322 results about "Lagrange multiplier" patented technology

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention belongs to the technical field of communication, and discloses a positioning method and system based on differential time delay and an alternating direction multiplier method, and the method comprises the steps: obtaining the position information and differential time delay of a receiver; performing joint estimation on the position of the target object and the position of the transmitter based on an alternating direction multiplier method; wherein during joint estimation, a first variable and a second variable are introduced, an original optimization problem is decomposed into two sub-problems related to the first variable, the second variable and a Lagrangian multiplier coefficient during iterative calculation in the joint estimation process, and in each iterative process, analytical solutions of the two sub-problems are solved respectively; and updating the first variable, the second variable and the Lagrange multiplier coefficient. According to the method, the accuracy of target object position estimation can be improved while the calculation complexity can be remarkably reduced.

The invention provides an anomaly detection method and device for image data and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: carrying out the feature extraction of the image data, fusing the spectrum and spatial features to obtain a joint feature matrix, and inputting an anomaly detection model; the model adopts an alternating direction multiplier algorithm to solve a low-rank sparse decomposition problem, and an objective function comprises a data fidelity item, a regularization item and a waveband weight item; the regularization item comprises a low-rank constraint and a sparse constraint, and the wave band weight item acts on the low-rank constraint in a weighting form; updating a background low-rank tensor, an abnormal sparse tensor, a Lagrange multiplier, a sparse constraint weight, a wave band weight item and penalty parameters of an algorithm by adopting an iteration mode in a solving process; and repeating iteration until a preset termination condition is reached, calculating an abnormal score graph pixel by pixel based on the abnormal sparse tensor, and comparing to determine an abnormal target. The problem that an abnormal target is difficult to accurately recognize in a complex scene can be solved, and detection precision and efficiency are improved.

The invention discloses a hydrodynamic analysis method for a floating structure with multi-body and multi-coupling characteristics, which comprises the following steps of: acquiring geometric characteristics of each floating body, and dividing boundary element computational grids; wave excitation force, additional mass and radiation damping of each floating body are calculated, and a mass matrix and a rigidity matrix of each floating body are obtained; determining a connection mode and a coupling factor of the floating body; according to a connection mode and coupling characteristics of the system, establishing displacement connection conditions among the floating bodies; constructing a motion constraint matrix according to the displacement continuity condition and the motion constraint relation; determining a frequency domain motion equation of the multi-floating-body system according to a Lagrange multiplier method; and solving the motion equation to obtain the motion response of the multi-floating-body system, and further systematically analyzing the wave energy capture power according to the coupling characteristics of the system. According to the analysis method, the problem that the motion response of each degree of freedom cannot be accurately described when the hydrodynamic response characteristics of the complex multi-body system are obtained through an existing simulation method is solved.

The invention discloses a data processing method based on multi-agent collaborative optimization, and the method comprises the following steps: building a multi-agent collaborative environment, and outputting a constraint threshold value; accessing multiple data sources according to the constraint threshold, and outputting a standardized input data set; distributing the input data set to each agent participating in collaboration, and outputting a processing suggestion set; performing cross evaluation and consensus formation on the processing suggestion set by each agent, and outputting a candidate scheme list; performing feasible region screening on the candidate scheme list, and outputting an optimized candidate scheme set; receiving an optimization candidate scheme set, carrying out joint optimization on candidate schemes by adopting a cooperation bidding rule simultaneously constrained by double Lagrange multipliers, and outputting a bid winning execution scheme; taking the bid winning execution scheme as input, and outputting execution state information; and receiving the execution state information, and outputting a final execution result when the system meets a preset termination condition. According to the invention, a collaborative optimization data processing method is realized.

The invention discloses a digital twinning multi-agent reinforcement learning intelligent decision-making system and method with a secure memory playback mechanism, and the system comprises a digital twinning module which is used for constructing a virtual model and synchronizing the virtual model with a physical entity in real time; the multi-agent reinforcement learning module is used for carrying out strategy learning based on a constrained Markov decision process and balancing performance and safety through a Lagrange multiplier; the safe memory playback module is used for weighting and playing back the experience samples according to the risk and the timeliness so as to improve the learning safety; the reversible grey influence network module is used for causal modeling and reasoning and enhancing decision interpretability; the double-loop self-constraint control module ensures that a control action is always in a physical safety boundary through a barrier function and safety projection; and the convergence and stability criterion module is used for verifying strategy security convergence and system asymptotic stability. According to the method, the problems of strategy border crossing, virtual-real mismatching and the like in the high-risk manufacturing process are solved, and multi-target optimal control under the safety constraint is realized.

