39 results about "Gray relational analysis" patented technology

The invention relates to the technical field of e-commerce, in particular to an e-commerce promotion method and system based on cloud computing, and the method comprises the following steps: building a user behavior sequence through log collection, extracting a sudden change point through dynamic time warping, generating a dynamic parameter through segmented integral, calculating a difference degree through gray correlation analysis, and reconstructing a weight set. And flexibly scheduling advertisement resource distribution, and generating a delivery instruction. According to the method, user click stream data is collected, a behavior sequence model is constructed, continuous dynamic characteristics of user browsing tracks are captured, time sequence relevance is analyzed, fluctuation peak points of user behavior paths are aligned, interest mutation nodes are captured, abnormal behavior modes are recognized, and implicit preferences and explicit feedback parameters in the interaction process are quantified. Establishing an associated feature analysis mechanism, resetting advertisement weight parameters, matching real-time requirements of users with putting strategies, mapping weights to distributed advertisement nodes, dynamically scheduling computing resources, adapting display requirements and resource allocation, and forming a closed-loop optimization link.

The invention relates to the technical field of safety evaluation of highway bridge girder erection machines, and discloses a safety evaluation method for the initial installation state of a highway bridge girder erection machine. Firstly, a multi-modal safety evaluation model including a structure layer, a dynamic parameter layer and a risk factor layer is established; dividing a dynamic parameter layer feature interval by using a fuzzy clustering algorithm, calculating a risk factor correlation coefficient by using a grey correlation analysis method, determining a dynamic parameter layer parameter objective weight by using an entropy weight method, and performing parameter fusion by combining the two to obtain a comprehensive weight; then multi-source sensing data installed by the bridge erecting machine are collected in real time and discretized; and finally calculating risk indexes layer by layer, and judging the safety level of the initial installation state of the bridge girder erection machine. The method comprehensively considers the structure, the dynamic parameters and the risk factors of the bridge girder erection machine, improves the evaluation accuracy by applying various algorithms, realizes the automatic judgment of the safety level, can effectively reduce the installation risk of the bridge girder erection machine, and guarantees the construction safety and the smooth engineering construction.

The invention discloses a supporting power supply technical index evaluation method and system for a novel electric power system, and the method comprises the steps: constructing the technical index of each supporting power supply, comprehensively covering the performances of each dimension of the supporting power supply, determining the subjective weight of each index through employing a dynamic network analytic hierarchy process, and carrying out the evaluation of the technical indexes. The objective weight of each index is calculated by using a weight fusion method based on an entropy method, a machine learning algorithm and grey correlation analysis, the actual contributions of the indexes in different scenes are comprehensively reflected, the subjective weight and the objective weight of each index are dynamically fused to obtain the comprehensive weight of each index, and the comprehensive weight of each index is calculated. According to the method, the influence of subjective weight and objective weight is effectively balanced, the improved TOPSIS method is used for sorting the plurality of supporting power supplies based on the comprehensive weight of each index of the supporting power supplies, and the technical index evaluation results of the plurality of supporting power supplies are obtained, so that the comprehensiveness, flexibility and simplicity of the technical index evaluation of the supporting power supplies are improved.

The invention discloses a rock compressive strength prediction system and method based on grey correlation analysis and GWO-BILSTM, and the system is characterized in that a data collection unit is used for collecting Schmidt rebound index, Leeb hardness, Rockwell hardness, longitudinal wave velocity, density and uniaxial compressive strength measured values, and taking the above factors as original data; a gray correlation analysis module, a model optimization module and a compressive strength prediction module are arranged in the data processing unit, and the gray correlation analysis module is used for performing normalization processing on data and calculating a gray correlation degree; the model optimization module is used for optimizing the compressive strength prediction module; the compressive strength prediction module is used for predicting the compressive strength of the rock and outputting a result to the visual output module; the method comprises: collecting data; correlation degree analysis and input feature screening; establishing a rock compressive strength prediction model based on a bidirectional long-short-term memory network, and performing optimization by using a GWO algorithm; and carrying out actual prediction. The system and the method can realize efficient and accurate prediction of the compressive strength of the rock.

