38 results about "Uncertain set" patented technology

The embodiment of the invention provides a virtual power plant cooperative control method and system and electronic equipment, and relates to the technical field of power distribution Internet of Things. The method comprises the steps of obtaining a control target; calculating a fluctuation coefficient according to historical data of the global state parameters, and determining an uncertain set; establishing a robust optimization model based on the uncertainty set, and solving the robust optimization model to obtain an optimal solution of a robust objective function; training a Bayesian deep reinforcement learning model by using the historical data; inputting the current state data of the global state parameters into a trained Bayesian deep reinforcement learning model, generating an optimal action, and calculating a corresponding confidence coefficient; and selecting one of the optimal action and the optimal solution of the robust objective function to generate a first control instruction according to the relationship between the confidence coefficient and a preset confidence coefficient threshold value, and issuing the first control instruction to a low-layer intelligent agent. According to the implementation mode provided by the invention, the virtual power plant can be promoted to develop in a more refined, intelligent and autonomous direction.

The application discloses a conformal calibration electricity price prediction method for computer-aided collaborative decision-making, and belongs to the field of power distribution network optimization; the method is applied to a collaborative system containing flexible resources on the computing power side and the power side, and comprises the following steps: in a training stage, a neural network is trained by using historical characteristic information, a conformal threshold is determined based on a conformal prediction theory, and a historical electricity price box type uncertainty set is constructed; the set is input into a differentiable convex optimization layer which is reconstructed from a robust scheduling model dual, and a total actual operation cost is taken as a loss function to perform decision focusing back propagation training on the prediction network. In a day-ahead scheduling stage, a trained prediction network and the conformal threshold are used to generate a calibrated day-ahead electricity price box type uncertainty set, and the set is input into the differentiable convex optimization layer again to obtain optimal scheduling decision variables containing the states of the two types of flexible resources and to issue control instructions. The application reduces the comprehensive operation cost of the system under the premise of guaranteeing uncertainty statistical coverage.

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

The application discloses a data-driven urban power grid multi-stage operation scheduling method and system, and establishes a mathematical model of a power distribution system coordination scheduling problem containing multiple energies; based on the obtained mathematical model of the power distribution system coordination scheduling problem, a linear budget constraint generation method based on historical data is used to construct a budget uncertainty set; based on the obtained budget uncertainty set, a multi-stage robust optimization method based on an implicit decision strategy is used to determine a coordination scheduling scheme; and the coordination scheduling of multiple resources and devices is adaptively optimized in a rolling time domain mode.

The invention discloses a post-disaster power distribution network recovery method based on communication-physical coupling modeling. The post-disaster power distribution network recovery method comprises the following steps: 1) coupling modeling: modeling a physical power distribution network and a communication network thereof together; 2) modeling a local distributed robust optimization model: based on an uncertain set defined by adopting a Wasserstein distance, constructing a regional-level distributed robust optimization model with optimization objectives of maximizing a load recovery amount and a load value and minimizing power generation cost and communication cost; 3) consistency variable design and region coupling: different regions establish consistency constraints through shared variables of shared boundary nodes to ensure the coordination consistency of key boundary states in distributed solution; 4) distributed iterative solution: performing parallel solution on the local optimization problem of each region by adopting an alternating direction multiplier method, and realizing global consistency through neighborhood shared boundary variables; and 5) scheduling result execution and feedback: issuing a final recovery decision of each region to field equipment for execution, and performing monitoring and iterative feedback on a recovery state.

The invention discloses a risk-driven high-proportion new energy power system electric quantity reserve decision-making method and system, and belongs to the technical field of power system operation. The method comprises the following steps: carrying out operation risk definition of a VRE admission domain for a power system, and learning a mapping relation between an uncertain set boundary and an operation risk for the power system by using a conservative sparse neural network CSNN after definition; constructing a risk-driven adaptive uncertainty set; based on the linearization model and the adaptive uncertainty set, establishing a risk look-ahead robust scheduling framework through offline training and online mapping modes; and using the risk prospective robust scheduling framework to identify the operation risk of the power system through an online mapping mode, generating a current power reserve decision based on the operation risk, and executing the power reserve decision. The method has good expandability and engineering feasibility, and can be directly integrated to an existing scheduling platform.

