80 results about "Non convex optimization" patented technology

A method for the joint active and passive beamforming and received signal optimization in an ISAC system assisted by dual IRSs is provided. The method jointly optimizes active beamforming at Base Station (BS), reception of sensing signals at the Base Station (BS), and passive beamforming at IRSs, so as to maximize communication sum-rate of users while ensuring that SNR of sensing signals meets a minimum requirement. To address the complex non-convex optimization problem, the method first applies fractional programming to decouple problem, then adopts successive convex approximation algorithm and alternating direction method of multipliers to transform intractable non-convex problem into multiple tractable subproblems, and finally employs an alternating optimization method to efficiently acquire the high-quality suboptimal solutions. The simulation results demonstrate that disclosed scheme exhibits satisfactory convergence and effectiveness, and can significantly improve the performance of IRS-assisted ISAC systems.

The invention relates to the technical field of power system stability constraint optimization and computer algorithms, and provides an energy storage fault ride-through optimization method considering optical storage access weak power grid stability constraint, which comprises the following steps: collecting power grid operation data to generate a dynamic enhancement data set; constructing and correcting a digital twinborn body on line; generating a guide vector field and initializing an optimization framework; executing hybrid iteration to generate a candidate strategy; an optimization process is fed back through simulation evaluation; verifying a decision output control instruction after convergence; and deploying an instruction and realizing online updating. According to the method, the characteristics of the power grid are accurately modeled by using the digital twin, optimization search is guided in combination with the guide vector field, the non-convex optimization problem caused by stability constraints is effectively processed through a hybrid iteration strategy, and the voltage and frequency recovery capability in the fault ride-through process is improved.

The invention provides a tunnel activity measurement method with a bidirectional loading demand, and belongs to the technical field of tunnel activity measurement, and the method comprises the steps: collecting a multi-dimensional force signal and a displacement signal, carrying out the digital processing, building a mechanical coupling tensor matrix, and obtaining a preliminary decoupling force component through a condition number adaptive inversion strategy; tensor network high-order correlation modeling and density matrix reforming swarm optimization are utilized for optimization to obtain an accurate decoupling force component, a simulated annealing particle swarm optimization algorithm is combined for solving a non-convex optimization objective function to obtain real mechanical response parameters, defects are identified through ultrasonic detection, and an influence coefficient matrix is established; a Bayesian inversion algorithm is adopted to remove defect influence to obtain corrected tunnel activity parameters, tunnel safety monitoring is finally realized through time sequence analysis and stress redistribution evaluation, and the technical problem of insufficient measurement precision caused by multi-dimensional force signal coupling interference is solved.

The invention relates to the technical field of intelligent sensors, in particular to an unmanned aerial vehicle track intelligent tracking method based on a millimeter-wave radar, which comprises the following steps: receiving echo signals collected by the front end of the millimeter-wave radar, carrying out constant false alarm rate detection and Doppler frequency shift extraction on the echo signals, generating an unmanned aerial vehicle target space coordinate set, and carrying out target tracking on the unmanned aerial vehicle. And mapping the target space coordinate set of the unmanned aerial vehicle to a three-dimensional Euclidean space. According to the method, a non-convex optimization objective function containing beam switching time cost and precision constraint is constructed, and mathematical optimal solution of radar beam resources is realized. The generated adaptive scheduling instruction is used for adjusting the pulse repetition frequency and the beam residence time, it is ensured that radar front-end parameters dynamically evolve along with the target state, overall centroid tracking is emphasized for rigid body formation so as to save resources, independent high-frequency refreshing is emphasized for discrete targets so as to ensure the precision, and the accuracy is improved. And the continuity and robustness of trajectory tracking of the unmanned aerial vehicle in a complex electromagnetic environment are improved.

The invention discloses an implementation method of a sub-connection active STAR-RIS assisted secure communication system. A base station, a sub-connection active STAR-RIS, a legal user and an eavesdropping user are included. According to the invention, M units of STAR-RIS are grouped into L sub-arrays, and an amplifier is shared to reduce power consumption and cost; with maximization of system secrecy energy efficiency as a target, base station beam forming, STAR-RIS reflection / transmission coefficients and amplifier amplification coefficients are jointly optimized; and solving the non-convex optimization problem by adopting an efficient algorithm based on block coordinate descent. According to the invention, the hardware overhead and the system power consumption are effectively reduced through the sub-connection structure, meanwhile, the secure transmission efficiency of the system is remarkably improved through joint optimization, multiplicative fading is overcome, and an efficient and reliable solution can be provided for 6G secure communication.