The invention discloses a secondary forest development stage division method based on forest stand state characteristics, and belongs to the technical field of forest development stage division. Comprising the following steps: selecting an initial index set, performing index forward and standardization processing on sample plot survey data, and forming a data set; classifying the data by adopting a system clustering analysis method, checking and analyzing a state index influencing a classification result, and selecting plt; the index of 0.05 constitutes a secondary forest development stage division index system; carrying out weight assignment, then correcting the weight by adopting an entropy evaluation method, and determining the comprehensive weight of the index by utilizing a Lagrange multiplier method; the sum of the dimension-removed value of the division index and the comprehensive weight value product is a development stage value; according to an equidistant method, the development stage of the secondary forest is divided into a forest gap stage, a renewal stage, a differentiation stage, a built-up stage and a stable stage. Depending on forest age is avoided, and the method is suitable for multi-tree mixed secondary forests; the indexes are easy to obtain, and the division result can accurately guide forest management.

The invention relates to the technical field of electric power system dispatching, in particular to a multi-microgrid collaborative optimization dispatching method based on an improved ADMM, and the method comprises the steps: constructing a microgrid operation cost minimization objective function based on a microgrid mathematical model, and carrying out the operation constraint of each microgrid; iteratively updating a local variable, a global variable and a Lagrange multiplier by using an ADMM model to solve the operation cost of the micro-grid; and income distribution is carried out based on the asymmetric Nash negotiation model. According to the method, the problem that the convergence speed and the calculation complexity need to be further improved when an existing model is used for multi-microgrid dispatching optimization is solved.

The invention relates to a low-dimensional subspace clustering method based on projection matrix guidance, and the method comprises the steps: extracting a light response non-uniformity PRNU noise residual error from input image data through employing a denoising filter, and constructing a PRNU feature data set of an image; performing feature dimension reduction on the feature data set by adopting a projection matrix method, constructing a projection matrix maintaining a geometric structure, mapping the projection matrix to a low-dimensional potential subspace, and further constructing a model for the subspace by utilizing a sparse self-representation method; constraint is applied to sparse self-representation in the low-dimensional potential subspace, and joint optimization is carried out through an augmented Lagrange multiplier method ALM and an alternating direction minimization ADM strategy to be used for efficient clustering of data in the low-dimensional potential subspace. According to the method, the projection matrix maintaining the geometric structure is constructed, the high-dimensional PRNU features are mapped to the low-dimensional potential subspace, the local neighborhood relation and the global distribution structure are reserved in the dimension reduction process, the calculation cost is reduced, and the clustering robustness and performance are effectively improved.

The invention discloses a multi-view migration interpretable method based on soft variable embedding and discriminant structure preserving, and particularly relates to the technical field of machine learning, and the method comprises the steps: obtaining a plurality of multi-view features for EEG samples of a tagged source domain and an untagged target domain through the extraction of a plurality of features; after TSK-FS antecedent network mapping, constructing inter-domain connection by adopting migration soft variable embedded consequent learning; a data structure is retained through a local-global structure retention item, so that a discriminant neighborhood relationship of original data is retained to the greatest extent in a migration process; and a target function is constructed in combination with a multi-view learning strategy, iterative optimization is performed through an enhanced Lagrange multiplier algorithm, and finally a result is output by using a given classifier.

The invention discloses a dual robust principal component analysis image denoising method based on a graph, and belongs to the technical field of image processing. The method comprises the following steps: flattening an image to be processed to construct a data matrix in a column vector form; constructing a graph structure by using a K nearest neighbor method, and generating a graph Laplacian matrix based on the graph structure; jointly considering an image reconstruction error, a sparse noise item, a linear mapping error item and a graph structure regular item, and constructing an optimization model; and carrying out variable alternating optimization by adopting an augmented Lagrange multiplier method and an alternating direction solution method, and obtaining an image denoising result according to the low-rank principal component. According to the method, image structure information and a double constraint mechanism are introduced into a robust principal component analysis framework, so that the detail retention capability and the structure consistency of the image are effectively enhanced, the robustness and the visual quality of image denoising are improved, and the method is suitable for application scenes such as image processing, video monitoring and target detection under complex backgrounds.