The invention discloses a multi-type standby resource collaborative optimization method and device considering source-load uncertainty, and the method comprises the steps: generating a plurality of new energy and load prediction error sub-databases based on data clustering, generating a rhombic convex hull uncertainty set based on the sub-databases, generating a standby demand curve set of a limit scene, and generating a system operation standby constraint; constructing a multi-type standby resource collaborative optimization model and a multi-type standby resource scheduling operation model which take minimum day-ahead stage cost and intra-day stage cost as targets and take system operation standby constraints as constraint conditions; and solving the multi-type standby resource collaborative optimization model to obtain the standby demand capacity of the system at each moment and a corresponding configuration scheme of the multi-type standby resources. According to the method, accurate quantitative evaluation of the system operation standby capacity demand and optimal configuration of standby resources are realized, and the new energy consumption is effectively promoted and the scheduling operation economy is improved while the sufficient standby margin of the system is guaranteed.

The invention discloses a data-driven spatial-temporal correlation uncertainty set construction method, and the method comprises the steps: obtaining a first data set according to the actual value and predicted value of the power generation power of a renewable energy unit in a historical time period; and based on the first data set, constructing an uncertainty set of temporal correlation constraint-spatial correlation constraint-residual constraint for the robust optimization model to call. According to the method, accurate modeling is carried out on the spatial-temporal correlation of the renewable energy unit by constructing a'time-space-residual 'multi-dimensional constraint system.

The invention discloses a demand side capacity two-stage robust planning method considering cost and flexible resources, and belongs to the technical field of power system operation and maintenance. According to the method, uncertainty adjustment parameters are introduced, a box type uncertainty set capable of flexibly adjusting conservative property is constructed, the inherent defect that a traditional robust optimization method is too conservative is effectively relieved, the model can dynamically adjust the boundary of the uncertainty set according to different bearing capacities of an actual system to risks, and the robustness of the system is improved. Therefore, refined balance of economy and reliability can be realized in the planning stage. According to the constructed two-stage robust optimization model considering the electricity-carbon coupling mechanism, the carbon transaction cost is taken as an important component of the operation cost to be incorporated into the target function, and the requirements of the national green low-carbon energy strategy are effectively responded. According to the improved column generation and interior point hybrid algorithm designed by the invention, a self-adaptive scene screening mechanism and a gradient guide initialization strategy are provided, the solving time is remarkably shortened, and meanwhile, the global optimality of understanding is ensured.

The invention relates to the technical field of new energy prevention and control, and discloses a novel uncertainty set representation design method for preventing new energy uncertainty risks. The method comprises the steps of collecting operation data from a new energy system in real time, and generating a multi-dimensional time sequence data set; carrying out multi-scale characteristic decomposition on the data set, identifying an uncertainty association mode in combination with historical similar scene information, and generating an uncertain factor set with confidence; constructing a dynamic uncertainty set model, optimizing boundary parameters by using a multi-scale feature decomposition result, and updating constraint conditions in real time; inputting real-time data into a model for verification, identifying risk events, marking levels, and feeding back the risk events to a scheduling decision-making system for collaborative analysis of a risk influence range; and performing reverse optimization on the scheduling decision system based on a risk identification result, identifying and verifying a risk mitigation strategy and an adjustment scheme, and determining an optimal decision path and implementation steps. The method can dynamically capture the uncertainty of the new energy, and effectively improves the operation safety and stability of the new energy system.

The invention discloses an active power distribution network optimization scheduling and risk assessment-oriented adjustable uncertainty set construction method, which comprises the following steps of: firstly, considering the correlation among various renewable energy sources, and constructing a maximum uncertainty set of the renewable energy sources based on variance, covariance and confidence factors; secondly, defining an adjustable uncertainty set in the maximum uncertainty set, and further constructing a risk fuzzy set considering a load reduction risk and a power abandoning risk; then, establishing an operation risk model based on a distributed robust optimization method, and converting the risk model into a solvable finite dimension linear optimization problem by introducing dual variables; and finally, drawing an operation risk curve by using a piecewise linearization method to realize coordination and balance of robustness and economy in uncertainty set selection. According to the method, the adaptive capacity of the power distribution network to renewable energy fluctuation can be effectively improved, and the economical efficiency and reliability of system operation are optimized.