The invention discloses a multi-user single-eavesdropper resource management and optimization method based on a Stackelberg game, and in a network scene with eavesdropping equipment, although each user can improve the data transmission rate by improving the respective transmitting power, the data eavesdropping rate of the eavesdropping equipment is also increased at the same time. How to carry out modeling of effective resource allocation for data sending power of users is a key problem to be solved in the invention. Based on the above problem, the invention provides a multi-user single-eavesdropper resource management and optimization method based on the Stackelberg game, the multi-user power distribution and eavesdropper eavesdropping rate limitation problem jointly form a game model, and Taylor expansion is adopted to convert the original non-convex optimization problem into a convex optimization problem for modeling.

This invention proposes an integrated beamforming method and system for communication and sensing based on reconfigurable smart surfaces, belonging to the field of wireless communication technology. The aim is to design suitable beamforming to maximize communication and data rate while ensuring adequate radar sensing performance. The method includes: first, constructing an ISAC system containing J RIS elements, a base station (BS), and M single-antenna mobile users (MUs), and completing the modeling of baseband signals and user received signals; second, constructing user communication and data rate models and radar target detection signal-to-noise ratio models, establishing unit mode constraints on RIS reflection coefficients and transmit power constraints; third, employing an improved algorithm combining alternating optimization and fractional programming to decompose the non-convex optimization problem into sub-problems for iterative solution, ultimately maximizing communication and data rate and radar sensing performance, while also meeting the dual functional requirements of communication and sensing. This invention is applicable to providing target detection and communication functions for vehicles.

The invention discloses a symbiotic radio system design and optimization method based on multiple active intelligent reflection surfaces. According to the method, high-efficiency backscatter communication is realized while the performance of the main transmission system is enhanced by using the intelligent reflection surface. A plurality of active intelligent reflecting surfaces are configured in the system, the main transmission performance is enhanced, and meanwhile, the active intelligent reflecting surfaces serve as backscattering equipment in turn, and transmission of secondary information is achieved by conducting scattering modulation on incident main signals. The method aims at maximizing the main transmission rate of the system, and jointly optimizes the base station sending power, the active beam forming vector and the reflection coefficient of the intelligent reflection surface. An original non-convex optimization problem is decomposed into a plurality of sub-problems, semi-definite relaxation, continuous convex approximation and other methods are adopted for conversion, and finally an optimal solution is obtained through iterative solution. Compared with a scheme based on a passive intelligent reflection surface and an active intelligent reflection surface scheme based on phase random, a higher main transmission rate can be realized under the condition of ensuring the same secondary symbol signal to interference plus noise ratio.

The present application relates to a kind of facing polynomial fuzzy control system cost control stability judging method, comprising the following steps: establishing polynomial fuzzy model;According to the first SOS condition based on Lyapunov function, try to solve the first polynomial matrix and the second polynomial matrix, omit non-convex term in solving process, judge whether it is successful, if yes, based on the first polynomial matrix and the second polynomial matrix, obtain feedback gain;Based on feedback gain, according to the second SOS condition, try to solve positive definite matrix, judge whether it is successful, if yes, based on positive definite matrix, construct polynomial Lyapunov function, realize cost control analysis based on polynomial Lyapunov function.Compared with prior art, the present application solves the non-convex optimization problem in cost control in two steps, avoids the influence of the constraint existing in input matrix itself on the flexibility of fuzzy control system design, and improves the performance of control system by obtaining the numerical value of cost J.

This invention discloses a communication method, apparatus, and system based on reconfigurable smart surfaces, belonging to the field of wireless communication technology. The communication method based on reconfigurable smart surfaces jointly optimizes the transmitter's current beamforming vector and current RIS reflection parameters based on real-time channel estimation results using a deep deterministic policy gradient algorithm. A joint optimization model of the transmitter's beamforming and RIS parameters is established, transforming the non-convex optimization problem into a Markov decision process, which is efficiently solved using the deep deterministic policy gradient algorithm, thereby improving the transmission capability of the communication. Furthermore, the introduction of reconfigurable smart surface technology, with its design featuring a reflection unit possessing independent amplitude-phase modulation capability, overcomes the fixed amplitude limitation of traditional passive RIS, effectively overcoming dual-fading path loss, and thus improving the anti-interference performance of the communication system in complex electromagnetic environments such as high spectral density and high interference power.

