567 results about "Bayesian optimization" patented technology

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention relates to the crossing field of hydraulic engineering hydrological monitoring technology and machine learning prediction technology, discloses a sediment concentration prediction method based on deep learning, and aims to solve the problems that in existing sediment concentration prediction, hyper-parameter manual tuning is low in efficiency, key feature attention is insufficient, local and time sequence information is difficult to consider by a single model and the like. Accurate prediction is realized through five core modules: a data preprocessing module performs missing value filling, abnormal value processing and derivative feature generation on hydrological data; the feature selection module screens key features based on mutual information; the time sequence construction module generates time sequence data through a sliding window; the hyper-parameter automatic optimization module adopts Bayesian optimization iteration to obtain an optimal hyper-parameter; the CNN-LSTM-attention prediction module fuses CNN local feature extraction, bidirectional LSTM time sequence dependence capture and multi-head self-attention mechanism key feature focusing capability, is suitable for scenes such as river channels and channels, and provides efficient decision support for hydrological regulation and control.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention belongs to the field of material performance optimization, and particularly discloses a carbon ceramic resistor formula optimization method based on a genetic algorithm and Bayesian optimization, and the method comprises the steps: receiving formula parameter combinations and corresponding performance parameters of a plurality of groups of carbon ceramic resistors; a Gaussian process regression model based on a radial basis kernel function is established to construct a mapping relation between formula parameters and performance parameters, and a performance prediction model of the carbon ceramic resistor is obtained through training by maximizing marginal likelihood optimization model hyper-parameters; and based on the performance prediction model, performing joint optimization by using a genetic algorithm and a Bayesian optimization algorithm, and determining an optimal formula combination. According to the method, global exploration and local fine convergence can be considered, the prediction efficiency can be improved, and the accuracy, comprehensiveness and reliability of a prediction result can be improved.

The invention relates to the technical field of parameter optimization, in particular to an automatic kernel network parameter optimization method, which comprises the steps of constructing an enhanced deep Q network model, and integrating the enhanced deep Q network model with a priority playback buffer area, a meta learning module, a Bayesian optimizer and a neural architecture search module; using performance index data to train an enhanced deep Q network model, the training process including using a priority playback buffer to store and sample empirical data, using a meta-learning module to perform task adaptation, and monitoring training indexes of multiple dimensions to evaluate the convergence state of the model; selecting a kernel parameter adjustment action according to the current state through the trained enhanced deep Q network model; executing the selected kernel parameter adjustment action, and evaluating a parameter adjustment effect based on the multi-target reward function; and updating the enhanced deep Q network model according to an evaluation result, wherein the priority playback buffer area and the Bayesian optimizer are utilized in the updating process.

The invention discloses a method and a system for determining optimal parameters of an X-ray excited extra-high voltage composite apparatus, and relates to the technical field of extra-high voltage electrical equipment detection. The method comprises the following steps: remotely operating the X-ray machine and calculating partial discharge intensity; for the current irradiation position, the tube voltage and the tube current are gradually adjusted, parameters are dynamically optimized based on the quantitative relation between the partial discharge intensity and the radiation dose rate change rate, and the optimal parameters are recorded; and changing position repetition parameter optimization, and constructing a BP neural network model after data set training optimization so as to predict an optimal irradiation position. The optimized BP neural network adopts an improved sparrow search algorithm to optimize an initial weight and a threshold value, and combines Bayesian to optimize a learning rate and a hidden node number. Accurate optimization of excitation parameters and irradiation positions is achieved, detection accuracy and safety are both considered, the optimal irradiation position positioning efficiency is improved, and the method is suitable for ultra-high voltage GIS detection of different voltage grades.