The invention discloses a multi-dimensional dynamic equilibrium comprehensive evaluation method for flexible operation of a coal-fired unit, and the method comprises the steps: determining a comprehensive evaluation index system of the coal-fired unit, and calculating each three-stage evaluation index value; calculating the objective weight of the secondary evaluation index by using an entropy weight method, introducing a dynamic group decision-making method on the basis of an analytic hierarchy process to obtain a subjective dynamic group decision-making model, and further determining the dynamic subjective weight of the secondary evaluation index; performing combined weighting on the subjective and objective weights to calculate a dynamic subjective and objective combined weight of each secondary evaluation index; and based on the dynamic subjective and objective combination weight of the second-level evaluation index, calculating the closeness of the first-level evaluation index and the positive and negative ideal solutions by using an approximate ideal solution sorting method and a gray correlation analysis method, and taking the closeness as a final evaluation result of the first-level evaluation index, thereby realizing the multi-dimensional dynamic equilibrium comprehensive evaluation of the second-level scale operation state of the coal-fired unit. According to the method, second-scale multi-dimensional dynamic equilibrium comprehensive evaluation can be realized during deep peak regulation and frequency modulation flexible operation of the coal-fired unit.

The invention relates to a multi-element reliability test data dynamic weight optimization method, which comprises the following steps of: inputting data, including accelerated life test data, fault excitation data, a mean value of historical failure and a standard deviation; calculating entropy values and dynamic weights of various types of data according to a formula; according to the dynamic weight optimization method, weight distribution is performed on existing test data related to various reliability faults through a multi-source data weight distribution method fusing an entropy weight method and grey correlation analysis, so that a verification result is more credible. According to the entropy-gray combined calculation technology, the theory of information entropy and the theory of gray relational degree are integrated, and test data are adjusted through the product term of the information entropy and the gray relational degree to verify the distribution weight. According to the full-automatic weight distribution system, a real-time weight updating mechanism without manual intervention is achieved by updating an input sequence of data.

The invention relates to the field of satellite navigation performance evaluation, in particular to a satellite navigation countermeasure multi-attribute decision-making performance evaluation method and system, which considers the advantage complementarity of grey correlation analysis and an ideal approximation method, carries out index weight determination based on improved combination weighting and grey ideal approximation, improves the credibility of a weight determination result, and improves the performance of the satellite navigation countermeasure multi-attribute decision-making performance evaluation. For different confrontation schemes, alternative scheme preferential selection is efficiently completed, a weighted average type fuzzy comprehensive evaluation method is adopted, three classic membership functions are selected, comprehensive evaluation verification of alternative scheme preferential selection is achieved, an efficiency evaluation result is obtained, and the accuracy of multi-attribute decision making is improved. The method can be used as a key basis for satellite navigation confrontation dynamic game effectiveness evaluation research and assists in making an optimal decision, so that protection benefits of an own navigation system during confrontation are maximized, and efficient transition of satellite navigation confrontation from evaluation to decision is realized.

The present invention relates to the technical field of engine simulation. Disclosed are a precision evaluation method and apparatus for an engine simulation model, and an electronic device, which are used for providing a technical solution that improves the precision of an engine simulation model to better support the design, optimization and application of engines. The method comprises: acquiring target parameter simulation data and target parameter test data of an engine; determining the target parameter simulation data and the target parameter test data to be target parameter steady-state simulation data, target parameter steady-state test data, target parameter transient-state simulation data and target parameter transient-state test data; on the basis of the target parameter steady-state simulation data and the target parameter steady-state test data, determining the steady-state precision of target parameters; using a plurality of prediction algorithms to determine a fitting degree evaluation result and a similarity evaluation result of the target parameters under each prediction algorithm; and using a grey relational analysis method to determine the transient-state precision of the target parameters.

