419 results about "Sensitivity analyses" patented technology

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

The present invention relates to the technical field of heat sink structure design, and in particular to a micro-channel heat sink structure design method based on a multi-objective function. The method comprises the following specific steps: S1, constructing a general mathematical description of a topology optimization problem; S2, acquiring input conditions for a specific design scenario, and dividing a topology optimization design domain; S3, establishing a governing equation for a topology optimization model; S4, on the basis of an adjoint method, performing sensitivity analysis of a multi-objective function with respect to design variables; S5, solving the topology optimization model by means of a method of moving asymptotes (MMA); S6, obtaining a two-dimensional flow channel configuration of a heat sink on the basis of contour reconstruction, carrying out finite element analysis of a two-dimensional flow channel structure, and narrowing down the selection range of constraint conditions; and S7, on the basis of a two-dimensional finite element analysis result, constructing a corresponding pseudo-three-dimensional model, carrying out finite element analysis, and determining optimal constraint conditions to obtain a final topology-optimized heat sink configuration. Compared with traditional methods, the present invention can more efficiently solve the problem in respect of heat sink structure optimization under different scenarios.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the technical field of synchronous reluctance motor structure optimization, in particular to a permanent magnet auxiliary synchronous reluctance motor structure parameter multi-objective optimization method. The method comprises the steps that motor structure optimization parameters are selected, and the change range of the parameters is determined; carrying out comprehensive sensitivity analysis according to the selected optimization target; performing hierarchical processing on the optimization variables according to the comprehensive sensitivity index, dividing the optimization variables into a strong sensitive layer and a weak sensitive layer, and taking parameters in the strong sensitive layer as to-be-optimized parameters of the next algorithm; comparing the prediction precision of the back propagation neural network, the radial basis, the extreme learning machine, the support vector machine and the kernel extreme learning machine, and selecting an agent model with an optimal prediction effect; combining the established high-precision agent model with a fast non-dominated sorting genetic algorithm, and searching an optimal combination of to-be-optimized parameters; and finally, carrying out single parameterization scanning determination on parameters in the weak sensitive layer by utilizing finite element simulation so as to obtain optimal structural parameters of the permanent magnet auxiliary synchronous reluctance motor.

The invention relates to the technical field of artificial intelligence, and discloses an agent rapid construction method and system based on enhanced retrieval, and the method comprises the steps: constructing a multi-dimensional task analysis system, retrieval task characteristics are automatically represented through characteristics such as data distribution, query complexity and semantic requirements; executing a progressive architecture evolution algorithm, and automatically searching an optimal architecture meeting multi-dimensional balance of retrieval precision, calculation complexity and memory occupation based on the task characteristic vector; constructing a hierarchical knowledge distillation transfer chain, and realizing efficient knowledge migration from a large model to a small model through feature matching and attention guidance; implementing mixing precision quantification and structured pruning on different hierarchies based on sensitivity analysis, and adapting to a target deployment environment; according to the method, the model volume is reduced, the reasoning speed is increased, the energy consumption is reduced, and the enhanced retrieval agent can efficiently operate on resource-constrained equipment.

The invention discloses a multi-dimensional data statistics intelligent analysis and prediction system and method, and particularly relates to the technical field of data prediction. Historical sales data, consumer behavior data, market environment data and external dynamic data of a target analysis object are collected, an LSTM basic prediction model is constructed, consumer behavior change trend features and market environment dynamic features are further extracted, the features are fused with the LSTM model to generate a multi-modal prediction model, and the multi-modal prediction model is analyzed. Comprehensively analyzing the interaction influence of the multi-dimensional data by using a multi-input structure; according to the method, influence weights and contribution degrees of different characteristics are evaluated through sensitivity analysis and interaction analysis, model parameter weights are dynamically adjusted, an optimized prediction model is utilized to generate a multi-dimensional prediction result in a fixed time period, and inventory management and a promotion resource allocation strategy are optimized accordingly, so that long-term drift of consumption behaviors is effectively coped with, and the consumption performance is improved. The inventory cost and the resource waste are reduced, and the operation efficiency and the business decision accuracy are improved.