The invention provides a low-voltage power distribution network load change trend intelligent prediction and management method, belongs to the field of low-voltage power distribution network load prediction, and is used for solving the problems of large load fluctuation, insufficient prediction precision and high cloud deployment delay of a hybrid industry transformer area in related technologies. The method is deployed at an edge node of a transformer area, high-quality data is output through multi-modal data anomaly detection and scene completion, a four-dimensional dynamic load portrait is constructed based on the high-quality data, model super-parameter self-adaptive parameter adjustment is realized by combining transfer learning and Bayesian optimization, and accurate load data is output through three-dimensional linkage resource scheduling and dynamic fusion residual error correction. The method improves the load prediction precision and efficiency, reduces the response delay, and can effectively support the real-time scheduling of the power distribution network.

The invention discloses a wind-solar-water-storage complementary system short-term risk scheduling method considering uncertainty, and the method comprises the steps: converging historical physical operation data and multi-subject behavior data, and constructing a training data set and a system parameter set; based on the training data set, constructing a combined robust radius and wind-solar combined scene containing behavior risk quantification; based on the system parameter set and the training data set, constructing a dynamic risk scheduling unit fusing multi-dimensional risks; solving a candidate short-term scheduling scheme by combining a joint robust radius and a dynamic risk scheduling unit and adopting a behavior risk-oriented Bayesian optimization method; and performing multi-subject consensus evaluation on the candidate short-term scheduling scheme, determining a final execution short-term scheduling scheme, and performing uplink execution. According to the method, the problem of separation of physical risks and behavior risks is solved, and the behavior acceptability of the scheme is improved.

The invention relates to the technical field of intelligent power grids, in particular to an urban power distribution network whole-process scene chain closed-loop monitoring method and system fusing digital twinning. The method comprises the following steps of: fusing a weighted evidence theory and a dynamic time bending algorithm to acquire multi-source data, and constructing a physical mechanism-data driven hybrid enhanced digital twinborn model; based on the model, abnormity is researched and judged through an improved isolated forest algorithm, grading alarms are generated, simulation deduction is conducted through Monte Carlo-Latin hypercube sampling, an optimal strategy is screened through a non-dominated sorting genetic algorithm III with uncertainty, and instructions are executed after graph neural network topology verification. Feedback is formed by the collection result, and the strategy is optimized through meta-learning and Bayesian optimization calibration model. The system comprises six cooperation units, an intelligent algorithm and a digital twinning technology are integrated, the power distribution network abnormity identification precision, decision scientificity and adaptive capacity are improved, and safe and efficient operation is guaranteed.

The invention belongs to the technical field of power equipment signal processing, and particularly relates to a multi-channel signal reverberation suppression method based on phase weighted cross-correlation optimization, which comprises the following steps: S1, acquiring an original multi-channel time domain signal matrix; s2, estimating propagation time delay of each channel relative to the reference channel; s3, enabling all channels to realize time domain alignment in a direct sound main energy section; s4, processing to obtain a multi-channel time-frequency domain signal; s5, performing reverberation suppression by adopting a WPE algorithm to obtain a multi-channel time-frequency domain signal after reverberation removal; before the WPE algorithm is executed, through a Bayesian optimization method based on a Gaussian process, a prediction order and prediction delay parameter combination enabling the SI-SNR of the multi-channel dereverberation signal to be maximum is searched, and the prediction order and prediction delay parameter combination is applied to the WPE algorithm; s6, reconstructing the multi-channel time-frequency domain signal after reverberation removal into a time domain signal; and S7, calculating a normalization coefficient, and performing amplitude scaling. According to the method, the dereverberation ultrasonic signal with high fidelity, high consistency and high robustness can be obtained in a complex reverberation environment.

The invention relates to the technical field of mass spectrometers, and discloses a mass spectrometer tuning method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a tuning parameter data set of a target mass spectrometer, the tuning parameter data set comprising a plurality of tuning parameters and a parameter range corresponding to each tuning parameter; obtaining an optimization target of the target mass spectrometer; based on the tuning parameter data set, the optimization target and a pre-constructed constrained multi-target Bayesian optimization framework, predicting an optimal tuning parameter combination of the optimization target, the optimal tuning parameter combination being used for tuning the target mass spectrometer; the constrained multi-objective Bayesian optimization framework combines Bayesian optimization and multi-objective optimization. According to the invention, multi-objective optimization is carried out based on the constrained multi-objective Bayesian optimization framework, the optimal tuning parameter combination can be automatically generated, simultaneous tuning of multiple parameters of the mass spectrometer is realized, and the tuning performance and precision of the mass spectrometer are improved.

