insect pest prevention and control.

The invention discloses an acute myocardial infarction prognosis prediction model establishment method and system, and relates to the technical field of risk prediction.The method comprises the steps that patient chip data is collected and screened, and a first myocardial infarction key gene is obtained; verifying the first myocardial infarction key gene to obtain a second myocardial infarction key gene; analyzing the relationship between the expression level of the key gene and the prognosis of the patient based on the second myocardial infarction key gene; and constructing an acute myocardial infarction prognosis prediction model by using the relationship between the expression level of the key gene and the prognosis of the patient, and performing performance evaluation. By combining immune-related LncRNA, a second myocardial infarction key gene is screened out, an acute myocardial infarction prognosis prediction model is constructed, the occurrence rate of MACE events of a patient is effectively predicted, the accuracy of myocardial infarction prognosis prediction is improved, high-risk patients are screened, and more effective prevention and treatment are provided.

The invention discloses an entity relationship prediction method and device for a time sequence knowledge graph, and the method comprises the steps: firstly obtaining a to-be-predicted tetrad of the time sequence knowledge graph; predicting missing contents of the to-be-predicted tetrad by using a pre-trained TFKE neural network model to obtain a plurality of predicted tetrad; the missing content of the to-be-predicted tetrad comprises missing of a head entity, missing of a tail entity or missing of a head-tail entity relationship; then, a TFKE neural network model is utilized to obtain the score of each prediction tetrad; and finally, adding the prediction tetrad of which the score of each prediction tetrad exceeds a score threshold value into the time sequence knowledge graph for completion. Vector representation of the head entity, the tail entity, the relation between the head entity and the tail entity and time is obtained through training, the TFKE neural network model capable of completing entity prediction and relation prediction in a vector space can effectively predict lost entities and relations in the time sequence knowledge graph, and therefore the prediction efficiency of the entity relations of the time sequence knowledge graph is improved.

The invention belongs to the technical field of deep learning, and particularly relates to a multi-dimensional data fusion processing method for medical text data, which comprises the following steps: acquiring multi-dimensional data, and inputting the multi-dimensional data into a trained fusion model to obtain a multi-dimensional data fusion result; the step of training the fusion model comprises the sub-steps of obtaining a multi-dimensional data set, and dividing the multi-dimensional data set into a training set and a test set by adopting a data splitting technology; performing feature extraction on the data in the training set, performing tensor conversion on the extracted features, and reconstructing the features after tensor conversion; inputting the reconstructed feature data into the fusion model for training, adjusting model parameters, and finishing model training when a model loss function is minimum; in a model definition stage, a DNN deep learning network architecture is used to ensure that a model can effectively learn and predict a target variable; through the constructed multi-dimensional data fusion processing method, the key information is directly extracted from the original medical text obtained in multiple inspection modes, and the working efficiency of the user is improved.

The invention provides a geothermal resource surface geochemical exploration method and system, and the method comprises the steps: determining a matched net degree according to an exploration area range, collecting gas in soil and soil samples, and determining the gas components and element compositions of the samples through different devices; the influence of the formation reaction is considered, and gas and elements with the migration capacity and anti-interference performance meeting the requirements are selected as geothermal chemical exploration indexes of different formation types; the content of soluble inorganic salt in the stratum and the content of gas in the stratum are measured, and the position where heat storage exists is recognized in combination with the set high-salt and low-gas area; and calculating a cover layer score and a reservoir layer score based on geothermal chemical exploration indexes, further calculating a comprehensive score, and realizing quantitative rating of the thermal reservoir stratum according to the comprehensive score. By adopting the scheme, the problems of insufficient comprehensiveness and insufficient exploration precision in the prior art are solved, the optimization of reliable indexes is realized, a heat storage and cover layer comprehensive evaluation method system is formed based on a heat storage mode of high salt and low gas, and underlying stratum geothermal resources can be effectively tracked.

The invention relates to an intelligent card chip energy consumption prediction and optimization method and a related device. The method comprises the following steps: acquiring operation data based on a target working mode acquired by an intelligent card chip in a target time window, and performing time slice division processing and clustering processing on the operation data to obtain a multi-dimensional feature vector in the target time window; obtaining a trained energy consumption prediction model, inputting the multi-dimensional feature vector into the energy consumption prediction model for processing, and predicting to obtain a dynamic energy consumption distribution diagram corresponding to the energy consumption parameters of the smart card chip; and determining each energy consumption unit of the smart card chip, and optimizing the energy consumption parameters based on the association relationship between each energy consumption unit and the dynamic energy consumption distribution diagram to obtain optimized energy consumption parameters. By adopting the method, energy consumption prediction and optimization can be efficiently and effectively carried out on the intelligent card chip.