The invention provides an uplink MIMO-NOMA (Multiple Input Multiple Output-Non-Orthogonal Multiple Access) system optimization method with a maximized sum rate, which comprises the following steps: firstly, in order to suppress interference between a transmission cluster and a reflection cluster, providing an inter-cluster SIC method: clustering users according to transmission characteristics, adopting space division multiple access in each cluster, and performing inter-cluster SIC of NOMA transmission so as to reduce the interference between the clusters; secondly, a non-convex optimization problem aiming at maximizing the total rate of the system is formulated and solved by using an alternating optimization framework, in the framework, a receiving equalizer is optimized according to a minimum mean square error standard, and transmitting power control of a user is processed through successive convex approximation, so that sub-problems are easy to process; and finally, the beam forming of the active STAR-RIS adopts a sequential rotation design. According to the invention, the dual-fading effect is effectively overcome by adjusting the phase shift and amplitude of electromagnetic waves, an alternating optimization framework is also provided to solve the described non-convex problem, user power is controlled through continuous convex approximation, and active STAR-RIS beam forming is realized through sequential rotation.

The invention discloses a double-domain non-convex joint optimization equipment state signal denoising method and system, and the method comprises the steps: carrying out the modeling of a partial discharge signal containing aliasing noise, obtaining a one-dimensional observation signal, and converting the one-dimensional observation signal into a two-dimensional observation matrix; based on the two-dimensional observation matrix, obtaining a double-domain collaborative regular model through double-domain decomposition, and optimizing the double-domain collaborative regular model by adopting a non-convex penalty function to obtain a non-convex optimized regular model; and carrying out iterative solution on the non-convex optimization regular model by adopting an improved collaborative alternating direction multiplier algorithm, obtaining an updated two-dimensional observation matrix of the partial discharge signal in combination with an original residual threshold and a dual residual threshold, and carrying out inverse conversion on the two-dimensional observation matrix of the partial discharge signal to obtain a denoised partial discharge signal. Cooperative iteration of double-domain variables is realized based on an improved cooperative alternating direction multiplier algorithm, double-domain residual index dynamic judgment convergence is designed, and redundant iteration is reduced while denoising precision is guaranteed.

The invention discloses a large-scale visual positioning optimization method based on an OCP theory, and relates to the technical field of deep learning. The method comprises the following steps: defining a target function of a visual positioning model; initializing model parameters, momentum vectors and related hyper-parameters; performing iterative optimization training, calculating a small-batch stochastic gradient in each iteration, performing exponential moving average and deviation correction on the gradient by using a diagonal element of an approximate Hessian matrix of element-by-element square of the gradient, performing weight attenuation, and finally calculating a parameter update quantity and updating a model by using an optimization method based on an OCP theory; and outputting the model with the optimal performance on the verification set after iteration is finished. According to the method, the OCP theory and approximate second-order information are combined, a new large-scale visual positioning method is provided, the convergence speed, stability and final test precision of visual positioning model training are effectively improved on the premise that linear complexity is kept, and the method is particularly suitable for large-scale non-convex optimization scenes.

The invention discloses a distributed driving vehicle integrated three-way cooperative control method based on FTV-MPC. Comprising the following steps: constructing a multi-domain coupling three-way control model of a distributed driving vehicle, linearizing and discretizing the multi-domain coupling three-way control model based on a planned trajectory, and constructing a state prediction error vector according to an obtained state matrix and a control matrix; normalizing the state planning error and the control planning error based on a multi-domain coupling three-way control model, and constructing a state weight matrix and a control weight matrix based on a normalization result; and constructing an FTV-MPC cost function according to the state prediction error vector, the state weight matrix and the control weight matrix, and solving the FTV-MPC cost function to obtain an optimal control sequence of the distributed driving vehicle. According to the method, the problems that distributed driving vehicle three-way cooperative control lacks a unified model, multiple control targets are difficult to balance and non-convex optimization solution is difficult are solved, and high-precision integrated three-way cooperative control is realized.

The invention discloses a high-reliability double-plane lensless on-sheet holographic microscopic phase image reconstruction method. The method comprises the following steps: firstly, acquiring two frames of holograms with different heights, acquiring optimized initial object plane complex amplitude distribution by utilizing a biplane intensity transmission equation, then calculating an object plane complex amplitude gradient value by adopting a biplane Wirtinger operator and updating the object plane complex amplitude gradient value, then applying complex amplitude total variation minimization and physical absorption double constraints, accelerating updating by utilizing Nesterov momentum, and finally calculating the object plane complex amplitude gradient value according to the updated object plane complex amplitude gradient value. And finally, a high-reliability object phase image is obtained through iteration. According to the method, the suitability of the non-convex optimization problem under the limited measurement condition can be effectively improved, and the calculation efficiency and precision of phase reconstruction are improved. Compared with an existing biplane phase reconstruction algorithm, the method can achieve lens-free on-sheet holographic microscopic phase image reconstruction with higher fidelity and higher imaging precision, and is suitable for processing complex samples and reconstruction scenes needing higher fidelity.