The invention discloses an engineering machinery industry green factory evaluation method and system. The method comprises the steps of obtaining a pre-formulated green factory evaluation index system; calculating the subjective weight of each secondary index relative to the target layer; introducing a Monte Carlo simulation algorithm, and calculating an objective weight and an evaluation matrix of each secondary index corresponding to each Monte Carlo simulation relative to the target layer; based on a minimum relative information entropy principle and a Lagrange multiplier method, combined with the subjective weight and the objective weight of each secondary index relative to the target layer, calculating the comprehensive weight of each secondary index relative to the target layer corresponding to each Monte Carlo simulation; on the basis of the comprehensive weight and evaluation matrix of each secondary index relative to the target layer, constructing a fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation; and obtaining a green factory grade evaluation result based on the fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation and a preset score interval corresponding to each comment grade. According to the method, the accuracy and objectivity of green factory evaluation can be remarkably improved.

The invention relates to the field of engineering project management, and discloses an engineering project full-process management system construction method, which comprises the following steps that project optimization objectives and constraint conditions are defined, the optimization objectives comprise project progress, cost and resource utilization rate, and the constraint conditions comprise task dependence, resource availability, construction period and budget limitation; based on the targets and the constraint conditions, a multi-target optimization model is constructed, and the multi-target optimization model adopts a weighted sum method to merge a plurality of target functions; and solving the optimization model by using a Lagrange multiplier method to obtain an optimal resource allocation and task scheduling scheme. By combining the dynamic control theory, the Internet of Things technology and the fluid mechanics model, real-time dynamic adjustment of project resource scheduling is achieved, resource allocation is optimized, project progress and cost control efficiency is improved, and it is ensured that the project efficiently and flexibly responds to changes in the execution process.

The invention provides a large model training data desensitization protection method and system based on differential privacy, and relates to the technical field of artificial intelligence security, and the method comprises the steps: calculating a differential privacy parameter, mapping training data to a feature subspace, and constructing a dimension reduction manifold projection operator; solving an optimal disturbance vector by using a Lagrange multiplier method to form a de-sensitization feature set; probability distribution is constructed based on kernel density estimation, and Gaussian noise is injected; and dynamically adjusting a parameter group updating strategy in model training. According to the method, data utility and privacy protection can be effectively balanced, the large model training safety is enhanced, and the model convergence efficiency is improved.

The invention discloses a mixed regular variational mode decomposition method, device and equipment suitable for an unsteady flow field of an aircraft and a storage medium, and relates to the technical field of flow analysis, and the method comprises the steps: determining a bandwidth estimation value and a frequency separation weight coefficient based on the unsteady flow field, an expected mode number, a spatial mode and a time evolution coefficient, constructing a first regular term and a second regular term, weighting to obtain a mixed regular term, introducing a flow field reconstruction term to establish a flow field modal decomposition variational optimization problem, converting the problem into a frequency domain optimization problem through Fourier transform, and simplifying the problem into a sub-convex optimization problem by adopting an alternating direction multiplier method; an analytic solution is obtained through a variational method and iteratively updated to obtain a flow field space mode, a time evolution coefficient frequency domain representation, a center frequency and a Lagrange multiplier, the time evolution coefficient is obtained through inverse Fourier transform, a mode decomposition result is obtained, and therefore high-precision mode separation of the unsteady flow field is achieved. And the calculation efficiency and the iteration convergence speed of the variational optimization problem are improved.

The invention discloses a manifold optimization transmitting directional diagram synthesis method for an analog digital hybrid array. According to the manifold optimization transmitting directional diagram synthesis method for the analog digital hybrid array, the HAD array transmitting directional diagram synthesis problem with the criterion of minimizing the matching error between an expected beam directional diagram and an actual beam directional diagram is researched. According to the method, the solution is carried out by utilizing block coordinate descent (BCD) and Riemannian manifold optimization. Firstly, a constant modulus auxiliary variable is introduced to equivalently represent that an objective function is a smooth biquadratic function; then, alternately optimizing a scaling factor, a digital weight, a simulation weight and a constant modulus auxiliary variable under a BCD framework, reexpressing the simulation weight and the auxiliary variable as a quadratic programming problem under constant modulus constraint, and solving through a Riemannian gradient descent method; the globally optimal solution of the digital weight is obtained through a Lagrange multiplier method. A numerical result shows that compared with other representative algorithms, the algorithm provided by the invention has better beam matching performance.