The invention belongs to but not limited to the technical field of new energy, particularly relates to a battery physical and chemical model parameter identification method and system based on different temperature impedances, aims to improve the accuracy of model simulation, considers contact resistance when establishing a one-dimensional P2D physical and chemical model of a battery, and simultaneously solves the problem that the model parameters are more and difficult to identify. In order to solve the problems that in the prior art, parameters with high sensitivity and medium sensitivity and meeting actual conditions are selected by adopting a parameter sensitivity analysis method, finally, the parameters selected under different working conditions are identified by adopting a grey wolf optimization algorithm, simulation EIS data are compared with actual measurement data, and the simulation accuracy of the model is influenced by excessive parameters. The result shows that the grey wolf optimization algorithm realizes accurate identification of the parameters of the one-dimensional P2D physical and chemical model of the battery. The method disclosed by the invention achieves remarkable results in the aspects of battery modeling and parameter identification.

The invention belongs to the technical field of structure optimization, and particularly discloses a geometric nonlinearity-considered parameterized level set porous structure topological optimization method, which comprises the following steps of: constructing a level set function to implicitly describe a structure boundary, and performing spline interpolation by adopting a tightly-supported radial basis function to obtain a parameterized level set function; establishing an equilibrium equation of geometric nonlinear topological optimization, and solving structural response under the condition of large deformation through geometric nonlinear finite element analysis; approximating and simplifying local volume constraint through a local constraint domain and a p norm function; establishing a topological optimization model based on geometric nonlinear porous filling rigidity maximization; performing sensitivity analysis through an adjoint method, and calculating a partial derivative of an objective function of the topological optimization model to an expansion coefficient so as to determine an optimization direction of each finite element in geometric nonlinear porous filling stiffness maximization; and carrying out iterative optimization to obtain an optimized topological structure. According to the method and the device, porous filling topological optimization under geometric nonlinear conditions can be realized.

The invention discloses a collaborative planning method for a regional integrated energy system, and the method comprises the steps: firstly collecting regional historical energy consumption data and various external factor data, and predicting an energy demand through employing a gray prediction and multiple linear regression combined algorithm; exploring existing energy supply facilities in the region and evaluating renewable energy potential; determining a candidate type of the integrated energy system component according to the result, establishing an optimal configuration model, and solving through a particle swarm optimization algorithm; constructing an energy network layout planning model, and determining an optimal topological structure and parameters of the network; establishing a dynamic operation model, and formulating a collaborative operation strategy by using a model prediction control algorithm; and finally, comprehensively evaluating the planning scheme, and analyzing and adjusting the scheme by utilizing sensitivity according to an evaluation result. According to the method, multiple factors are comprehensively considered, and the problems that in existing regional integrated energy system planning, demand prediction is not accurate, resource evaluation is not comprehensive, component configuration and network layout lack optimization collaboration, and the operation strategy is single are effectively solved.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, and the method comprises the steps: constructing an accident chain knowledge graph and a physical trigger graph, and building a causal relationship and physical constraints among equipment, states, events and consequences; gathering and mapping historical records, expert rules and online observation data into map entities and relationships, forming a baseline accident scene and calculating a baseline index; generating candidate paths by utilizing graph reasoning and coupled multi-physics field simulation, and realizing a closed loop of graph reasoning and physical solution through consistency check; performing scoring and disturbance simulation analysis on the candidate paths, and screening robust target paths; and carrying out high-fidelity simulation on the target path, identifying key nodes in combination with sensitivity analysis and a minimum cut-off set, and generating disposal suggestions and action priorities. According to the method, high-credibility prediction of accident evolution and emergency response closed-loop linkage are realized, and the method has high precision, high robustness and engineering implementability.

A method for reducing mDON concentration in wastewater, including a) acquiring a kinetics associated with production and consumption of a mDON of an activated sludge system, and importing a kinetic expression of the mDON into a conventional activated sludge model No. 1 (ASM1) to build a kinetic equation for the mDON; b) inputting component variables, parameter variables, model matrices, process rate equation and operating parameters of a predictive model into a simulation software to build an ASM-mDON model; c) inputting initial values of the component variables and the parameter variables into the simulation software for model initialization; d) acquiring initial mDON kinetic and sensitivity analysis results, selecting corresponding parameters, calibrating kinetic and stoichiometric parameters of the ASM-mDON model using a parameter estimation function of the simulation software; and e) replacing the initial values of the ASM-mDON model with optimal values obtained in d).