The invention relates to a flexible production line equipment collaborative energy-saving control system based on a random ADMM, and the system comprises a multi-dimensional energy consumption sensing module which is used for collecting the time sequence power data, equipment state transition data and environment parameter data of equipment in a production line; the data transmission module is used for transmitting the time sequence power data, the equipment state transfer data and the environment parameter data to the central control system; the central control system is internally integrated with a cross-industry federated learning module which is used for carrying out federated learning and collaborative optimization of multi-source energy consumption data based on a privacy protection mechanism; the non-convex optimization model is used for performing non-convex optimization solution on the equipment start-stop strategy by adopting a random ADMM algorithm to generate a global optimal equipment start-stop strategy; and the dynamic correction module is used for receiving the collaborative optimization data and the global optimal equipment start-stop strategy, carrying out fusion analysis and optimization calculation, and then feeding back the optimized final control strategy to a production line execution mechanism so as to regulate and control the start-stop state of the production line equipment.

Disclosed are a dynamic decision making and planning method and system for multi-objective operation of train virtual coupling. The method comprises the steps: S1, designing an overall objective function and single objective functions of a lead vehicle and a following vehicle, and designing target weights in the overall objective function and the single objective functions, to obtain a weighted multi-objective function; S2, on the basis of non-convex optimization set decomposition of a Monte Carlo decision tree, converting a non-convex optimization problem into a convex optimization problem, to obtain a reachable set; and in the reachable set searched by the Monte Carlo decision tree, using an evolutionary multi-objective optimization algorithm to solve the weighted multi-objective function, obtaining a planning curve sequence of the lead vehicle and the following vehicle. The present invention can achieve overall multi-objective virtual coupling control, and has high control efficiency and accuracy.

The invention provides a self-adaptive multi-order cooperative non-convex optimization control method, device and system. According to the method, rapid convergence is realized by adopting a local optimization strategy based on gradient information, and second-order curvature analysis is automatically introduced after a first-order stable point is reached; and if the second-order stability condition is met, activating high-order information as required, and constructing directional disturbance in the identified escapable subspace to break through stagnation. The above process is iteratively executed until the effective escape direction cannot be identified under the current information. According to the method, on the premise that the calculation overhead of the main optimization stage is not remarkably increased, the capacity of the algorithm for escaping various flat saddle points (including degraded saddle points and weak positive curvature saddle points) and converging to a better local solution is remarkably improved, and the calculation efficiency and the solution quality are effectively balanced.

The application belongs to the technical field of communication system, and particularly relates to a method and system for deriving optimal degree distribution of regular LDPC code based on AWGN channel. The method comprises the following steps: S1, taking the maximization of code rate of the regular LDPC code as a criterion and taking the decoding success as a prerequisite, a target function with a constraint condition is constructed; S2, fixing the degree d v of the variable node, based on the fixed point analysis theory, a theoretical analytic expression of the degree d c of the check node in the LDPC code is derived, and an inverse function of the target function in the non-convex optimization problem is solved; S3, based on the mathematical set theory, the uncertainty in the inverse function solving problem in the step S2 is solved, and the optimal degree distribution of the LDPC code under the AWGN channel is obtained. The application has the characteristics of improving the channel coding performance in the communication system.

A kind of unmanned aerial vehicle and intelligent reflector auxiliary communication system trajectory planning and resource allocation method, by constructing signal transmission model, optimizing system minimum secrecy rate, optimizing unmanned aerial vehicle trajectory, optimizing mixed intelligent reflector phase shift coefficient, optimizing user scheduling strategy and user transmission power step composition.The present application satisfies the premise of unmanned aerial vehicle flight constraint, mixed intelligent reflector power constraint, user transmission power constraint and user scheduling constraint, jointly optimizes unmanned aerial vehicle trajectory, mixed intelligent reflector phase shift coefficient, user scheduling strategy and user transmission power;Since the present application adopts alternating iterative algorithm combined with continuous convex approximation method, effectively solves the non-convex optimization problem, improves the system minimum secrecy rate.

