498 results about "Iterative search" patented technology

The invention discloses an intelligent construction method based on data driving, and relates to the field of intelligent construction, and the method comprises the steps: collecting multi-source heterogeneous data of an intelligent construction project; performing feature selection on the acquired multi-source heterogeneous data by adopting a recursive feature elimination (RFE) algorithm to obtain a feature subset reflecting project progress, quality and safety; according to the feature subset, a multi-target model used for predicting project progress, quality and safety is constructed through a support vector machine (SVM) algorithm; a non-dominated sorting genetic algorithm NSGA-II is adopted to solve the multi-target model, a simulated annealing strategy is introduced in the iterative search process of the NSGA-II to jump out of a local optimal solution, and an optimized Pareto optimal solution set is output; and selecting an optimal parameter combination according to the Pareto optimal solution set, and adjusting the BIM model of the current intelligent construction project by using the optimal parameter combination. In view of low multi-objective optimization efficiency in intelligent construction project management in the prior art, the optimal model parameters considering different management objectives are obtained, and the efficiency is improved.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention discloses a game reinforcement learning-based reactive power dispatching method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: collecting node voltage, line current, electric vehicle charging and discharging states and topological information, and constructing graph attention embedding; generating an initial reactive power regulation action in the multi-agent game reinforcement learning framework, and shaping and updating a strategy gradient through double rewards; the improved particle swarm is initialized according to the updating strategy gradient, and a self-adaptive inertia coefficient is set; iteratively searching according to the node voltage sensitivity, the line reactive margin and the expected convergence step number to obtain an optimized particle swarm; the particle speed is mapped to a main strategy network through online cooperative training, the inertia coefficient is synchronously adjusted, a cooperative optimization strategy is output, a reactive power dispatching instruction is generated, and the algorithm is updated in a closed-loop mode according to real-time feedback. According to the invention, rapid and cooperative reactive dynamic scheduling of an electric vehicle group is realized, and the voltage stability and the electric energy quality are remarkably improved.

The invention relates to an environment adaptive radio measurement calibration method and system. The method comprises the following steps: synchronously sampling temperature, humidity, air pressure and electromagnetic interference around radio equipment to obtain an environmental parameter vector; constructing a linear model and a nonlinear target model of a three-layer neural network structure according to the environmental parameter vector; solving an environmental parameter separation vector set by using the linear model and the nonlinear target model; executing one-dimensional iterative search optimization on the environment parameter separation vector set to obtain a target environment parameter vector; and inputting the target environment parameter vector into the nonlinear target model for calibration compensation calculation to obtain dynamic calibration compensation data. According to the method, the updating frequency is reduced when the environment is stable, the calibration precision is ensured, the system resource use is optimized, and the method is particularly suitable for wireless equipment with limited resources.

The invention relates to the technical field of PCB processing, and discloses a multi-layer circuit board drilling method and device based on machine vision, and the method comprises the steps: obtaining a multi-layer circuit board image; preprocessing the multi-layer circuit board image to obtain a to-be-identified image; inputting the to-be-recognized image into a pre-trained defect detection model to obtain defect position data; carrying out point location identification and matching on the to-be-identified image to obtain an optimal hole location coordinate, and carrying out correction according to the optimal hole location coordinate to obtain hole location information data; according to the hole site information data and the defect position data, nodes are constructed respectively, distance calculation is carried out, and a hole site node graph is generated; according to the hole site node graph, performing path planning by applying an ant colony algorithm, and performing iterative search to obtain an optimal drilling path; and drilling the multilayer circuit board according to the optimal drilling path. The method has the following effect that the drilling efficiency of the multilayer circuit board can be improved.

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention provides an intelligent analysis method and system for territorial space planning data, and relates to the technical field of data processing, and the method comprises the steps: recognizing an overlapping region through comparative analysis; performing buffer area analysis on the geographic information data, the land utilization data and the urban planning data, and creating buffer areas for rivers, roads and protection areas to identify conflict areas; according to the overlapping region and the conflict region, a corresponding territorial space planning scheme is found through iterative search; performing pattern recognition on the territorial space planning scheme to obtain a planning risk assessment result; according to the planning risk assessment result, the risks are divided into different categories, and each risk is quantified to obtain a specific score of each risk category; and obtaining an overall risk assessment result of the whole territorial space planning scheme according to the scores of the various risks. According to the invention, the accuracy and efficiency of risk assessment can be improved.