The invention discloses a large language model knowledge base self-adaptive updating method combining documents and interactive feedback, and relates to the technical field of knowledge base updating, and the method comprises the steps that a real-time change detection module monitors streaming documents and interactive feedback, and captures and marks a single fact change event; the decision module calculates a shunting decision coefficient according to the heat density index and the linkage sweep index, and the event is shunted to a Micro-Edit or Incremental-Tune path; if shunting to the Micro-Edit path, the positioning module executes attention backtracking and gradient sensitivity analysis, and determines a to-be-updated weight address; the rank-one editing module implements rank-one updating and records an audit log; the verification module generates an online token or a rollback instruction through consistency comparison; the version management module manages a version, activates a new version route and cleans out expired branches. Through event granularity identification, intelligent shunting, local updating and multi-version management, the timeliness of knowledge base updating is remarkably improved, and resource consumption and maintenance cost are reduced.

The invention relates to a machining path planning system, in particular to the technical field of intelligent manufacturing and numerical control machining, and overcomes the defect that a thermal coupling effect is ignored in traditional geometric planning by constructing an evolution model fused with a physical law. A dynamic decoupling algorithm is utilized to extract a real cutting load from multi-source data and reconstruct a rigidity field changing along with material removal in real time, the problem that a physical boundary is difficult to perceive under a complex working condition is solved, and the system effectively eliminates size out-of-tolerance caused by heat accumulation through transient deformation advanced prediction. According to the method, the non-linear error is actively counteracted in cooperation with a reverse compensation strategy based on sensitivity analysis, the machining precision and surface quality of the thin-wall weak-rigidity part are remarkably improved, the rejection rate and trial cutting cost are reduced, and intelligent closed-loop control over the manufacturing process is achieved.

The invention discloses a method and system for flexibly formulating a product carbon footprint model based on LCA, and relates to the technical field of environmental sciences, and the method comprises the steps: determining a carbon footprint evaluation demand, and constructing an evaluation framework according to the demand; dividing the LCA data into data sets according to life cycle stages; collecting carbon emission data in a life cycle stage in real time; constructing an LSTM model for predicting the carbon emission trends of the product in different life cycle stages; and carrying out carbon footprint accounting, hotspot analysis, sensitivity analysis and uncertainty evaluation based on the LSTM model. According to the method for flexibly formulating the product carbon footprint model based on the LCA provided by the invention, flexible carbon footprint evaluation is realized through a modularized data platform and an adjustable system boundary; by combining the Internet of Things and the AI algorithm, the data acquisition accuracy and analysis efficiency are improved, manual intervention is reduced, carbon emission trend prediction and multi-dimensional analysis are realized, and the evaluation precision and decision support capability are improved.

The invention relates to the technical field of cost assessment, in particular to a civil construction project cost assessment optimization system, which comprises a feature analysis module, a cost estimation module, a real-time monitoring module, a model training and tuning module, a risk analysis module, a decision support module, a financial simulation module and a reinforcement learning cost adjustment module. According to the invention, a convolutional neural network algorithm and a principal component analysis method in the feature analysis module excavate building information model data and screen key features, and the cost estimation module combines a multi-layer perceptron neural network and a linear regression model to enhance the cost mode analysis capability and calculate the project cost. The digital twinborn technology and the dynamic programming algorithm realize tracking of project progress and real-time monitoring of cost change in the real-time monitoring module, and the genetic algorithm and the machine learning algorithm realize continuous optimization and self-learning of model parameters in the model training and tuning module. And a Monte Carlo simulation method and a sensitivity analysis method are used for identifying and evaluating project risks in the risk analysis module.