The application discloses a pre-coding optimization method for a 6G-oriented MIMO-OTFS system, which is based on a maximum signal-to-noise ratio criterion and constraints the total power of a base station to save resources and achieve the purpose of designing an optimal pre-coder.The implementation steps are as follows: firstly, a channel matrix is determined according to a system model; secondly, a delay-Doppler domain input-output relationship is obtained; thirdly, a target function problem with a pre-coding matrix as a variable is established according to the input-output relationship; and finally, the Dinkelbach algorithm is used to convert the target function into a form of subtraction of auxiliary variables, fix a variable, convert the original non-convex optimization problem into a semi-definite programming (SDP) problem, and obtain an optimal pre-coding matrix.The optimal pre-coder designed in this way not only eliminates the inter-user interference, but also eliminates the inter-symbol interference of the same user.The communication performance between different users is effectively ensured through the design and optimization of the pre-coding matrix, and the bit error rate of the MIMO-OTFS system is further reduced.

The invention belongs to the technical field of wireless communication, and discloses a sight distance multi-antenna reliability and capacity cooperative improvement method and related equipment. The method comprises the following steps: modulating and orthogonally mapping a useful signal to generate an orthogonal mode emission signal; the signal is transmitted after being formed by a transmitting beam, and a receiving end receives the signal and preliminarily separates the signal to obtain a solution orthogonal mode receiving signal; constructing a multi-constraint coupled non-convex optimization problem which takes system bit error rate minimization as a target; solving the problem based on a reinforcement learning framework to obtain an optimal sending beam forming matrix and a power distribution strategy, and further solving a receiving beam forming matrix to realize interference suppression and signal recovery; interference channel environment and intensity fluctuation are monitored in real time, receiving and transmitting end strategies and parameters are dynamically iteratively optimized, and communication reliability and capacity are continuously improved. According to the invention, the technical problem of wireless communication transmission reliability and capacity improvement in a complex electromagnetic interference environment is solved.

The application provides a power grid fault traveling wave positioning method based on a fusion optimization algorithm, comprising: establishing a dynamic virtual fault framework model of a power grid, randomly generating a plurality of virtual fault particles containing position coordinates, and constructing an initial virtual fault particle cluster; using a shortest path algorithm to calculate the topological path length of each virtual fault particle in the virtual fault particle cluster to all measurement points in the power grid, and then calculating a virtual fault space-time feature vector; setting the traveling wave arrival time when a real fault occurs as a target clustering centroid in a feature space, and calculating a feature matching fitness; constructing a non-convex optimization problem with the optimal feature matching fitness as a target, solving the problem using a fusion optimization algorithm, and thus obtaining a fault positioning result and a traveling wave velocity, so as to realize traveling wave positioning. The application can solve the problems of local optimum, slow convergence speed and high calculation cost in the prior art.

The invention discloses an intelligent high-reliability semantic communication method oriented to a low-altitude intelligent mobile network, and relates to a low-altitude edge computing network assisted by an unmanned aerial vehicle. According to the method, balancing between task delay of ground equipment and energy consumption of the unmanned aerial vehicle is realized in an interference environment by jointly optimizing task unloading scheduling, computing resource allocation and a three-dimensional trajectory of the unmanned aerial vehicle. For a non-convex optimization problem caused by discrete and continuous variable coupling, an accurate penalty alternating algorithm and a deep reinforcement learning optimization framework (EPA-SP3O) based on soft penalty are designed. According to the method, the average time delay and energy consumption of the system can be remarkably reduced on the premise of meeting semantic accuracy constraints, and the interference resistance and robustness in a complex urban environment are improved.

This invention provides a wavefront phase recovery method, an atmospheric channel estimation method, and an apparatus for atmospheric laser communication systems. The method includes: acquiring multiple sets of deflection measurement amplitude data corresponding to a target distorted beam; obtaining a first complex amplitude corresponding to each set of deflection measurement amplitude data based on a non-convex initialization prediction algorithm; iteratively operating on each set of first complex amplitudes using a gradient descent-based non-convex optimization algorithm to obtain a reconstructed complex amplitude that meets the target recovery conditions; and determining the wavefront phase information of the target distorted beam based on the reconstructed complex amplitude. This invention can obtain the globally optimal wavefront phase information when using a gradient descent-based non-convex optimization algorithm for wavefront phase recovery, thereby improving the accuracy of phase recovery.