The invention relates to the technical field of electric power monitoring, in particular to a zone area transformer abnormity online monitoring method based on measurement time sequence analysis, which comprises the following steps of: fusing multi-dimensional measurement data characteristics to construct health state evaluation indexes, and dividing time sequence samples by adopting a sliding window; a Bayesian optimization (BO) TCN-BiLSTM hybrid prediction model is fused, and high-precision prediction is realized by cooperatively extracting spatio-temporal characteristics of a metering time sequence; based on a health reference and actually measured data residual error comparison, a bilateral threshold strategy is adopted to realize accurate and reliable monitoring of the abnormal operation state of the transformer. According to the method, a multi-dimensional anomaly monitoring index system fusing distribution characteristics, volatility and tendency is constructed, health hierarchical division is introduced, and quantitative and hierarchical evaluation of transformer health degradation is realized. The indexes consider both the global trend and the local fluctuation, and the method has good applicability and generalizability at the transformer area level.

The invention discloses a consumer finance marketing strategy reaching model. According to the model, user behaviors and service data are acquired in real time through a data acquisition system, a dynamic user portrait is constructed by adopting a multi-head self-attention mechanism of a Transform model, a user behavior sequence is coded into a 512-dimensional dynamic interest vector, and the 512-dimensional dynamic interest vector and a 128-dimensional static attribute vector are fused to generate a comprehensive portrait. And training a strategy network based on a PPO reinforcement learning algorithm, modeling a marketing decision into a Markov decision process, and outputting a personalized strategy including a product type, a reach channel, an incentive limit and a reach opportunity. The system collects user feedback through full-link data burying points, maintains experience playback pools with the capacity of 1 million, carries out incremental learning every 4 hours, and adopts an elastic weight consolidation technology to avoid disastrous forgetting. A new strategy effect is verified through an A / B test, and automatic hyper-parameter tuning is carried out through Bayesian optimization. Practical application shows that the marketing conversion rate of the model is improved from 2.3% to 3.1%, the ROI is improved from 1.5 to 2.1, and the complaint rate is reduced by 40%.

The invention belongs to the field of ocean internal temperature detection, and particularly relates to a method for optimizing a multi-source constraint weight in ocean thermohaline profile variational inversion, which comprises the following steps of: acquiring a historical actually measured thermohaline profile and matched satellite remote sensing sea surface data, constructing a training set and counting a climate state background field; constructing a weighted variational cost function containing multiple constraint terms, and introducing an adjustable weight parameter for a key constraint term; based on the training set, constructing an external optimization function taking inversion error minimization as a target; weight parameters are iteratively learned through a double-layer optimization framework, the weight is updated by adopting methods such as Bayesian optimization in outer-layer optimization, and the optimal profile is solved under the given weight in inner-layer optimization; and finally, applying the optimal weight obtained by learning to variation inversion of new observation data to realize high-precision reconstruction of the thermohaline profile. According to the method, objective and adaptive optimization of the weight is realized, the inversion precision and robustness are remarkably improved, and the method has good physical interpretability and system universality.

The invention relates to a DAS-VSP seismic data noise suppression method based on a double-domain generative adversarial network, and belongs to the field of seismic exploration data denoising and deep learning. The method comprises the following steps: constructing a double-domain generative adversarial network, determining an optimal hyper-parameter combination by Bayesian optimization, adding actual noise to a pure seismic signal obtained by a forward modeling method to construct a complete training set, training the double-domain generative adversarial network, and testing the double-domain generative adversarial network. According to the method, DAS-VSP data which is low in signal-to-noise ratio and contains various kinds of complex noise can be effectively processed, the denoised seismic signals are clearer in structure, better in continuity, higher in signal-to-noise ratio and more thorough in noise suppression, effective signals are reserved to the maximum extent, high-quality basic data are provided for subsequent seismic data processing and explanation, and the method is suitable for large-scale popularization and application. The method meets the high-precision requirement of current seismic exploration, and has a wide application prospect in the field of oil and gas resource exploration and development.