A neural network on-chip mapping method and apparatus based on a tabu search algorithm are provided. The method includes: constructing a tabu search table and using a heuristic-based iterative search process to select local computing cores of a network-on-chip as candidates, establishing an integer programming model and solving an optimal solution, continuously reducing an objective cost function of a mapping solution by loop iteration, and finally obtaining an approximately optimal deployment scheme.

The present disclosure describes methods, systems, apparatus, and media for object identification and classification, utilizing multi-feature and multi-modal data. This includes shape, material, brand, price, odor, taste, tactility, and sound. The system integrates a server space for data processing, a querying device for iterative searches, and a data interface module for refining results. It features AI-driven image optimization, feature extraction, and pattern recognition, employing novel techniques for fusing multi-feature and multi-modal embeddings utilizing multi-head attention. Additionally, a linker module powered by two active learning with feedback loops AI models consolidates scattered data into a unified object information database. The system also employs novel AI algorithms for isolating the object of interest through a saliency map and semantic analysis, as well as for enhancing raw images with a GAN-autoencoder.

The invention discloses a factory network river-lake integrated scheduling method and system based on a butterfly optimization algorithm, and solves the problems that resource scheduling is not systematic and the optimization effect is poor in urban water environment management. The method comprises the following steps: firstly, collecting and sorting data information such as a rain sewage pipe network, pump gate characteristics and a water system in a research area, constructing one-dimensional and two-dimensional hydrodynamic models, and simulating dynamic behaviors of a drainage system; on the basis, a meta-heuristic butterfly optimization algorithm is adopted to construct an optimization scheduling model, and an optimization scheme conforming to a scheduling target is generated through the steps of population initialization, fitness calculation, iterative search of an optimal solution and the like. In the optimization process, scheduling parameters are adjusted in combination with real-time monitoring data, and the effectiveness and practicability of the scheme are ensured.

The invention discloses a traffic scheduling method and an electronic device, and relates to the technical field of traffic scheduling, and the method comprises the steps: determining the priority weight of a micro-service, and predicting a target traffic according to the historical traffic information of a network device; constructing a graph model according to the topological information of the network equipment and the dependency relationship of the micro-service, and performing embedded learning on nodes in the graph model to generate a state vector representing a network state; the priority weight, the state vector and the target traffic of the micro-service serve as input of a reinforcement learning model, and a traffic scheduling strategy of the network equipment is obtained; performing iterative search according to iterative particles formed by encoding the strategy network parameters of the reinforcement learning model and the feature learning network parameters of the graph model to determine reinforcement learning model parameters; and issuing the traffic scheduling strategy to the network equipment and executing the traffic scheduling strategy so as to solve the technical problem that a traffic scheduling method in related technologies is difficult to adapt to a dynamic and complex network environment and service requirements under a micro-service architecture, and the reliability of traffic scheduling is improved.

The invention relates to the technical field of railway line selection design, in particular to a railway line searching method, medium and equipment in a wide and dense obstacle environment. The method comprises the following steps: acquiring obstacles passed by a line and determining preferentially treated obstacles; and inserting an optimal line intersection point around the obstacle, adjusting the line scheme and repeating the point insertion operation until the spatial relationship requirement of the line and the obstacle is met, and finally generating a global optimal line conforming to the constraint. According to the method, through a priority obstacle handling strategy and a dynamic point insertion mechanism, key obstacles are intelligently recognized, intersection points are preferentially generated around the obstacles, the problem of circuit bypassing redundancy or repeated adjustment caused by an improper obstacle handling sequence in a traditional method is avoided, and the efficiency and quality of circuit scheme design in a complex environment are remarkably improved. Through point-by-point insertion type iterative search, it is ensured that each adjustment approaches to the global optimum direction, and compared with a traditional trial and error method, the calculation time consumption is reduced, and meanwhile local optimum is avoided.