The invention provides an intelligent decision-making system based on reinforcement learning, and relates to the technical field of finance, and the system comprises a threshold generation module which can respond to a current credit application flow, extract a customer feature vector and an environment state vector in real time, and generate a dynamic risk threshold set according to the customer feature vector and the environment state vector; and the decision action determination module inputs the client and environment feature vectors into a pre-training strategy network to determine a decision action. After executing a decision action, the risk function value calculation module calculates function values of a plurality of risk constraints in real time, and the Lagrange multiplier adjustment module determines a deviation by comparing the risk function values with a dynamic risk threshold value, dynamically adjusts a Lagrange multiplier associated with each risk constraint, and adjusts the risk function value of each risk constraint. And the strategy network parameter updating module updates strategy network parameters by using a primal dual gradient descent method based on the real-time reward signal and the adjusted multiplier, and the risk early warning module triggers early warning when any risk constraint function value reaches a preset proportion of a corresponding dynamic risk threshold.

The invention discloses a low-altitude edge network anti-fault task migration and DNN adaptive segmentation method, which comprises the steps of constructing a multi-unmanned aerial vehicle auxiliary MEC system, and performing DNN task division through a DNN adaptive division strategy; constructing a fault migration model, and migrating an uncompleted DNN task on the failed unmanned aerial vehicle to a normal unmanned aerial vehicle; the weighted energy consumption minimization problem of the multi-unmanned aerial vehicle assisted MEC system is reconstructed into a Markov decision process, and an optimal strategy is learned through interaction with the environment; a DKSAC-PER joint optimization framework is constructed, transmission power is optimized through the DKSAC-PER, calculation resource allocation optimization is performed through a Lagrange multiplier method, a reward value of a current step is calculated through an SAC-PER algorithm, network parameters are updated, and a complex mixed decision space is solved. According to the invention, the flight paths of the remaining unmanned aerial vehicles can be automatically adjusted, the calculation load is redistributed, and the survivability of the system in a severe environment is greatly improved.

The invention discloses an electric vehicle collaborative optimization scheduling method based on a'network-station-vehicle 'layered architecture. The method comprises the following steps: constructing a double-layer distributed scheduling model; in the upper-layer model, modeling is carried out on aggregation flexibility of the charging stations, and an aggregation charging and discharging plan of each charging station is formulated by taking maximization of the total benefit of the system as a target; in the lower-layer model, the charging and discharging behaviors of the individual electric vehicle are scheduled by following an upper-layer plan instruction and taking the comprehensive satisfaction degree of a vehicle owner as a target; a second-order cone programming relaxation technology is adopted to convert a non-convex power flow constraint equation in the upper-layer model into convex constraint; decomposing the global optimization problem into a plurality of independent sub-problems which can be solved by each charging station in parallel by adopting an improved Lagrange relaxation dual method; and iteratively updating the Lagrangian multiplier through a sectional self-adaptive step length strategy until the algorithm is converged and a global optimal scheduling plan is generated. According to the method, the scheduling effect close to centralized optimization can be obtained while the privacy of the user is protected; and the problem of curse of dimensionality is effectively solved.

The invention discloses a large-scale MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) joint active user detection and channel estimation method. The invention provides a probability model for modeling a user beam domain channel based on channel support and channel values for a large-scale MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) unlicensed random access system. Under the framework of the Bethe free energy theory, the problem of large-scale MIMO-OFDM joint active user detection and channel estimation is converted into the problem of Bethe free energy minimization. The Bethe free energy minimization problem is solved through a Lagrange multiplier method to obtain a related mixed message passing algorithm, and joint active user detection and channel estimation of the large-scale MIMO-OFDM unlicensed random access system are realized by using the algorithm. According to the method, the accuracy of joint active user detection and channel estimation of the large-scale MIMO-OFDM authorization-free random access system can be effectively improved.