The invention provides a dual-RIS-assisted robust optimization method for security and covert communication, and relates to the technical field of communication. The dual-RIS-assisted robust optimization method for security and covert communication comprises the following steps: S1, system modeling and index definition; s2, modeling an uncertainty set of the imperfect CSI, and establishing a robust constraint; and S3, constructing a unified optimization problem P1 of'worst case KL divergence minimization '. According to the invention, under the same power and energy budget, the robust scheme significantly reduces the KL divergence in the whole CSI error upper bound range, is always close to a perfect CSI reference, and is obviously superior to a non-robust baseline; and meanwhile, the total safety rate is kept higher and more stable. Therefore, the method can be improved at the same time when the detected probability is low and the confidential information is difficult to eavesdrop.

The application discloses a kind of virtual power plant planning-operation two-stage distribution robust optimization methods based on scene driving, comprising: S1, based on the master-slave game framework of power supply, load and energy storage three parties, capacity configuration model and master-slave game model are constructed;S2, considering the correlation and uncertainty of wind turbine, photovoltaic output, construct the uncertainty set of wind light output probability distribution, and generate multiple typical scenarios by sampling and clustering algorithm;S3, under the driving of typical scenario, the confidence interval of uncertainty set is comprehensively norm-constrained, and the distribution robust optimization model of virtual power plant considering uncertainty is established in planning and operation two stages;Wherein, distribution robust optimization model contains capacity configuration model and master-slave game model;S4, based on the column and constraint generation algorithm of modified Kriging model, distribution robust optimization model containing master-slave game model is solved.Utilizing the application, the influence of uncertainty on virtual power plant planning rationality and operation economy can be reduced.

The invention provides a power system unit commitment optimization method considering new energy probabilistic reserve, and the method comprises the steps: constructing a decision-dependent uncertain set based on wind power data before and after wind curtailment of a wind power plant; constructing a first-stage robust unit combination model by taking the maximum value of the day-ahead scheduling cost and the cost required for real-time scheduling in various possible scenes in the uncertain set as an objective function; constructing a second-stage robust unit commitment model by combining constraint conditions of the first-stage robust unit commitment model and taking real-time scheduling cost as a target function; and coupling the first-stage robust unit commitment model and the second-stage robust unit commitment model to obtain a new energy power system unit commitment optimization model. According to the method, all available standby resources including the new energy can be more effectively scheduled and utilized, the actual utilization rate and the absorption level of the new energy are improved, and the capacity of the whole power system for coping with uncertainty impact is enhanced.

The application discloses a kind of distributed robust optimization method, equipment and storage medium considering railway power grid flexibility, for high-altitude area power grid access impact traction load and fluctuation new energy, the flexibility requirement uncertainty set based on Wasserstein distance is constructed by analyzing the flexible demand of high-altitude railway along power system, the potential risk size caused by flexibility requirement uncertainty to system is quantitatively evaluated, the risk cost when flexibility shortage is determined, according to the risk cost when flexibility shortage and flexibility resource operation cost, the distributed robust optimization model based on Wasserstein distance is constructed, the model is converted into processable optimization problem using the approximation framework based on conjugate function, with high reliability, improve the flexibility adjustment capability of circuit, improve the balance of robustness and economy of optimization scheduling of system by reducing the number of constraints.

The invention discloses a virtual power plant regulation and control method based on distributed energy and flexible load aggregation, and belongs to the technical field of power system operation and control. A cooperative control architecture comprising a resource layer, an aggregation layer, an optimization layer, a control layer and a market layer is constructed, and distributed optimization is realized by improving an ADMM algorithm; according to the method, opportunity-constrained programming and an ellipsoid uncertainty set are used for processing wind and light uncertainty, a GRU and a Dueling DQN are adopted to form a deep reinforcement learning load response mechanism, a resource layer completes equipment access and multi-frequency sampling, an aggregation layer clusters to generate an equivalent model and constructs a low-delay communication network, an optimization layer constructs a multi-objective function, and the multi-objective function is combined with an MIQP and a robust algorithm for solving. The control layer executes regulation and control through MPC rolling optimization and a priority strategy, and the market layer realizes transaction and income sharing. According to the method, the wind and light absorption rate is increased by 15%-20%, the calculation efficiency is 30% or above, and the method is suitable for a high-proportion renewable energy scene.

