9 results about "Gray relational analysis" patented technology

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.

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 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.

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 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.

This invention discloses an enterprise-level SSD resource scheduling system based on storage virtualization, relating to the fields of storage scheduling and data processing technology. It includes a virtual resource modeling module that performs aggregation and association processing, service level binding processing, and access feature organization processing on tenant identifiers, service volume identifiers, namespace identifiers, virtual function identifiers, IO queue occupancy information, and access request characteristics to generate virtual resource status data. In this invention, the virtual resource modeling module performs aggregation and association, service level binding, and access feature organization; the media status awareness module performs disk-level correspondence organization, time-sequence organization, and media pressure merging; and the dual-state mapping construction module performs resource instance correspondence processing, media bearer association processing, and gray relational analysis algorithm processing based on dynamic time warping alignment. This achieves continuous mapping between virtual resource instances and media operating states, explicit bearer relationships, and unified cross-layer state expression.

This invention discloses a method for predicting sand production in oil wells based on production dynamics and ESP (Electric Submersible Pump) operating parameters, comprising the following steps: S1: a primary sand production factor correlation analysis strategy based on oil well production dynamic parameters; S2: a secondary sand production pattern prediction strategy based on ESP pump current. On one hand, this invention constructs a multi-dimensional dynamic parameter evaluation system by integrating grey relational analysis and analytic hierarchy process (AHP), establishing a quantitative correlation model between production parameter fluctuations and sand production levels, and realizing dynamic screening and weight allocation of sensitive factors of operating parameters. On the other hand, based on the temporal feature capture capability of LSTM networks, a two-dimensional feature space of pump current change rate and amplitude is constructed. By deeply mining the coupling relationship between current waveform and sand particle transport, a dynamically adaptive sand production state identification mechanism is established.

This invention relates to a carbon emission prediction method and system based on meta-learning data augmentation. The method acquires and preprocesses historical carbon emission time-series data and multi-dimensional influencing factors for a target region to construct an original dataset; it then constructs a few-shot time-series generation model and introduces time-series datasets from other domains for cross-domain meta-learning pre-training, fine-tuning it in the target domain to generate synthetic data, which, together with the original data, forms an augmented dataset; next, it filters key influencing factors through improved grey relational analysis and a lightweight attention mechanism; finally, using the augmented data and key influencing factors as input, it constructs a Transformer-based prediction model and outputs the predicted future carbon emissions for the target region. Compared with existing technologies, this invention has advantages such as improved accuracy, stability, and robustness in carbon emission prediction.