The invention discloses a multi-energy complementary cooperative scheduling method based on an improved step-by-step optimization algorithm, and belongs to the technical field of water, wind and light storage multi-energy complementary scheduling. The method comprises the following steps: acquiring hydrological, engineering and load data, and generating a preliminary feasible scheduling solution; carrying out iterative search by adopting an improved step-by-step optimization algorithm (POA), wherein the improvement comprises adopting a variable discrete step size, a dynamic feasible region and combining a heuristic candidate update strategy; combining the output sequence of each subsystem, and introducing a dynamic credit value based on nonlinear updating to carry out weighted redistribution on the load deviation; and finally, evaluating and selecting a scheme with the maximum peak regulation benefit through a scheduling objective function coupled with a dynamic penalty function. According to the method, a credit mechanism, a gradient dead zone solution and the like are introduced, so that the intelligent level of scheduling, the calculation stability and the economic benefit of a final scheme are remarkably improved.

The invention belongs to the technical field of parameter identification, and provides a thermal power generating unit electro-hydraulic servo system PID parameter identification method and system, and the method comprises the steps: obtaining the historical operation data of a steam turbine, carrying out the preliminary screening of the data, and constructing a parameter identification data set; according to the mean square error between the actual value and the calculated value of the opening degree of the valve, an objective function of PID parameter identification of the DEH electro-hydraulic servo system is established; setting population initial parameters, and generating a chaotic initial population; based on a parameter identification data set and a target function, introducing an initial population value of the generated chaotic initial population into an improved Bayesian optimization model, and obtaining a preliminary optimization result through Gaussian modeling and kernel function prediction; and taking the preliminary optimization result as input, and utilizing a whale optimization algorithm introducing a Levy flight disturbance mechanism to carry out iterative search for multiple times to obtain a final optimization result. According to the invention, the parameter identification precision and robustness are improved.

The invention belongs to the related technical field of task scheduling optimization, and discloses a transport vehicle operation path and scheduling optimization method and system, and the optimization method comprises the steps: generating a path library, carrying out the global search of a particle swarm search algorithm on the basis of the path library, and during the execution of the particle swarm search algorithm, on the basis of a current global optimal solution, carrying out the global search of the particle swarm search algorithm; circularly executing local search, namely generating a new solution by using a damage and repair strategy and performing conflict adjustment, then updating a global optimal solution based on the new solution, then updating particle positions and speeds, and performing particle swarm iterative search again, namely executing global iteration; and after the number of iterations is reached and an optimal solution is obtained, decoding is carried out to generate a scheduling scheme, that is, tasks in the task set are allocated to each transport vehicle, a vehicle scheduling scheme and path selection are generated, and transport vehicle operation path and scheduling optimization for solving space-time conflicts by multiple vehicles and multiple task points is efficiently realized.

The invention discloses a wireless power transmission coil optimization method and a related system, which can realize automation of coil structure parameters and multi-target global optimization by acquiring a coil geometric parameter space, constructing a comprehensive optimization function and performing iterative search in the coil geometric parameter space. The problems that in traditional design, the number of simulation iterations is large, design efficiency is low, local optimum is prone to occurring, and multiple performance indexes are difficult to balance are effectively solved, coil design efficiency and precision are remarkably improved, and dependence on artificial experience is reduced. Therefore, the number of manual intervention and simulation is remarkably reduced, the coil design efficiency is improved, and the prototype development period of the WPT system is shortened. Besides, iterative search is carried out in the parameter space, so that the whole design space can be effectively explored, a locally optimal solution trap can be jumped out, and a globally optimal or approximately globally optimal coil structure parameter combination can be obtained more possibly.

The invention discloses a shield tunnel settlement prediction method based on artificial intelligence. The method comprises the steps of data acquisition, data optimization, construction of a shield tunnel settlement prediction model, model optimization and settlement prediction. The invention relates to the technical field of tunnel construction monitoring, in particular to a shield tunnel settlement prediction method based on artificial intelligence. According to the scheme, data cleaning is carried out by defining a dimension refining distance function and calculating gravitation between data, potential relations between the data are deeply mined, noise interference is reduced, and the accuracy and reliability of the data are improved; by designing a mapping function, a circulation control layer, a depth correlation layer and a loss function, the recognition and prediction precision of the model on a complex settlement mode is improved; model parameters are dynamically searched by designing an intervention factor, defining an adaptive step length, performing bidirectional iterative search and gradually adjusting search, so that the search efficiency is improved, time consumption and a loss value of model training are balanced, and the model is more efficient and stable.