The application relates to the technical field of electric power, and discloses a virtual power plant dynamic resource optimization scheduling method and system. The application solves the identification deviation problem caused by parameter coupling, and improves the robustness of a scheduling strategy to parameter uncertainty. An equivalent parameter combination method reduces the model dimension, so that multi-parameter joint estimation has computational feasibility. A multi-time scale optimization framework realizes dynamic cooperation of a parameter model and a scheduling strategy, and avoids scheduling deviation caused by parameter update lag in a traditional method. A coverage rate calibration mechanism ensures the matching degree of an uncertainty set and an actual error distribution, and provides a reliable data basis for robust optimization. The application realizes accurate modeling and identifiable optimization of key parameters of a virtual power plant, and solves the model misalignment problem caused by parameter redundancy or unidentifiability in a traditional method. Through generation of a candidate identifiable parameter set, the feasibility of subsequent experimental design and parameter estimation processes is ensured.

The invention provides a multi-granularity energy consumption characteristic parameter decoupling method and system based on a constraint uncertainty set, and the method comprises the steps: dividing the energy consumption characteristic of an industrial load into a plurality of intervals according to the energy consumption distribution and condition category probability, constructing a corresponding convex hull, and forming an uncertainty set based on a multi-interval convex hull; constructing a worst expectation four-layer robust optimization model by using the uncertainty set based on the energy consumption uncertainty and the condition category of the industrial load on different time scales; a Bregman alternating direction multiplier method is adopted to solve the non-convex problem of the worst expectation four-layer robust optimization model; and on the basis of the solution of the non-convex problem, performing continuous updating and decoupling optimization on the worst expectation four-layer robust optimization model by adopting a column and constraint generation algorithm of an alternating iteration strategy so as to realize decoupling of the multi-granularity energy consumption characteristics of the industrial load. According to the invention, the flexibility and adaptability of energy management are enhanced, and the energy utilization efficiency is improved.

The present application relates to the technical field of administration, in particular to a security personnel intelligent scheduling system and method based on hierarchical architecture. The system adopts five-layer architecture of data management layer, feature calculation layer, intelligent core layer, decision support layer and user interaction layer to realize business logic decoupling. The method core includes: converting requirements into structured portraits through a project analyzer; the intelligent scheduling engine adopts a multi-objective evolutionary algorithm integrating robust optimization, generates a resilient scheduling scheme capable of resisting the worst absence scenario by defining an uncertain set of personnel availability; the dynamic scheduler adopts an online algorithm with a guaranteed competitive ratio to provide real-time decisions with bounded performance for unpredictable temporary tasks. The scheme realizes the change from post-event remediation to pre-event prevention, guarantees business continuity, and provides quantifiable performance guarantees for scheduling decisions in dynamic environments.

The invention relates to the technical field of administrative management, in particular to a security personnel intelligent scheduling system and method based on a layered architecture. The system adopts a five-layer architecture of a data management layer, a feature calculation layer, an intelligent core layer, a decision support layer and a user interaction layer to realize business logic decoupling. The core of the method comprises the following steps: converting a demand into a structured portrait through a project analyzer; the intelligent scheduling engine adopts a multi-objective evolutionary algorithm fused with robust optimization, and generates a flexible scheduling scheme capable of resisting the worst absence scene by defining an uncertain set of personnel availability; and the dynamic scheduler adopts an online algorithm with competition ratio guarantee to provide real-time decisions with bounded performance for unpredictable temporary tasks. According to the scheme, shift arrangement is changed from post-event remedy to pre-event prevention, and quantifiable performance guarantee is provided for scheduling decisions in a dynamic environment while service continuity is guaranteed.