The invention relates to a multi-target energy-saving optimization method for a propylene rectifying tower based on state simulation and an improved wolf pack algorithm, which comprises the following steps of: S1, on the basis of a rectification process balance-level steady-state model, carrying out production working condition state simulation on the propylene rectifying tower based on a PRO / II process simulation platform, and establishing a propylene rectifying tower state simulation model; s2, on the basis of the state simulation model of the propylene rectifying tower, carrying out sensitivity analysis of single parameters and combined parameters on the operation parameters of the propylene rectifying tower, and establishing a data relation table between the propylene yield of the propylene rectifying tower and the raw material feeding temperature as well as between the energy consumption of the propylene tower and the tower top reflux quantity; s3, performing model parameter identification on the propylene rectifying tower data relation table by adopting a multiple regression analysis method and a least square method, and establishing a propylene tower multi-target energy-saving optimization model; s4, setting penalty factors to construct penalty function items, and converting the constrained multi-objective optimization function of the propylene tower into an unconstrained multi-objective optimization function; s5, in order to improve the local search capability of the optimization algorithm, the convergence speed is accelerated, the algorithm is prevented from falling into local optimum, and a dynamic search factor is added to improve the optimal solution search capability of the wolf pack algorithm; s6, executing an improved multi-objective wolf pack algorithm to solve an unconstrained multi-objective energy-saving optimization function of the propylene rectifying tower; and S7, judging whether the current maximum number of iterations reaches an upper limit value or not, if so, outputting population non-dominated individuals as a propylene tower multi-target energy-saving optimization result, and otherwise, returning to S4 to continue iterative calculation.

The invention relates to the technical field of big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.

The invention discloses a face gear worm grinding machine error compensation method based on sensitivity analysis and a multi-source error mapping model, which comprises the following steps of: 1, constructing a multi-source error-tooth surface error mapping model based on a vector decomposition and truncation function method, the truncation function method is used for quantitatively describing propagation mechanisms of geometric errors and thermal errors in a machining chain and influences of the propagation mechanisms on tooth surface deviation, and high-order error terms are eliminated to improve calculation efficiency; 2, responding to the output of the mapping model, applying an improved Morris sensitivity analysis method, distributing a weighting coefficient higher than a geometric error component for a thermal error component, and calculating a weighting sensitivity coefficient so as to identify a key error component which has the greatest influence on the tooth surface precision; and 3, based on the key error component, a motion axis compensation value is generated through an error compensation model, and the compensation value is input into a machine tool control system to adjust the relative position of the worm grinding wheel and the workpiece, so that the tooth surface error is reduced.

The invention provides a quantum-classical multi-stage optimization method for unit commitment and transmission line switching, and the method comprises the steps: decomposing an original model into a plurality of modules, introducing consensus variables between nodes into the modules, further decomposing the modules into a plurality of sub-problem models, and coordinating the unit power generation and transmission line switching through the external circulation between the modules and the internal circulation of the modules, and the optimal unit scheduling scheme of the power system and the corresponding optimal power grid topology can be obtained. According to the method, a main problem for optimizing discrete variables is converted into an equivalent Hamiltonian in a secondary unconstrained binary optimization form suitable for quantum calculation, the Hamiltonian is efficiently solved through a quantum computer, and the solving speed is increased; the modules of the method are subjected to efficient iterative interaction through cutting plane Hamiltonian, so that the global search capability is improved; according to the method, a sensitivity analysis theory is applied to the construction of the cutting plane, and the effectiveness of the cutting plane and the convergence of the method are ensured; the optimal unit scheduling and the power grid topology can be quickly searched, and the power generation energy consumption is reduced.

The invention discloses an unstructured pruning compression lightweight speech recognition method based on second-order information, and the method comprises the following steps: carrying out the sensitivity analysis based on a Hessian matrix on different components of each layer of a target speech recognition model, and carrying out the mixed sparsity pruning compression search; searching the model layer by layer from shallow to deep according to the obtained sparseness, calculating the contribution of each parameter to the model performance according to the input data and the values of the model parameters, cutting off the parameters with the bottom contribution, and updating the residual parameters; and generating a final pruning compression model by adopting cyclic pruning, pruning to a relatively low sparseness lower than the target sparseness, and then finely adjusting the model so as to correct the model parameters to the optimal performance. According to the method, unimportant parameters can be efficiently removed from the pre-trained large voice model on the premise of not sacrificing the performance, the calculation and storage requirements of the model are reduced, and the good performance is achieved under the high sparse condition.

