9 results about "Multiplier method" patented technology

The invention relates to the technical field of information, and discloses a data migration scheduling method based on equipment anomaly detection, which comprises the following steps of: S1, modeling through the health state of equipment, monitoring and estimating the health state of the equipment in real time, and judging whether the equipment is in an abnormal state or not based on an estimated value; s2, when the equipment is in an abnormal state, triggering data migration scheduling; s3, designing an optimization scheduling target, minimizing data migration delay, equipment load fluctuation and bandwidth resource consumption, and calculating a data migration path and a scheduling strategy based on an equipment health state and bandwidth constraints; and S4, solving an optimal scheduling problem with constraints by adopting a Lagrange multiplier method. Modeling and real-time estimation are carried out on the equipment health state through the stochastic differential equation and the Kalman filtering algorithm, accurate abnormal state detection is achieved, and the problems that in the prior art, response to equipment state changes is not timely, and accuracy is insufficient are effectively solved.

This invention discloses an ISAC waveform design method based on Wasserstein split-bar and CVaR risk constraints. First, an empirical probability distribution is constructed based on historical channel observation samples. Using Wasserstein distance as a metric, an uncertainty set of the channel statistical distribution is constructed within the neighborhood of the empirical distribution. Then, by introducing conditional risk value as a risk metric, the tail distribution of the interference index is constrained to suppress low-probability but high-impact extreme interference events. Based on this, a split-bar waveform optimization model that simultaneously considers sensing performance and communication risk constraints is constructed. Through equivalent reconstruction of distribution uncertainty and risk constraints, the original stochastic optimization problem is transformed into a solvable deterministic convex optimization problem. The problem is decomposed and solved by introducing variable splitting and alternating direction multiplier methods, ultimately obtaining the ISAC transmit waveform that satisfies the total power and peak power constraints.

The application discloses a knowledge graph learning path planning method based on multiple agents, and belongs to the technical field of intelligent optimization. In order to solve the problem that planning constraint conflicts are prone to occur when multiple agents plan learning paths on a knowledge graph, and it is difficult to reach an agreed path in real time, the application encodes the knowledge graph through a graph transformer, generates a candidate learning path by using centralized training and decentralized execution of multiple agent reinforcement learning, constructs an incremental satisfiability judgment problem for conflict detection and outputs an unsatisfiable core and a conflict type, performs consistency arbitration update based on a segmented consensus variable driven by a conflict kernel by using an alternating direction multiplier method, and adjusts a penalty parameter according to an original residual error, a dual residual error and a conflict type. Meanwhile, the application learns and compresses transmission of the consensus variable update amount, and controls the number of rounds, so that the technical effects of outputting a consistent learning path satisfying constraints, reducing the number of iteration rounds and communication overhead, and improving real-time performance are achieved.

The invention belongs to the technical field of demand response strategies, and provides an industrial park hierarchical response method and system based on a federal alternating direction multiplier method. An industrial park hierarchical response model giving consideration to multi-subject benefits of load aggregators and industrial users and considering the technological process is constructed, the industrial park hierarchical response model comprises a load aggregator layer and an industrial user layer, and the load aggregator layer optimizes a user-level response scheme with the minimum cost as the target; the industrial user layer optimizes an equipment-level response scheme by taking the maximum additional income as a target; based on the industrial park hierarchical response model, constructing a federal alternating direction multiplier method distributed algorithm; and solving the hierarchical response model of the industrial park through a federal alternating direction multiplier method distributed algorithm, and dynamically updating the compensation price by the load aggregator layer and the industrial user layer until the compensation price is converged to an optimal solution. The sensitive data protectiveness and the calculation efficiency can be improved, and a demand response scheme considering the multi-subject benefits and the technological process is obtained.