The invention relates to the technical field of data processing, in particular to a large model reasoning optimization method based on context incremental updating, which comprises the following steps: processing a multi-modal data stream through timestamp alignment and a filtering algorithm, extracting features by adopting a shared encoder and a private encoder, and realizing feature decoupling through a depth information bottleneck principle. A dynamic emotion map is constructed by using Gaussian process regression and a random process algorithm, and self-adaptive updating control is realized by combining meta-learning and Bayesian optimization. Incremental state management is realized by adopting a neural Turing machine, reasoning consistency is guaranteed through a generative adversarial network, model parameters are optimized in combination with a digital twin system and reinforcement learning, and a mental health service response is finally generated through a conditional generation model and hierarchical reinforcement learning. According to the method, the problem of asynchronism of multi-modal emotion feature dynamic evolution and context increment updating is effectively solved, accumulated drift of emotion state tracking is eliminated, and the continuity of reasoning logic is guaranteed.

The invention discloses a numerical control machine tool part machining data storage method and system. The numerical control machine tool part machining data storage method comprises the steps that multi-source data in the machining process is collected and synchronized in real time; performing process-entropy-based process section segmentation on the data stream, extracting multi-modal features, evaluating data values through a classifier fusing rules and machine learning, and generating feature data packets according to a hierarchical storage strategy; a data gene code is bound for each part, a processing process knowledge graph is constructed, and full-chain data association and accurate tracing are achieved; a sample is generated based on the feature data packet, a graph neural network and Bayesian optimization are utilized to train a process optimization model at a cloud end, optimization parameters are fed back to a machine tool to be executed, and a closed loop is formed. Process entropy segmentation, knowledge graph and GNN closed loop optimization are adopted, the problems that data storage cost is high, tracing is difficult, and data values are not mined are solved, and the accuracy of data tracing is improved. And intelligent data compression, full-life-cycle tracing and process adaptive optimization are realized.

The invention relates to the field of power supply intelligent control management, in particular to an AI-based power supply module adaptive control method, which comprises the following steps: collecting real-time data of a power supply module, the real-time data comprising real-time load data, real-time temperature data and real-time voltage data; inputting the real-time data into a preset convolutional neural network model; extracting data features from the real-time data by using a preset convolutional neural network, and calculating the data features by using a built-in Bayesian optimization formula to obtain adaptive adjustment parameters, wherein the adaptive adjustment parameters comprise current parameters and / or frequency parameters; and adjusting the output current and / or the output frequency of the battery module according to the adaptive adjustment parameter. According to the invention, the output current, frequency and the like of the power supply are automatically adjusted through parameters such as real-time load, temperature, input voltage and the like of the power supply module, so that optimal energy efficiency and performance are realized, load abrupt change in more scenes can be adapted, and the working efficiency of the power supply module and the robustness of an automatic assembly line working in an application scene are improved.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.

The invention discloses a coal seam gas content dynamic prediction method based on Bayesian optimization XGBoost, and belongs to the field of coal mine gas control, and the method comprises the steps: collecting a sample set U composed of influence factors of coal seam gas occurrence, preprocessing the sample set to obtain a sample set, and dividing the sample set in proportion to obtain a training set, a verification set and a test set; dividing the training set into a plurality of sample sets; an XGBoost model is constructed, the nth sample set serves as input, the actual coal seam gas content serves as output, the XGBoost model is trained, and an nth coal seam gas content prediction model is obtained; the influence factors of coal seam gas occurrence are calculated through a three-dimensional geologic model, geologic structure characteristics and coal seam roof and floor lithologic characteristics are comprehensively considered, and a Bayesian optimization algorithm is adopted to carry out adaptive search on hyper-parameters of an XGBoost model. The technical problem that a traditional gas content prediction method mostly depends on static geological parameters and cannot achieve real-time prediction of the gas occurrence state under the complex geological condition is solved.