The invention discloses a cibotium barometz sporophyte transformation method and system based on multi-factor coupling regulation, and relates to the technical field of biology. The method comprises the following steps: synchronously acquiring environment data and sporophyte image data; preprocessing the environment data, performing feature extraction on the image data, and splicing the processed data into a comprehensive feature vector; quantifying the weight, correlation degree and interaction level among the environmental factors through an entropy method, grey correlation analysis and a coupling coordination degree model; constructing an improved CNN-LSTM neural network, extracting features through a double-branch structure, fusing a coupling analysis result to carry out cross-modal fusion, and training to obtain a prediction model; when the predicted conversion rate is lower than a threshold value, environment parameters are automatically adjusted until conversion is completed; according to the method, the problems of environmental factor data isolation and regulation lag in the prior art are solved, accurate prediction and intelligent regulation of the sporophyte transformation process are realized, and the transformation success rate and the production efficiency are remarkably improved.

The invention discloses an energy-saving evaluation method, system, equipment and program based on multi-parameter coupling. The method comprises the following steps: acquiring parameters; the parameters comprise environment parameters, equipment parameters and operation parameters; calculating the correlation degree among the parameters based on a grey correlation analysis method; establishing a coupling energy consumption model based on the parameters and the correlation degree between the parameters; and performing energy-saving evaluation based on the coupling energy consumption model and the dynamic reference to obtain an energy consumption index. Through the processing scheme disclosed by the invention, the coupling loss can be detected, the evaluation effectiveness is still kept under the unsteady working conditions such as equipment aging and climate abnormality, and the evaluation error is reduced.

The invention provides an agricultural product traceability management method and system based on a block chain technology, and the method comprises the steps: collecting key information of a current link in an agricultural product supply chain, determining risk factors, carrying out the quantitative evaluation of each evaluation index of each risk factor through fuzzy comprehensive evaluation, determining the initial risk level of each risk factor through a risk matrix, and carrying out the traceability management of the agricultural product. And determining the correlation degree among the risk factors through grey correlation analysis, correcting the initial risk level according to the correlation degree among the risk factors to obtain a final risk level, and performing real-time uplink storage on the key information and the risk levels of the risk factors through a block chain. According to the method, the risk level of the risk factor is determined by using fuzzy comprehensive evaluation and the risk matrix, and then the risk level is corrected in combination with grey correlation analysis, so that accurate risk management is realized, and meanwhile, a digital guarantee is provided for risk and traceability management of agricultural products through real-time chaining storage of the block chain.

The invention discloses an industrial software resource evaluation method and system based on an improved FAHP-TOPSIS method, and belongs to the technical field of industrial software resource evaluation. The method comprises the following steps: determining an evaluation index system of industrial software resources by using an AHP method; according to score comparison of a plurality of experts for the evaluation index system, obtaining a weight cognitive matrix of each expert for each industrial software resource evaluation index; and analyzing and determining the relative importance of the weight cognitive matrix endowed by each expert in the overall index system through a grey correlation analysis method, and forming a comprehensive weight value of each evaluation index. According to the method, the TOPSIS method is utilized, the industrial software resources are subjected to efficiency sorting and optimization according to the comprehensive weight values, judgment errors caused by professional background differences of experts are effectively avoided, the accuracy and reliability of evaluation are remarkably improved, complex evaluation indexes and interrelations are effectively processed, and the decision making process is made to be more reasonable.

The invention discloses an electricity purchasing and selling strategy optimization aided decision-making method, and the method comprises the steps: determining an evaluation index set, and evaluating the gray correlation degree of each evaluation index through gray correlation analysis. Based on the grey correlation degree, the weight vector of the evaluation index is further determined so as to comprehensively consider multiple factors influencing the electricity market, such as market transaction share, electrical load and generating capacity, and meanwhile, the influence degree of each index on the electricity market is more accurately evaluated. Then, a fuzzy evaluation matrix of the evaluation indexes is constructed, and a fuzzy comprehensive evaluation vector is calculated in combination with the weight vectors of the evaluation indexes; the vector can comprehensively reflect the comprehensive membership degree of a plurality of evaluation indexes to high, medium and low risk levels, so that comprehensive evaluation of market transaction risks is realized. In the decision-making process, the particle swarm optimization algorithm is adopted to solve the electricity purchasing and selling strategy decision-making model, the optimal electricity purchasing and selling strategy is automatically explored, and the decision-making efficiency and accuracy are remarkably improved.