The invention relates to the technical field of power grid dispatching, and particularly provides a power distribution network-district micro-grid collaborative dispatching method, which comprises the following steps of: inputting a connection variable between a power distribution network and each district micro-grid into an optimization model, determining a first optimization problem of the power distribution network and a second optimization problem of the district micro-grids, and calculating a connection variable residual error, quadratic terms of penalty functions of the first optimization problem and the second optimization problem are subjected to linearization processing; under the condition that a first objective function value of the first optimization problem, a second objective function value of the second optimization problem and a connection variable residual error meet a convergence condition, determining an optimal solution of a connection variable so as to carry out cooperative scheduling; and under the condition that the convergence condition is not met, updating the Lagrange multiplier and the penalty weight of the penalty function based on the convergence degree index value of the connection variable residual error so as to obtain a new connection variable residual error. The problem that the real-time performance of cooperative scheduling of a power distribution network-transformer area micro-grid is insufficient in the prior art is solved.

The invention discloses a main lobe gain maximization method for an analog-digital hybrid array of a radar communication system. According to the method, the ratio of the maximum minimum main lobe level to the peak side lobe level is used as a target function, and under the main lobe ripple constraint, the simulation weight constant modulus constraint and the digital weight power constraint, the ADMM (Alternating Direction of Multiplexing) is used for carrying out joint design on the simulation weight and the digital weight. In the ADMM framework, a Riemann conjugate gradient method is adopted to solve a simulation weight subproblem with constant modulus constraint; and solving a digital weight by adopting a Lagrange multiplier method. A numerical result shows that the method can generate better main lobe ripples and lower peak side lobe level, and has good beam directional diagram accurate control performance.

The invention relates to an electric vehicle aggregator electric energy market optimization decision-making system and method based on reinforcement learning. The system comprises a data acquisition module, a multi-time scale modeling module, a reinforcement learning optimization module, a market clearing feedback module and a control execution module. The data acquisition module collects the state and market information of the electric vehicle; the multi-time scale modeling module constructs a double-layer optimization model of a day-ahead market and a real-time market, and realizes time scale coupling through a Lagrange multiplier; the reinforcement learning optimization module adopts a depth deterministic strategy gradient algorithm to dynamically adjust the energy bidding amount, the capacity bidding amount and the charging and discharging power distribution strategy; the market clearing feedback module calculates bid winning electric quantity and node marginal electricity price; and the control execution module issues a charging and discharging instruction. According to the method, the bidding strategy is optimized through reinforcement learning, electricity price uncertainty and multi-time scale coupling are comprehensively considered, the income of an electric vehicle aggregator is remarkably improved, and meanwhile, the adjustment requirement of a power system is met.

This invention discloses a bitrate control method at the image block level for P-frames and / or B-frames. Step S1: Obtain the inter-frame coding mode candidates and their prediction costs for each image block to be encoded. Step S2: Select the minimum prediction cost to characterize the inter-frame coding complexity of the image block to be encoded; use the sum of the inter-frame coding complexities of all image blocks within a video frame as the inter-frame coding complexity of that video frame. Step S3: Calculate the target number of coding bits for each image block to be encoded within the P-frame or B-frame to be encoded. Step S4: Calculate the Lagrange multipliers for the image block to be encoded within the P-frame or B-frame to be encoded. Step S5: Perform video encoding on the image block to be encoded, and then adjust the target number of coding bits for the next image block to be encoded within the P-frame or B-frame to be encoded. This invention has low hardware overhead and low implementation cost; it does not introduce a large number of complex floating-point operations, reducing the difficulty and cost of hardware implementation.

The invention discloses an intelligent operation and maintenance management method and system based on multi-modal AI, and belongs to the technical field of data artificial intelligence and industrial intelligent operation and maintenance crossing. The method comprises the steps that multi-modal data is collected and preprocessed, an improved VMD is used for conducting modal component decomposition on vibration signals, a first-order energy moment and a second-order energy moment are calculated to generate an initial vibration feature vector, and the initial vibration feature vector is calculated; a de-noising auto-encoder SDAE is used to obtain a final vibration feature vector, and the multi-modal data comprises vibration signals, energy consumption, images and text data; an improved VMD method is introduced to be combined with a Lagrange multiplier and a self-adaptive step length mechanism, a frequency domain iteration optimization solving process is constructed, meanwhile, nonlinear and deep feature enhancement is achieved through a denoising self-encoder, and therefore the extraction capacity and stability of a system for key modal components in complex vibration signals are effectively improved.