The invention discloses a Bayesian optimization-based hydrate reaction condition calculation method. The method comprises the following steps of S1, obtaining an initial data set through experimental measurement and numerical simulation; s2, establishing mathematical modeling of a hydrate reaction process based on chemical reaction engineering knowledge, and generating a simulation model; s3, constructing a proxy model by using a Bayesian optimization algorithm, and performing probability modeling and prediction on the target performance index to obtain performance distribution under a given experimental condition; s4, calculating and recommending optimal experiment parameters according to an expected improvement criterion; s5, performing an experiment or simulation under a recommendation condition, and merging new data into an existing data set; s6, repeating the steps of Bayesian optimization modeling, experiment recommendation and data updating until a predetermined termination condition is reached; and S7, outputting the optimized optimal reaction condition parameters and performance indexes. According to the method, the optimization efficiency and the data utilization rate are improved, multiple targets and complex constraints can be considered, and the scientificity and practicability of industrial process parameter optimization are remarkably enhanced.

The invention discloses an AI-driven polymer composite material process optimization method, and aims to solve the problems of data islands, process optimization lag and insufficient model timeliness in a polymer material production process. The method comprises the following steps: collecting full-link data according to a six-level customer product coding specification; the state parameters of the high-frequency equipment are safely stored in the sub-table 1 through encryption and identity authentication; constructing a structured database based on the production batch number association main table, the raw material sub-table set, the sub-table 2 and the sub-table 3; training a multi-model artificial intelligence system fusing gradient boosting regression, Bayesian optimization, a neural network and a random forest, and realizing a bidirectional linkage closed loop of formula recommendation, process optimization and performance prediction; and incremental learning is carried out by adopting a sliding window mechanism in combination with an online gradient descent and elastic weight consolidation strategy. According to the technical scheme, intelligent, efficient and safe optimization of the high polymer material process can be achieved, and the product quality and the production efficiency are remarkably improved.

The invention relates to the technical field of water quality online monitoring, in particular to an intelligent processing and service platform for ocean water quality monitoring data based on cloud-side collaboration. The method comprises the steps that an edge sensing preprocessing unit collects multi-point sensor data and uploads the data to a cloud after format standardization processing; the cloud multi-source fusion unit outputs a layered water quality spatial and temporal distribution result through a layered dynamic weighted interpolation model in combination with Bayesian optimization and cross validation; an edge end in the collaborative decision-making unit feeds back measured data to correct model parameters, and performs anomaly detection and early warning based on a One-Class SVM (Support Vector Machine) algorithm; and the intelligent information service unit is based on a cloud edge collaborative architecture, the cloud end provides global data query and visualization service, and the edge end pushes local abnormity early warning information. Through cloud-side cooperation, efficient cooperation of local rapid response and global accurate analysis is realized, and the accuracy and practicability of ocean water quality monitoring are improved.

The invention discloses an electric power system relay protection fault identification method, system and device based on Bayesian optimization and a storage medium, and relates to the field of electric power system fault identification and relay protection, and the method comprises the steps: collecting original electric quantity data from a power grid fault recording system, and converting the original electric quantity data into a two-dimensional image with a label; based on the two-dimensional image with the label, constructing a hybrid deep learning model skeleton for cooperatively extracting local features and associating with a time sequence; based on the two-dimensional image with the label, a Bayesian optimization algorithm is adopted to carry out automatic hyper-parameter tuning and training on the constructed hybrid deep learning model skeleton, and a CBAM-CNN-LSTM model capable of being used for actual fault identification is obtained; the CBAM-CNN-LSTM model is used to carry out online fault identification on the electrical quantity data acquired in real time; according to the method, the weak fault and the high-resistance fault of the new energy power grid are accurately and rapidly identified, the accuracy rate exceeds 99.7%, the response time of the whole process is less than 10 milliseconds, and the safety of the power grid is remarkably improved.