This invention discloses a health status prediction system and method for electric shovel equipment based on edge-cloud collaboration and digital twins, belonging to the field of electric shovel equipment status prediction technology. The system includes an electric shovel equipment health status management system based on edge-cloud collaboration and an electric shovel digital twin model. It collects multi-dimensional status data of the electric shovel through multiple sensors, and achieves layered transmission and edge-cloud collaborative processing via NB-IoT, edge devices, and 5G base stations. A high-fidelity electric shovel digital twin model is constructed to achieve synchronous mapping between the physical entity and the virtual model. The method utilizes grey relational analysis to calculate the correlation between real-time status data and offline fault samples, accurately assessing the equipment's health status, and outputs predictive maintenance processes through edge-cloud collaboration, synchronously updating the cloud database. This invention achieves closed-loop management of electric shovel equipment from data acquisition, twin modeling, fault diagnosis to predictive maintenance, significantly improving the accuracy of status assessment and the efficiency of operation and maintenance collaboration.

The invention provides a high formwork project safety management level comparison method based on a grey system theory. The method comprises the following steps: establishing an index system for safety management level evaluation by analyzing safety management influence factors of a construction site, and constructing a hierarchical structure model; constructing a judgment matrix according to a pairwise comparison result of the importance of the evaluation indexes; calculating the weight of each index of safety management level evaluation according to the judgment matrix; collecting data of each evaluation index of a to-be-evaluated project, establishing a grey correlation analysis model, and reflecting the quality of the project safety management level according to the grey correlation closeness of each project so as to quantitatively realize the evaluation and comparison of the construction safety level of the high-formwork project; through quantitative analysis and calculation, the high-formwork project safety management level is compared and judged, the scientificity of high-formwork safety evaluation is improved, and the accuracy of high-formwork safety evaluation is improved.

This invention discloses a method, apparatus, equipment, and medium for information integration processing based on power grid fault analysis, relating to the field of information integration processing technology for power grid fault analysis. The method includes: constructing a multi-source fault analysis model of the power grid based on pre-acquired multi-source fault feature data using a grey relational analysis algorithm; generating a data feature sequence based on the multi-source fault analysis model using a wavelet transform algorithm; generating a time-scaled calibration result based on the data feature sequence using a dynamic time warping algorithm; updating the multi-source fault feature data using a time-series interpolation algorithm based on the time-scaled calibration result to obtain a corrected fault feature sequence; and fusing the acquired power grid topology and the corrected fault feature sequence using a support vector machine algorithm to generate a fault judgment result. This application aims to solve the problems of low time-scaled calibration accuracy and lack of topology correlation in traditional methods.

This invention discloses an early detection and evaluation method and system for the abiotic stress tolerance of new forest tree germplasm. The method includes: collecting chlorophyll fluorescence indicators, phenotypic indicators, and physiological and biochemical indicators of forest trees under abiotic stress conditions, and performing data cleaning and standardization; performing principal component analysis on the multi-source standardized dataset to screen candidate indicators; performing grey relational analysis using the comprehensive score of forest tree stress tolerance as a reference sequence and the candidate indicators as a comparison sequence; determining the core indicators and their weights based on the variance contribution rate of the principal component analysis and the correlation degree of the grey relational analysis; obtaining the abiotic stress assessment index from the core indicators using a membership function; and classifying the tolerance level of forest tree germplasm using a clustering algorithm based on this index. This invention achieves early, rapid, and accurate evaluation of the abiotic stress tolerance of forest tree germplasm, and can effectively guide forest tree stress resistance breeding work.