The invention discloses a notch position blade profile tolerance point taking device, system and method, and the method comprises the steps: enabling an actual measurement point cloud to be aligned with a theoretical model through progressive spatial registration, carrying out the initial coarse registration, and dynamically adjusting a root mean square error threshold value and a curvature change rate threshold value based on curvature sensitivity analysis, so as to achieve the self-adaptive segmented interruption control; a three-dimensional Hash mapping engine is constructed to store original point cloud data, an incremental data access mode and an iterative data structure are combined, a historical transformation matrix stack, a B spline control point and an 8-bit state register are included, and data processing integrity and high efficiency are ensured; a genetic algorithm is adopted to optimize segmented B-spline surface fitting, and contours of an air inlet edge and an air outlet edge are synchronously optimized through transverse segmentation and longitudinal segmentation; and finally, composite convergence verification is passed. According to the method, through dynamic threshold regulation and control and a hybrid optimization strategy, the detection precision and the calculation efficiency of the complex curved surface are remarkably improved.

The invention discloses a gear machining multi-source thermal error compensation system based on tensor compression edge deployment, which fuses physically guided multi-source error modeling, tensor-based model compression, edge deployment and bandwidth sensing signal scheduling to realize low-delay and high-precision error compensation. The method comprises the following steps: firstly, establishing a three-layer system architecture comprising a cloud layer, an edge layer and a sensing layer, supporting distributed model training, edge reasoning and error compensation so as to support closed-loop operation for realizing adaptive scheduling with extremely low delay, and mainly comprising: (1) a multi-source error model, thermal and geometric errors are fused into coupling high-order representation for capturing space-time interaction through an analytical method; and (2) establishing a sensitivity analysis model based on a physical guidance tensor mode decomposition method (PG-TMDM), identifying a key error source through a three-order tensor structure, and calculating a physical perception contribution rate to retain physical interpretability. And in order to realize real-time deployment, deploying to the edge layer through model compression, model packaging and an edge deployment strategy.

The invention discloses a parallel computing simulation method for thermal exploitation of a buried pipe well group in a geothermal reservoir, which comprises the following steps of: dispersing a grid volume unit of a buried pipe by adopting a parallel finite element unit method, and simplifying the buried pipe and circulating fluid in the pipe into a one-dimensional linear finite element unit; constructing a plurality of pipeline flow models arranged in an array, and combining the in-pipe one-dimensional linear units and the out-pipe three-dimensional volume units to obtain a well group type pipeline flow geometric model; a parallel finite element unit method is adopted to be combined with a non-isothermal pipeline flow physical field, an underground water seepage physical field and a porous medium heat transfer physical field to conduct multi-physical-field coupling, and a well group type pipeline flow numerical model is obtained; fluid flow and convective heat transfer in the buried pipe and pipe wall heat conduction are solved in a combined mode; and performing factor sensitivity analysis by changing different thermophysical parameters of the rock-soil body and heat exchange characteristic factors of the buried pipe well group. The invention provides a new method for researching fluid flowing and heat transfer behaviors of the buried pipe well group, and provides a new idea for optimal design and efficient simulation of thermal exploitation of the buried pipe well group in a geothermal reservoir.

The invention relates to an active power regulation and control method and system based on boundary constraint, and the method comprises the following steps: constructing a boundary constraint condition model, and achieving the dynamic updating of the boundary constraint condition model through a visual maintenance tool; the load rate of the power grid equipment is monitored in real time, whether the power grid equipment is out of limit is judged according to the scene definition table, working condition classification is performed, and generation of a control strategy is triggered; a control strategy is generated based on sensitivity analysis and resource priority, and a comprehensive index calculation model is adopted to screen and adjust resources according to a strategy constraint definition table; sensitivity analysis obtains a sensitivity coefficient based on real-time topology of power grid equipment in combination with load flow calculation, and the resource priority is determined according to a resource rule definition table; and when a single type of resources cannot meet the regulation and control requirements, generating a comprehensive control strategy containing multi-type resource regulation variables, and executing closed-loop effect verification and strategy iteration through a strategy index definition table.