The invention discloses a TDOA / FDOA passive positioning method based on an alternating direction multiplier method. The method comprises the following steps: firstly, carrying out initial estimation on a target position and speed by utilizing a weighted least square method; then, performing first-order Taylor expansion on the nonlinear geometric constraint at the initial point, and reconstructing an original non-convex optimization problem into a constraint weighted least square model with linear coupling constraint; finally, an ADMM framework is introduced, and efficient decoupling solution of position and speed variables is achieved by alternately updating the target position, the speed and the Lagrange multiplier. The system, the device and the medium are used for realizing the TDOA / FDOA passive positioning method based on the alternating direction multiplier method. According to the method, the problem that traditional nonlinear optimization is prone to falling into local optimum is effectively solved, the positioning precision is remarkably improved, meanwhile, the calculation complexity is greatly reduced, and the robustness and the real-time performance of the algorithm under the conditions of low signal-to-noise ratio and complex observation are enhanced.

The embodiment of the application provides a power distribution network-multi-microgrid collaborative optimization method and related equipment, and belongs to the field of power system optimization scheduling. The method comprises the following steps: each operation subject constructs a chaotic polynomial proxy model of operation cost based on a random response surface method, and quantitatively obtains an expected independent operation cost of each subject considering uncertainty risk; the cost is used to reconstruct a negotiation breakdown point of a cooperative game; a Nash bargaining model is established based on a new benchmark, and is decomposed into power and price optimization sub-problems, distributed iterative solution is carried out by using an alternating direction multiplier method, and an optimal collaborative transaction strategy is obtained; and finally, the strategy is executed and dynamically updated under an intra-day rolling framework. The application realizes endogenous quantification and fair allocation of risk cost by deeply integrating the random response surface method and the game theory, overcomes the defects of the traditional "risk-game separation" paradigm, and effectively improves the overall economy, operation stability and benefit distribution fairness of the system.

The invention discloses an ISAC waveform design method based on Wasserstein distribution robustness and CVaR risk constraint, and the method comprises the steps: constructing empirical probability distribution based on a historical channel observation sample, taking a Wasserstein distance as a measure, constructing an uncertainty set of channel statistical distribution in an empirical distribution neighborhood, introducing a conditional value-at-risk as a risk measurement index, and carrying out the calculation of the risk measurement index. And constraining the tail distribution of the interference indexes to suppress extreme interference events with small probability but high influence. On this basis, a distributed robust waveform optimization model considering both perception performance and communication risk constraint is constructed, and an original stochastic optimization problem is converted into a solvable deterministic convex optimization problem through equivalent reconstruction of distribution uncertainty and risk constraint. The problem is decomposed and solved by introducing a variable splitting and alternating direction multiplier method, and finally an ISAC transmitted waveform meeting total power and peak constraint is obtained.

The invention discloses an adaptive model parameter quantification method and device based on Taylor expansion approximation. Performing unified time step training on the basic model; time steps are grouped, the unified time steps are divided into a plurality of groups, and each group is subjected to independent quantitative configuration optimization after grouping; for each time step length group, modeling a quantization problem as a rate distortion optimization problem; performing mixed precision quantization on the weights and the activation values of different layers; decomposing output distortion into the sum of quantization distortion of each layer by utilizing first-order Taylor expansion approximation so as to find the optimal bit allocation of each layer in the linear time complexity; a Lagrangian multiplier method is adopted, a rate distortion optimization problem is converted into a minimization problem of a Lagrangian cost function, and it is ensured that output distortion is minimum under the constraint of the size of the model; according to the method, the compression ratio is improved by optimizing the bit allocation quantification technology of the diffusion model, and meanwhile, high reasoning efficiency and accuracy are kept.

This invention discloses a reactive power optimization method for distribution networks based on the accelerated gradient alternating direction multiplier method, relating to the field of power system operation and control technology. The technical solution includes the following steps: S1, acquiring the topology and node operation data of the distribution network, and constructing an improved similarity matrix; S2, dynamically partitioning the distribution network using an adaptive spectral clustering algorithm; S3, establishing a local reactive power optimization model for each sub-region obtained from the dynamic partitioning; S4, introducing globally consistent variables for the voltage amplitude and phase angle of boundary nodes, establishing consistency coupling constraints between regions, constructing the augmented Lagrangian function for each region, and solving the local optimization problem; S5, updating the globally consistent variables using an accelerated gradient strategy; S6, calculating the original residual and dual residual. This invention achieves dynamic partitioning of the distribution network through adaptive spectral clustering, significantly improving the algorithm's convergence speed, effectively reducing system network losses, and eliminating the risk of node voltage exceeding limits.