This invention discloses a method for constructing a time-series prediction model for the water environment based on dynamic feature mining of data, belonging to the field of environmental engineering technology. It solves the problems of high complexity and low efficiency in existing methods for dynamic feature mining of water environment data. The invention identifies target variables for a target watershed, collects historical data of the target variables and related variables, extracts features from the historical data, and establishes an initial input dataset. It uses grey relational analysis to analyze the correlation between the features of the target variables and the features of related variables, filtering variables whose correlation with the target variables exceeds a threshold. It uses SSA to optimize the parameters of STL, and uses the optimized STL to decompose the historical data of the target variables. Finally, it establishes an LSTM model, trains the LSTM model using the decomposed data and variables whose correlation exceeds the threshold, and obtains the SSA-LSTM model. This invention is applicable to water environment prediction and dynamic feature mining.

This invention discloses a comprehensive evaluation method for multi-dimensional dynamic equilibrium of flexible operation of coal-fired power units, comprising: determining a comprehensive evaluation index system for coal-fired power units and calculating the values ​​of each tertiary evaluation index; calculating the objective weights of secondary evaluation indicators using the entropy weight method, and introducing a dynamic group decision-making method based on the analytic hierarchy process to obtain a subjective dynamic group decision-making model, thereby determining the dynamic subjective weights of the secondary evaluation indicators; calculating the dynamic subjective-objective combined weights of each secondary evaluation indicator by combining and weighting the subjective and objective weights; and calculating the closeness between the primary evaluation indicators and the positive and negative ideal solutions using the approximation ideal solution ranking method and the grey relational analysis method based on the dynamic subjective-objective combined weights of the secondary evaluation indicators, as the final evaluation result of the primary evaluation indicators, thereby achieving a multi-dimensional dynamic equilibrium comprehensive evaluation of the operating status of coal-fired power units at the second-level scale. This invention can realize a multi-dimensional dynamic equilibrium comprehensive evaluation of coal-fired power units at the second-level scale during flexible operation with deep peak shaving and frequency regulation.

This invention pertains to the field of intelligent technology in the catering industry, specifically a training method for a large-scale reasoning language model based on task vector fusion. It involves collecting comprehensive business data from the catering industry, achieving cross-modal entity alignment based on fractional-order grey relational analysis, constructing a domain knowledge graph including a causal relationship layer, and enhancing the corpus. A domain-adaptive base model is obtained through pre-training with causal language modeling loss as the primary loss and combined with dual-auxiliary loss. Task vectors are then fine-tuned and purified through adaptive low-rank decomposition. Adaptive task weights are calculated based on Sobol global sensitivity analysis, and task vectors are nonlinearly fused using Caputo fractional derivatives to obtain a multi-task fusion model. Finally, multi-source causal evidence is fused based on Riemannian manifold geodesic causal distance and D-S evidence theory to enhance the model's causal reasoning training. This method improves the model's multi-task balance performance and causal reasoning accuracy.

This invention relates to the field of safety evaluation technology for highway bridge erecting machines, and discloses a method for safety evaluation of the initial installation state of a highway bridge erecting machine. First, a multimodal safety evaluation model is established, including a structural layer, a dynamic parameter layer, and a risk factor layer. Then, a fuzzy clustering algorithm is used to divide the feature intervals of the dynamic parameter layer, and a grey relational analysis method is used to calculate the correlation coefficient of risk factors. The objective weights of the parameters in the dynamic parameter layer are determined using the entropy weight method, and the two are combined to obtain a comprehensive weight. Next, multi-source sensor data of the bridge erecting machine installation are collected in real time and discretized. Finally, the risk index is calculated layer by layer to determine the safety level of the initial installation state of the bridge erecting machine. This method comprehensively considers the structure, dynamic parameters, and risk factors of the bridge erecting machine, uses multiple algorithms to improve the accuracy of the evaluation, and achieves automatic determination of the safety level. It can effectively reduce the installation risk of the bridge erecting machine and ensure construction safety and the smooth progress of the project.