The invention discloses a matrix decomposition and reconstruction method for a large model reasoning scene, and the method comprises the following steps: 1, carrying out the error statistics of conventional nodes of a network layer in a large model, and screening weight nodes capable of carrying out low-rank decomposition through a sensitivity analysis strategy; step 2, gradually performing low-rank setting, decomposing the weight matrixes in the weight nodes which can be subjected to low-rank decomposition and are obtained by screening in the step 1, and obtaining and recording respective optimal low-rank values of the decomposable weight matrixes; and step 3, performing network reconstruction on the original model based on the decomposable weight matrix and the corresponding low-rank value. The invention further discloses a system for implementing the method and a corresponding application, and the system and the application have a wide application scene.

The invention discloses a distributed power supply-containing power distribution network coordinated scheduling method, which relates to the field of power distribution network coordination, and comprises the steps of S1, multi-flexibility resource refined modeling and interaction characteristic analysis, S2, power distribution network linear load flow calculation and sensitivity analysis, and after the power distribution network linear load flow calculation and sensitivity analysis in S2 are completed, carrying out power distribution network coordinated scheduling. And S3, generating an uncertainty scene based on the generative adversarial network. According to the coordinated dispatching method for the power distribution network containing the distributed power supply, differences of various resources in control modes, response speeds and adjusting capabilities are analyzed through refined modeling, complementary characteristics of the resources are fully utilized by a collaborative optimization model, an overall optimization effect is achieved, a single type of resources are not optimized in an isolated mode, and the method is suitable for large-scale popularization and application. All adjustable resources such as a distributed power supply, an energy storage system, a reactive power compensation device, a flexible load and an intelligent soft switch are incorporated into a unified modeling and optimizing framework, and deep fusion and collaborative interaction of flexible resources can be realized.

The invention discloses a carbon dioxide geological sequestration quantity prediction method, system and device and a storage medium, and the method comprises the steps: obtaining a mathematical expression of carbon dioxide sequestration capacity through combining a coupling model of a flow field, a mechanical field and a thermal field with random parameters, and introducing a leakage risk coefficient; taking the weighted sum of the expected value of the storage capacity and the variance as an optimization objective function, and according to the mathematical expression of the carbon dioxide storage capacity, carrying out optimal solution solving on the carbon dioxide storage capacity to obtain an optimal storage strategy; performing spatial discretization on the control equation by adopting a finite element method, discretizing time by adopting a semi-implicit difference method, discretizing random variables by combining a random Galkin method, and solving a coupling model of a flow field, a mechanical field and a thermal field to obtain a prediction result of the carbon dioxide sequestration amount; and according to a prediction result of the carbon dioxide sequestration amount, outputting an expected value and a variance of the sequestration capacity, and generating a sequestration amount distribution diagram, a risk assessment diagram and a sensitivity analysis diagram.

The invention provides a sewage treatment process modeling control optimization method based on mechanism-data dual drive, and the method comprises the steps: collecting basic data of a sewage treatment process, constructing an ASM2d model of a sewage treatment plant, carrying out the sensitivity analysis and parameter calibration of ASM2d model parameters, constructing an environment and a data set, screening input features according to the data set, and carrying out the modeling control optimization of the sewage treatment process. A training set and a test set are divided according to a proportion, an ASM2d-LSTM mixed water quality prediction model is established based on an ASM2d model, the ASM2d-LSTM mixed water quality prediction model is trained based on the training set, an MPC control framework is integrated on the basis of the trained ASM2d-LSTM mixed water quality prediction model, an optimized operation sequence is generated by using the model, and the operation sequence is optimized. And evaluating the control effect of the optimization strategy by taking the collected real control parameters as a reference. According to the method, through a series of programs such as ASM2d-LSTM sewage quality prediction model construction based on mechanism-data dual drive, MPC control based on energy consumption and carbon emission reduction target optimization, key index online optimization strategy output in the sewage treatment process is realized.