The invention discloses a multi-period non-stationary hydrological variable valuation method, and relates to the technical field of hydrological prediction. Comprising the steps of obtaining hydrological variables of a plurality of observation stations in a drainage basin at different time points as hydrological time sequences of the observation stations; constructing an experimental variation function according to the hydrological time sequence of each observation station; selecting a drift model through a trend surface analysis method and model inspection; constructing a multi-period non-stationary variable equation set comprising a Lagrange multiplier and a hydrological variable weight based on the experimental variation function and the drift model; expressing the multi-period non-stationary variable equation set as a matrix form, and calculating a Lagrange multiplier and a hydrological variable weight through a matrix inversion method; and constructing an estimation formula according to the calculation result of the Lagrange multiplier and the hydrological variable weight, and performing multi-period spatial estimation on the hydrological variable of the to-be-measured drainage basin through the estimation formula. Dependence on the number of observation stations can be reduced, and accurate estimation can be achieved only through a small amount of observation station data.

The invention discloses an aircraft recovery scheduling method and device based on a self-adaptive Lagrange multiplier, and relates to the technical field of aviation traffic control automation, and the method comprises the steps: building a constrained Markov decision process model of an aircraft recovery scheduling problem; introducing and initializing a Lagrangian multiplier by using a Lagrangian relaxation technology, and converting the constrained Markov decision process model into an unconstrained optimization problem to obtain a Lagrangian function; and performing main dual collaborative iterative optimization based on a Carlo demander-Kuhn-Tucker condition, and adaptively adjusting the Lagrange multiplier until the Lagrange multiplier converges to obtain an optimal strategy. According to the method, the constraint weight is dynamically adjusted in the main dual collaborative iteration mechanism, so that the rationality and accuracy of large-scale cluster recovery scheduling are improved.

The application discloses a kind of anti-saturation preset performance despun control methods of failed satellite and related devices, the method includes: obtaining the attitude of service spacecraft and failed satellite, establishes the dynamics model of service spacecraft and failed satellite;According to the dynamics model of service spacecraft and failed satellite, the flexible operating rod dynamics model of service spacecraft is established, the large deformation dynamics characteristics of despun brush are obtained, under the framework of the dynamics model of service spacecraft and the large deformation dynamics characteristics of despun brush, the rigid-flexible coupling dynamics model of service spacecraft is established by Lagrange multiplier method;Based on the rigid-flexible coupling dynamics model of service spacecraft, an anti-saturation preset performance controller is designed, and an anti-saturation full-state constraint control strategy is used for despun control.The application is aimed at service spacecraft with flexible operating rod, realizes efficient and accurate despun process, and greatly improves control performance.

The invention discloses a cross-modal Hash learning method oriented to high-order structure similarity and label correlation, which comprises the following steps of: inputting cross-modal information into a joint learning framework, and learning high-order similarity structure information and label correlation between samples, consistency potential representation and Hash codes at the same time; and finally generating a discrete hash code capable of fully retaining high-order similarity structure information and label correlation between samples. The learning framework uses a graph diffusion-fusion mechanism to adaptively learn the high-order structure similarity between samples, and uses an asymmetric embedding mechanism to migrate the consistent similarity and semantic relationship between the samples to a to-be-learned hash code; and obtaining high-order label correlation through label correlation adaptive learning, integrating the high-order label correlation into a framework, and obtaining high-level semantic information of a multi-modal sample to guide learning of a hash code. The variables of the Hash code learning model are subjected to interactive optimization through an iterative optimization algorithm, and the iterative optimization algorithm adopts an augmented Lagrangian multiplier method to iteratively update the variables to solve the problem.

The invention discloses a distributed optimal power flow solving method and system based on self-supervised learning, and belongs to the field of power distribution network dispatching. According to the solution method, an alternating direction multiplier method (ADMM) is fused into a training process of a neural network, and a self-supervised learning algorithm is provided. According to the method, two neural networks are respectively trained to estimate the values of a global variable and a Lagrange multiplier, and mutual supervision of the two networks is realized based on iterative interaction of an ADMM algorithm. And then, estimating values of a global variable and a Lagrangian multiplier through the trained neural network, and obtaining a solution of the distributed optimal power flow based on the estimated values. According to the distributed optimal power flow acceleration solving method provided by the invention, the training of the neural network can be realized without a label, and the solving time of the distributed optimal power flow can be effectively shortened while certain accuracy is ensured.