The invention relates to the technical field of power electronic converters, and discloses a grid-connected inverter multi-parameter online setting method, system and device and a medium. The method comprises the following steps: establishing a circuit simulation model of the grid-connected inverter; output currents of the grid-connected inverter under different power grid impedance working conditions are obtained through the circuit simulation model, and target control parameters of the grid-connected inverter under different power grid impedance working conditions are obtained through iterative search by adopting a particle swarm optimization algorithm through the circuit simulation model; performing off-line training on the parameter reasoning model through the output current of the grid-connected inverter under different power grid impedance working conditions and the target control parameter under the corresponding power grid impedance working condition; acquiring an output current of the grid-connected inverter in real time, and inputting the output current acquired in real time into the trained parameter reasoning model to obtain a corresponding target control parameter; and on-line setting is performed on the control parameters of the grid-connected inverter through the target control parameters output by the parameter reasoning model, so that rapid setting can be realized.

The invention discloses a high-dimensional vector approximate nearest neighbor retrieval method for persistent storage. The method comprises an index construction stage and a search stage. In the index construction stage, a clustering algorithm is adopted to carry out space division on a data set, a center replacement strategy is introduced to select a representative vector for establishing an entry point diagram, and meanwhile, a second-layer neighbor graph is constructed on the original data set; in the search stage, the query vector is quickly positioned to the closest entry point by using the result of the entry point diagram, so that the search path is shortened. According to the method, the optimal product quantization coding strategy is adopted, so that the SSD data loading overhead is reduced; a multi-beam search strategy is adopted to effectively utilize the page-by-page reading characteristic of a file system, so that the time overhead is reduced, and the data loading amount is increased; and an iterative search strategy is adopted to collect query vector search features, the search process is self-adapted, and the search efficiency is improved. Aiming at mixed architecture challenges of large-scale data, the invention provides an efficient search solution for optimizing approximate nearest neighbor search.

An unmanned aerial vehicle assisted MEC network energy efficiency optimization method based on reinforcement learning belongs to the field of mobile communication, and comprises the following steps: firstly, carrying out system modeling and state information collection, inputting state information to a strategy model, generating flight and energy transmission actions, and carrying out unmanned aerial vehicle movement and service equipment selection; an energy transmission and task unloading model is defined, and a gradient-based equipment unloading time distribution algorithm is started; then initializing binary search of the equalization marginal utility gradient, executing single iterative search of the equalization marginal utility gradient, updating a search interval of the equalization marginal utility gradient, and completing search of the equalization marginal utility gradient; then determining a final time allocation scheme, executing energy transmission and task unloading, collecting performance data, calculating a reward function and storing experience; and finally, updating the Critic network, updating the Actor network and softly updating the target network, and obtaining an optimal strategy after iterative optimization. The overall energy efficiency and task processing throughput of the system are remarkably improved.

The invention discloses a model quantification method and system based on stability scoring, and the method comprises the steps: calculating the output features of each layer of an original model and the probability distribution of the output features of each layer of a quantification model, calculating the stability value of each layer of the quantification model according to the probability distribution, and evaluating the stability of each layer of the model through the stability value. Bit width is distributed according to characteristics of each layer, the method does not depend on labels, is only based on network forward activation information, and simultaneously measures output distribution difference and direction consistency before and after quantization, and a hierarchical stability measurement index can be constructed according to a stability value to accurately identify a steady-state layer and a high-sensitivity layer for guiding precision distribution. The bit width is adaptively reduced by adopting an iterative search strategy on the basis of ascending sorting of the stability value scores; on the premise that precision and resource constraints are met, the model compression ratio is increased to the maximum extent; retraining is not needed in the whole process, and large-scale network one-key quantitative deployment is supported.