The invention relates to the technical field of industrial simulation, in particular to an industrial simulation optimization method based on multi-modal feature fusion. The method comprises the steps of obtaining multi-source industrial data, performing standardization processing through a data synchronization and timestamp alignment strategy, and performing feature extraction and vectorization by adopting a modal specific algorithm to generate a feature mapping relationship; modal features are obtained from the feature mapping relation for normalization, dimensionality reduction and deep learning model construction processing, and a multi-modal feature library is generated; generating a cross-modal attention weight matrix based on an improved attention mechanism; carrying out feature correlation evaluation, screening and weighted fusion by adopting a graph neural network and Bayesian optimization to generate a fusion feature vector; the fusion features are converted into simulation parameters, and a multi-physics field engine is loaded to execute an optimization algorithm to generate a simulation result; and finally, verifying, evaluating and adjusting parameters through space-time alignment, and generating optimal configuration. According to the invention, the precision, efficiency and adaptive optimization capability of industrial simulation are effectively improved.

The invention discloses a logistics unmanned aerial vehicle material distribution energy consumption and communication collaborative optimization method. The method comprises the steps of collecting distribution task data of an unmanned aerial vehicle and communication deployment data in a flight environment; establishing an unmanned aerial vehicle distribution system model based on the distribution task data and the communication deployment data, and constructing an optimization target to optimize the total energy consumption and the total communication interruption time of the unmanned aerial vehicle for completing the distribution task; based on the unmanned aerial vehicle distribution system model, solving a global distribution sequence with the lowest energy consumption; taking the global distribution sequence as input, converting an unmanned aerial vehicle trajectory planning problem into a Markov decision process, and solving a local distribution trajectory in the unmanned aerial vehicle distribution process; on the basis of the local distribution track, a Bayesian optimization model and a deep reinforcement learning network are adopted to obtain an actual flight action of the unmanned aerial vehicle at the next moment and execute the actual flight action; and based on the real-time environment information, generating an optimal flight path satisfying energy consumption and communication collaboration, and completing a distribution task. According to the invention, an efficient and safe distribution system is realized.

The invention discloses a power distribution network load access point dynamic weight preferential determination method considering new energy access, and belongs to the technical field of electric power. The method solves the problems that an existing load access point determination method depends on expert experience or static empowerment and is difficult to adapt to multi-source time-varying data of the power distribution network under new energy access, weight distribution lacks dynamic response, and consequently the deviation between a decision and actual operation is large. According to the technical scheme, the method comprises the steps of collecting multi-source data and preprocessing; constructing an evaluation index system, and performing dimension alignment based on the standardized data to form a feature tensor; generating an initial dynamic weight of a load access point by using a physical information space-time diagram network; optimizing the initial dynamic weight by adopting a constrained multi-objective Bayesian optimization method to obtain an optimal dynamic weight; and calculating a comprehensive score of each load access point based on the optimal dynamic weight and determining an optimal access point. According to the invention, the safety and operation efficiency of the power distribution network under new energy access are improved.

The invention provides an elastic monitoring alarm method and system based on multistage cooperative verification. The method comprises the step of remarkably improving the alarm accuracy through a three-level verification mechanism. The first-stage verification adopts intelligent anomaly detection, compares a current index with a historical baseline, and identifies a preliminary anomaly; in the second-stage verification, association index analysis is introduced, and pseudo anomalies caused by normal business fluctuation are filtered; and the third-stage verification performs cross-customer collaborative analysis to distinguish individual anomaly from global events. In addition, fault root causes are automatically positioned through dependency link analysis, and structured alarm information containing causes, confidence coefficients and processing suggestions is generated. And a closed-loop learning mechanism is established, operation and maintenance feedback is continuously collected, and alarm parameters are automatically adjusted by adopting Bayesian optimization. According to the method, the false alarm rate is reduced, the root cause positioning accuracy is improved, the average repair time is shortened, and the IT operation and maintenance efficiency is remarkably improved.