The invention discloses a distribution network flexible resource day-ahead robust scheduling method under a source load multivariate fluctuation risk, and belongs to the field of power system optimization operation. The core innovation of the method lies in that an uncertainty set model considering internal and external source load fluctuation risks is constructed, and a two-stage decision-making mechanism and a solving method thereof are designed: resources such as an interconnection switch, an on-load voltage regulation transformer gear, an energy storage charging and discharging mode and the like are pre-allocated in a day-ahead stage; in the second stage, multi-type energy storage and transferable load power scheduling are optimized, and a self-adaptive robust framework coping with the worst scene is formed. On the basis, an optimization model is decomposed into a main problem and a risk stabilization sub-problem, non-convex features brought by endogenous uncertainty are processed by adopting McCormick envelope, and iterative solution is performed through a column constraint generation algorithm. According to the method, through collaborative optimization of source-load double-side flexible resources, wind power, photovoltaic and load multivariate fluctuation risks are effectively stabilized, and the operation economy and flexibility are remarkably improved while the safety robustness of the power distribution network is guaranteed.

The application discloses a kind of water resource collaborative robust scheduling method considering supply and demand double uncertainty, comprising: the risk exposure contribution rate of risk of system risk of inflow and water demand is evaluated, and asymmetric inflow and water demand robust uncertainty set is constructed accordingly;Supply and demand risk coupling amplification index is calculated, based on the index, the joint uncertainty space is screened and reconstructed, and a joint scenario tree is generated;Scenario tree is input into water resource hierarchical collaborative scheduling model, inter-period optimization is carried out using global resource allocation layer, and stage risk quantification parameter representing risk constraint strength of each stage is determined;The parameter is used as cross-layer risk constraint by using local scheduling decision layer, and a dynamic scheduling strategy that meets the requirements of global risk control is generated.The application effectively solves the problems of over-conservatism and extreme risk defense failure in the scheduling process through risk scenario construction driven by physical mechanism and cross-layer risk explicit conduction.

The application discloses a data-driven robust optimization scheduling implementation method based on a multi-affine strategy, obtains a scheduling strategy by establishing a two-stage robust optimization model and solving, adopts a self-organizing mapping neural network to cluster a wind power sample data set to obtain a multi-uncertainty set, and then solves the robust optimization model based on the multi-affine strategy, transforms the optimization model by adopting the multi-affine strategy, and combines a dual principle to convert the original optimization model into a linear programming problem for solving, so as to realize data-driven robust optimization scheduling. The application is based on a robust optimization method, improves the conservativeness of uncertainty factor modeling by establishing a multi-uncertainty set, and improves the economy of the obtained scheduling strategy by introducing the multi-affine strategy, can ensure the optimization model solving efficiency, effectively depicts the fluctuation range of the uncertainty factor, and improves the power grid operation efficiency while ensuring the power grid safety.

This invention discloses a unit combination method based on historical data under renewable energy penetration, comprising the following steps: S1. Constructing standby constraints in the unit combination problem; S2. Constructing a prediction error dataset consisting of prediction errors at various times within the data collection period; S3. Dividing the error dataset into two parts: a dataset and a set of uncertainties; S4. Dividing the dataset into uncertainties and constructing an uncertain set, and estimating its shape parameters; S5. Transforming robust constraints with uncertainties into deterministic constraints for direct solution; S6. Reconstructing the uncertain set and introducing a transformation function to transform and solve the optimization problem. This invention reduces the impact of prediction errors on solving the unit combination problem by using historical data of renewable energy prediction errors, effectively improving energy utilization.

The invention belongs to the technical field of task scheduling of multi-core processors, and discloses a task allocation method and device for a multi-core processor and a vehicle. The task allocation method comprises the steps that S1, a system task is divided into n subtasks, and a directed acyclic graph model is constructed with the subtasks as nodes, establishing a multi-objective optimization model according to the execution cost of the sub-tasks, the communication cost between the sub-tasks, the constraint conditions and the optimization objective; s2, on the basis of the multi-objective optimization model, introducing an uncertainty set to represent the uncertainty of the cost, and establishing a multi-objective robust optimization model considering the uncertainty of the cost; and S3, solving the multi-target robust optimization model by adopting a double-layer multi-target optimization algorithm to obtain Pareto front edges, and selecting an optimal allocation scheme from the Pareto front edges as a task allocation scheme of the multi-core processor. By applying the technical scheme of the invention, the robustness can be improved, the inter-core communication consumption is reduced, and the load balancing is realized.