The invention belongs to the technical field of robot path planning, and provides a mobile robot three-dimensional path planning method based on an adaptive ant colony algorithm, and the method comprises the steps: determining a starting node and a target node in a rasterized three-dimensional search space; iteratively searching the optimal path from the starting node to the target node, and outputting the optimal path from the starting node to the target node obtained by the last iterative search; each iterative search comprises the following steps of: A, putting all ants to an initial node; b, generating an attenuation factor which is in negative correlation with the number of iteration search times, and adjusting heuristic information among the nodes by using the attenuation factor; c, each ant selects the next node according to the heuristic information between the nodes and the pheromone concentration between the nodes; d, updating the pheromone concentration of the road section passed by the ants; the invention further discloses a three-dimensional path planning device for the mobile robot, a computer program product and the mobile robot. According to the method, the convergence and efficiency of path search are improved, and the three-dimensional path planning time is shortened.

The invention discloses a cable cabling control method based on process big data, and belongs to the technical field of cable cabling control. Comprising the following steps that S1, parameters are collected in real time and preprocessed, historical batch records are called, the tension instability and the arrangement offset trend rate are calculated, simulation coupling factors are constructed, and an optimized coefficient combination is obtained; s2, the current tension instability and the arrangement offset change rate are calculated respectively, and the adjusted take-up speed and the adjusted twisting torque are obtained; according to the method, historical batch records of cables of the same specification are called, the tension instability and the arrangement offset trend rate are calculated, a numerical optimization algorithm is adopted to iteratively search an amplification coefficient and a sensitivity coefficient, a process-oriented optimization objective function is minimized, an optimized coefficient combination is obtained, and the stability of the cable tension is improved. Therefore, self-adaptive parameter optimization based on historical data is realized, the high adaptability of a control strategy to different batches of process fluctuations is improved, and the variability of quality indexes is remarkably reduced.

The invention relates to the technical field of low-altitude safety, and provides an aircraft optimal trajectory screening method and system based on adjustable phase quantum search. Candidate trajectories are generated according to flight mission constraints, cost values are calculated, and Boolean markers are distributed based on cost threshold values to identify high-quality solutions; performing iterative search and measurement on the balanced quantum superposition state of the coding track index by utilizing a Grover quantum search algorithm based on the adjusted search phase to obtain an output index; and mapping the index into a track, obtaining an optimized track through classic local optimization, adaptively adjusting the phase parameter and the cost threshold value based on performance evaluation, and repeatedly executing until an end condition is met, thereby determining an optimal track. On-line reconfiguration of a strategy is realized by introducing an adjustable phase mechanism, a feedback closed loop is established to realize adaptive optimization of parameters, and the real-time requirements of scenes such as urban air traffic control and unmanned aerial vehicle clusters are met.

The invention relates to the technical field of wireless communication, in particular to a reachable rate optimization method based on an intelligent reflecting surface, which comprises the following steps of: firstly, constructing a channel model in an application scene, namely designing a system reachable rate optimization model on the basis of establishing a base station-user, base station-IRS and IRS-user three-link model; then, a phase shift matrix of the IRS and an emission covariance matrix of a base station serve as joint optimization variables, a population is divided into an elite subpopulation, a suboptimal population and a common population through a subpopulation grouping strategy of an improved particle swarm algorithm, each particle represents the composed variable, a dynamic cooperation strategy is proposed to calculate the speed in each subpopulation, and the speed in each subpopulation is calculated; and updating individual optimization and group optimization, and continuously carrying out iteration to search a reachable rate optimal solution of the user. According to the method, local search can be finely carried out while large-range global search can be carried out, the calculation complexity is remarkably reduced, and the IRS transmission effect is improved.