The invention relates to a layered distillation method for botnet detection in encrypted DNS (Domain Name Server) traffic, which mainly comprises the following steps of: carrying out adaptive feature extraction and screening on preprocessed data by combining an entropy weight method and grey correlation analysis, and screening out optimal features which are strongly correlated with botnet behaviors; classifying the botnet by adopting a spatial clustering classification strategy to obtain a plurality of different types of botnet features; a hierarchical knowledge distillation model HLD-TCN is constructed and trained; deploying a trained multi-student model; and inputting the real-time encrypted DNS traffic, sequentially carrying out parallel detection on the real-time encrypted DNS traffic through the student models, and if a certain student model is judged to be abnormal, immediately marking the real-time encrypted DNS traffic as Botnet traffic and terminating subsequent detection. The method has the advantages that the accuracy of botnet detection in the encrypted DNS traffic is improved, and an efficient and lightweight solution is provided for network security protection.

This invention discloses a project portfolio optimization method based on heterogeneous networks, relating to the fields of project management and intelligent decision-making. The method first acquires project objectives, capability requirements, resource inputs, task dependencies, and risk information, and then standardizes and encodes projects, capabilities, tasks, resources, and strategies. Based on RDF triples, it constructs project association networks, capability association networks, and capability-project fusion heterogeneous networks. It uses grey relational analysis and entropy weighting to determine capability importance, and combines project support, project contribution, data envelopment analysis, CRITIC, critical path method, and TOPSIS to complete project value, core projects, critical path, and risk assessment. Under budget, capability thresholds, project mutual exclusion, and schedule constraints, a multi-objective portfolio optimization model is established, and NSGA-II is used to generate a Pareto optimal solution set. Finally, a recommended solution is output through weighted scoring. This invention can improve the systematicity, accuracy, and risk controllability of resource allocation.

This application provides a method and system for analyzing and modeling user electricity consumption data based on semi-supervised learning, belonging to the field of power system data analysis and prediction technology. It solves the problems of low data verification accuracy and large deviations in characteristic correlation in existing technologies. The method includes the following steps: based on user electricity consumption characteristic data and climate characteristic data, a semi-supervised learning-driven electricity consumption data verification and tracking feedback model is constructed using a graph convolutional network; the Pearson correlation coefficient between electricity consumption characteristics of different types of users is calculated, and a characteristic correlation model based on a deep belief network is constructed to analyze the nonlinear relationship between user type, user electricity consumption time-series characteristics, and climate characteristics; based on grey relational analysis, the distribution characteristics of electricity consumption changes are captured. This application is applicable to power system resource optimization, especially improving grid operation efficiency under climate variability.

The invention discloses a coal-fired unit flexible operation dynamic comprehensive evaluation method and system with adaptive, robust and multi-criterion evaluation, and the method comprises the steps: constructing an evaluation index set through employing a fuzzy Delphi method; based on Kalman filtering dynamic fusion, using a subjective weight calculated by a triangular fuzzy number best-worst method and an objective weight calculated by an entropy weight method to obtain a dynamic subjective and objective combination weight of the multi-dimensional index; a comprehensive evaluation result is obtained by adopting optimal preference coefficients of a Monte Carlo simulation optimization approximation ideal solution sorting method and a grey correlation analysis method, and multi-dimensional dynamic comprehensive evaluation of the wide-working-condition operation state of the coal-fired unit is achieved; two new indexes of the time ranking fluctuation entropy and the cumulative ranking offset area are provided for evaluating the robustness of the comprehensive evaluation method, and it is ensured that the evaluation method is scientific and reliable. According to the method, an effective tool is provided for scientifically evaluating the wide-load operation performance of the coal-fired unit and optimizing the flexible adjustment capability of the coal-fired unit, and the competitiveness of coal power enterprises in the electric power spot market can be improved.

This invention provides a blockchain asynchronous consensus method based on GRA-HB-BFT, belonging to the field of blockchain asynchronous consensus technology. This invention employs grey relational analysis combined with entropy method to construct a dynamic node reputation evaluation model, objectively evaluating consensus node performance through multi-dimensional indicators and outputting a comprehensive reputation value. Secondly, the traditional asynchronous common subset protocol is decoupled into a reliable broadcast protocol area and an asynchronous Byzantine protocol area, enabling parallel execution of the two areas. Consensus nodes perform threshold encryption on transaction proposals and broadcast them. Then, based on the reputation evaluation results, broadcast instances of high-reputation consensus nodes are selected as input for Byzantine consensus, effectively reducing communication overhead. Finally, after parallel processing, the encrypted proposals are threshold decrypted, verified, and sorted before being submitted to the ledger. Simultaneously, the comprehensive reputation value is dynamically adjusted based on the consensus node performance, forming a continuously optimized closed-loop control mechanism.