This invention relates to a two-stage robust planning method for demand-side capacity that considers both cost and flexibility resources, belonging to the field of power system operation and maintenance technology. By introducing an uncertainty adjustment parameter, this invention constructs a box-shaped uncertainty set with flexibly adjustable conservatism, effectively mitigating the inherent drawbacks of overly conservative traditional robust optimization methods. This allows the model to dynamically adjust the boundary of the uncertainty set according to the different risk tolerances of the actual system, thus achieving a refined trade-off between economy and reliability during the planning stage. The two-stage robust optimization model constructed in this invention, considering the electricity-carbon coupling mechanism, incorporates carbon trading costs as an important component of operating costs into the objective function, effectively responding to the needs of the national green and low-carbon energy strategy. The improved column generation and interior point hybrid algorithm designed in this invention proposes an adaptive scenario selection mechanism and a gradient-guided initialization strategy, significantly shortening the solution time while ensuring the global optimality of the solution.

The application discloses a robust optimization model for container booking decision of sea shipping considering order uncertainty and an optimization solving method thereof, wherein the modeling process of the optimization model comprises the following steps: 1) determining a target function of a two-stage robust optimization model; and 2) determining constraint conditions contained in the model, including one-stage constraint conditions and two-stage constraint conditions. The container shipping booking problem considering customer order demand uncertainty is established as a two-stage robust optimization model. Since the uncertain set is a polyhedron, the model cannot be solved by simply enumerating all uncertain scenarios. The model is solved by using a row and column generation algorithm. The shipping container booking problem is abstracted as a mathematical model, and a row and column generation algorithm based on a main problem-subproblem framework is designed for the model. The algorithm can efficiently solve the problem and has high technical and economic value.

The invention relates to the technical field of electric power, discloses a virtual power plant dynamic resource optimal scheduling method and system, solves the problem of identification deviation caused by parameter coupling, and improves the robustness of a scheduling strategy to parameter uncertainty. The equivalent parameter combination method reduces the model dimension, so that the multi-parameter joint estimation has calculation feasibility. The multi-time scale optimization framework realizes dynamic cooperation of the parameter model and the scheduling strategy, and avoids scheduling deviation caused by parameter updating lag in a traditional method. A coverage rate calibration mechanism ensures the matching degree of an uncertain set and actual error distribution, a reliable data basis is provided for robust optimization, accurate modeling and identifiability optimization of virtual power plant key parameters are achieved, and the problem of model misalignment caused by parameter redundancy or unidentifiability in a traditional method is solved. By generating the candidate recognizable parameter set, the feasibility of the subsequent experimental design and parameter estimation process is ensured.

Embodiments of the present application provide a virtual power plant cooperative control method and system and an electronic device, and relate to the technical field of power distribution Internet of Things. The method comprises: obtaining a control target; calculating a fluctuation coefficient according to historical data of a global state parameter, and determining an uncertainty set; establishing a robust optimization model based on the uncertainty set, solving the robust optimization model to obtain an optimal solution of a robust objective function; training a Bayesian deep reinforcement learning model using the historical data; inputting current state data of the global state parameter into the trained Bayesian deep reinforcement learning model, generating an optimal action, and calculating a corresponding confidence; according to the relationship between the confidence and a preset confidence threshold, selecting one of the optimal action and the optimal solution of the robust objective function to generate a first control instruction and issuing the first control instruction to a low-level intelligent agent. The embodiments provided in the present application can promote the development of virtual power plants in the direction of finer, more intelligent and more autonomous.

This invention provides a novel method for worst-case performance-optimal robust beamforming. First, through analysis, the following important conclusions are obtained: In the design of a worst-case performance-optimal beamformer, the constraint of the uncertainty set can be given by the Capon spectrum search grid spacing. Based on this, and combined with a method for reconstructing the interference noise covariance matrix, the novel robust beamforming method of this invention is obtained. Because the interference noise covariance matrix is ​​reconstructed, the target signal component is removed, and the constraint of the uncertainty set for worst-case performance is determined based on the analysis, the novel method of this invention can ensure the design of an optimal beamformer under worst-case performance conditions. Furthermore, for the uncertainty set constraint method in robust adaptive beamforming technology, the novel method of this invention also provides a general and universal method for calculating the uncertainty set constraint, which is of great significance.