The invention discloses a dynamic hierarchical encryption system for multi-tenant e-commerce data, and relates to the technical field of multi-tenant data security, the system comprises an encryption management center, the encryption management center is in communication connection with the following modules: a data sensing and grading module, which is used for collecting multi-source context data of an e-commerce platform, and sending the multi-source context data to the encryption management center; performing basic sensitivity level division on the context data; and the multi-tenant knowledge graph construction module is used for constructing a multi-tenant knowledge graph based on the tenant-data-authority association network in combination with the multi-source context data. According to the method, dynamic self-adaptive adjustment of encryption strength is realized through cooperation of the fruit fly algorithm and the knowledge graph, the system dynamically optimizes combination of key length and the encryption algorithm based on data grading labels and real-time context data, and the fruit fly algorithm searches an optimal solution through iteration, so that the system has high encryption efficiency while ensuring compliance. And encryption delay is controlled within a real-time requirement range, so that the user experience in a multi-tenant scene is remarkably improved.

The invention discloses a pixelated matching circuit design and optimization method based on a large language model, and relates to the technical field of radio frequency circuits, and the method comprises the steps: obtaining a target demand and a physical constraint of a matching circuit; generating an initial topological structure based on the target demand and the physical constraint, and performing parameter estimation on the initial topological structure to obtain an initial binary matrix constrained by the initial topological structure and the parameter; obtaining an optimization problem based on the initial binary matrix and the target demand; wherein the optimization problem comprises distribution of optimization parameters, optimization objectives and solution spaces; and obtaining a key performance index and an optimization algorithm according to the optimization problem, and carrying out iterative search in the optimization algorithm according to the key performance index to obtain an optimal solution for solving the optimization problem so as to generate an optimal pixelated topology. According to the method, the optimal pixelated topology can be generated, and efficient global optimization is realized.

The invention provides a photovoltaic energy storage joint optimization scheduling method and system based on deep reinforcement learning, and relates to the technical field of energy management, and the method comprises the steps: collecting multi-source heterogeneous time series data, extracting energy storage health state features through a dual codec network and a recurrence plot convolution network, dividing an energy storage power interval, and constructing a feasible region; based on the power difference value sequence, extracting fluctuation characteristics in a segmented manner, and constructing a risk evaluation index; and generating a plurality of search paths in the feasible region, obtaining an energy storage charging and discharging power time sequence through iterative search, and generating a scheduling instruction. According to the method, light storage joint optimization scheduling under the energy storage safety constraint is realized, and the photovoltaic consumption rate and the system economy are improved.

The invention discloses an automatic wavelength tuning method and system for a tunable laser based on particle swarm optimization, and is applied to the technical field of wavelength tuning of semiconductor tunable lasers. According to the method, a particle swarm optimization (PSO) algorithm is taken as a core, multi-target iterative search optimization is carried out on initial current-wavelength data, and in a tuning initialization stage, a system firstly carries out coarse scanning on three paths of current of a left reflecting grating region, a right reflecting grating region and a phase modulation region of a sampling grating distributed Bragg reflection laser; manual intervention is not needed, the system can complete efficient configuration screening in a given target wavelength range, the intelligent level of the tuning system is remarkably improved, good automation capacity and expansibility are achieved, scanning of a large number of invalid data and tedious operation procedures in the traditional tuning process are avoided, and the tuning efficiency is improved. And meanwhile, a high-quality current-wavelength lookup table can be quickly constructed, and the method has wide engineering application prospects in optical communication, sensing and other scenes.

The invention relates to the technical field of power electronics and direct current transmission, in particular to a two-level VSC-HVDC minimum direct current capacitance calculation method based on LMI. The method comprises the following steps: establishing a state equation comprising a main circuit and a controller dynamic model, converting a three-phase alternating current quantity into a direct current quantity under a synchronous rotating coordinate system by utilizing Park transformation, and deducing a system state equation in combination with a Kirchhoff's law; performing Taylor expansion and linearization on the nonlinear equation, and constructing a small signal model based on a Lyapunov stability theory; by separating a diagonal matrix and defining a stability criterion in an LMI form, minimum direct-current capacitance solving is converted into a convex optimization problem, a critical value is iteratively searched by adopting a dichotomy method to determine minimum capacitance, and meanwhile, a tolerance mechanism is introduced to improve the robustness of an algorithm. Through combination of mathematical modeling and an optimization algorithm, the calculation efficiency and accuracy are effectively balanced, and a theoretical basis is provided for reducing the cost and the size of the converter.