The invention discloses a trajectory and power joint optimization method based on a honeycomb-eliminating and inductance-eliminating integrated unmanned aerial vehicle system. The method comprises the following steps: constructing a honeycomb-eliminating and inductance-eliminating integrated unmanned aerial vehicle system model; establishing a non-convex optimization problem model of the honeycomb-eliminating and inductance-eliminating integrated unmanned aerial vehicle system; selecting an unmanned aerial vehicle as a receiving unmanned aerial vehicle; dividing a non-convex problem into two sub-problems, and respectively solving the two sub-problems by adopting a successive convex approximation method; solving an optimization problem by adopting an alternating optimization method; and finally, obtaining an optimal solution of a corresponding convex problem by utilizing a convex optimization solving tool. According to the method, the sensing rate of the system can be maximized under the condition that the requirements of communication users are met. Compared with a reference scheme that only the transmitting power is optimized in linear constant-speed flight, the method can achieve a higher sensing rate.

The application provides a global sparse texture filtering method based on edge structure preservation, including introducing a texture inhibition function in a penalty term, and constraining the gradient of an output image, the texture inhibition function inhibits texture, noise and unnecessary detail information in the image by setting two threshold values, then using the inhibited gradient as the input of the denominator of the penalty term, so that the penalty term can sufficiently distinguish texture and structure; sparse regular L1 norm is used to constrain the penalty term, non-convex optimization is converted into a convex optimization problem by introducing a sub-gradient, and an alternating direction multiplier method is used for iterative solution, so that better edge preservation is achieved; sparse L p Norm is used to constrain the penalty term and a preconditioned conjugate gradient method is used to accelerate and improve the calculation efficiency, so that more robust and sparse image smoothing effect is achieved. The application can improve the robustness of the algorithm in distinguishing texture and structure, retain better semantic information, and achieve better edge structure preservation and smoothing performance.

This invention provides a method for constructing a wireless network resource allocation system. The system is used to obtain a wireless network resource allocation strategy based on the wireless network state. The method includes: S1, obtaining a non-convex optimization objective with interruption probability constraints corresponding to wireless communication requirements under an imperfect global channel state information (CSI) environment; S2, transforming the obtained non-convex optimization objective to obtain a non-convex optimization objective without interruption probability constraints; S3, obtaining imperfect global channel state information of the wireless network; S4, using the obtained non-convex optimization objective as the training target and the imperfect global channel state information from step S3 as input, training the initial resource allocation system to convergence using reinforcement learning. This invention uses more realistic CSI to train the learning-based initial allocation system, improving the convergence rate of the wireless network resource allocation system and enhancing its performance in achieving the optimization objective.

The present application relates to a kind of resource optimization method based on RIS auxiliary wireless energy-carrying backscattering communication system, belong to low-power Internet of Things technical field, the present application is aimed at the problem that transmission performance is poor caused by obstruction obstruction and channel perturbation, consider transmission time constraint, energy collection constraint, the maximum interference power constraint allowed by main receiver, channel uncertainty constraint and intelligent metasurface phase shift constraint, construct energy efficiency maximization resource management model based on bounded channel uncertainty, utilize worst criterion method and Cauchy inequality to convert original non-convex optimization problem containing parameter uncertainty into deterministic problem;By variable substitution method, continuous convex approximation method, block coordinate descent method and semi-positive definite programming method, the deterministic problem is converted into convex problem solution, the present application can effectively improve system total energy efficiency, while reducing main user interruption probability, improve signal propagation path.

The invention provides an RIS-assisted cellular-free large-scale MIMO network security energy efficiency optimization method. The method comprises the following steps: establishing an RIS-assisted cellular-free large-scale MIMO network model under a Rician fading channel; acquiring estimated channel state information of aggregation channels between the base station and the user equipment and between the base station and the eavesdropper by adopting a linear minimum mean square error estimation method; performing downlink channel data transmission based on the estimated channel state information; deriving a lower bound of a user reachable rate and an upper bound of an eavesdropper leakage rate according to a downlink channel data transmission result; deducing a safety reachable rate expression and a safety energy efficiency expression according to the lower bound of the user reachable rate and the upper bound of the eavesdropper leakage rate; based on the safety energy efficiency expression, a non-convex optimization problem containing RIS phase shift and power distribution is established, an iterative optimization algorithm is adopted for solving, and the iterative optimization algorithm decouples the original problem into an RIS phase shift optimization sub-problem and a power distribution optimization sub-problem for alternate solving until convergence is achieved to a local optimal solution meeting all constraints.