The present invention discloses a method for formulating loss reduction measures based on the identification of abnormal line loss in the same period, including identifying abnormal line loss data in the same period and taking corresponding loss reduction measures based on the identification results. The fluctuation characteristics of the abnormal line loss data in the same period are extracted through statistical analysis; the low-rank characteristics of the abnormal line loss data in the same period are extracted by constructing a line loss power matrix in the same period and performing singular value decomposition on the matrix; the same-direction correlation characteristics of the allocated line loss abnormal data in the same period are extracted by performing grey correlation analysis on the line loss allocation data in the same period; and the "rank sum is approximately equal" characteristics of the abnormal line loss data in the same period deviating from the normal data are obtained by theoretically calculating the line loss and analyzing the differences between the line losses in the same period. The present invention can effectively extract the characteristics of the abnormal line loss data in the same period and construct a corresponding neural network for training, and can realize the effective identification of the abnormal line loss data in the same period. Corresponding loss reduction measures are taken according to different identification results, which can effectively improve the loss reduction efficiency.

The application discloses a discrete element contact parameter prediction method based on feature analysis-machine learning, comprising the following steps: selecting a discrete element linear parallel bond model for contact parameter calibration according to soft rock particle characteristics and mold material parameters, and determining model parameters and value ranges; obtaining contact parameters of the discrete element linear parallel bond model based on a small amount of uniaxial compression and Brazilian splitting tests, and constituting a contact parameter-soft rock macro feature low-fidelity dataset based on the contact parameters; determining key contact parameters affecting the soft rock macro feature based on grey relational analysis (GRA), Pearson correlation analysis and mutual information (MI); obtaining a contact parameter-macro feature high-fidelity dataset based on a large amount of uniaxial compression and Brazilian splitting tests; establishing a key contact parameter prediction model of the discrete element linear parallel bond model based on machine learning; and determining an optimal prediction model of the key contact parameters of the linear parallel bond model. Corresponding systems, electronic devices and computer readable storage media are also disclosed.

This invention discloses a multi-faceted and refined method for predicting electric vehicle charging load, relating to the field of load prediction technology. The method includes the following steps: Step S1: Constructing a system of influencing factors for electric vehicle charging load and quantifying the time series of each influencing factor; Step S2: Based on the system of influencing factors for electric vehicle charging load, using the information gain method to calculate the correlation between different influencing factors and electric vehicle charging load, and filtering them according to a descending order principle to determine the input factor type; Step S3: Selecting similar days for the electric vehicle charging load of the predicted day using grey relational analysis and DTW distance analysis to determine the input order of the input factors; Step S4: Building a prediction model using a deep belief network, and using the determined input factor type and the input order of the input factors as the input to the prediction model, outputting an effective prediction of the electric vehicle charging load.

The invention belongs to the technical field of distribution network line checking, particularly relates to a distribution network line checking method, and more particularly relates to a distribution network line checking method based on a distribution network diagram modulus incidence relation. The method comprises the steps that an equivalent resistance method is used for calculating a distribution network line, equipment data, operation data, electric quantity data and topological data are collected, and the topological data are difficult to verify; performing distribution network line topology anomaly check on the topology data by using a frequency domain similarity grey correlation analysis method, and preprocessing the checked abnormal data; and carrying out graphic file and model file separation on the pre-processed abnormal data, and respectively checking and correcting. According to the invention, automatic identification of a transformer area topological relation can be realized, transformer area internal information can be comprehensively acquired, transformer area archive data is accurate, terminal fusion of a marketing and distribution system is realized, an intelligent transformer area with active and accurate service is created, line loss treatment efficiency and checking efficiency are greatly improved, power supply reliability is improved, and accurate data support is provided for production and regulation and control.