133 results about "Non convex optimization" patented technology

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

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 discloses a dynamic confrontation sample generation method and device based on non-convex optimization, and relates to the technical field of artificial intelligence safety and confrontation machine learning, and the method comprises the steps: training to obtain a pre-training model; applying noise to the original sample image to generate an initial confrontation sample, inputting the initial confrontation sample into the pre-training model to obtain model prediction output, and calculating a loss function value; applying symmetric differential disturbance in the neighborhood of the adversarial sample along a random orthogonal direction, estimating an interpolation gradient norm according to a loss function value, and optimizing disturbance and updating the adversarial sample along a gradient symbol direction to obtain a current adversarial sample; projecting the current adversarial sample to a legal disturbance space; judging whether the current disturbance update is valid or not based on a line search condition, and if yes, accepting the current disturbance update and entering the next round of iteration; if not, the dynamic projection range is dynamically adjusted until the iteration stop condition is met, the final adversarial sample is generated, and the efficiency and stability of adversarial sample generation are improved.

The embodiment of the invention discloses a power configuration method and device, a storage medium and terminal equipment, and is applied to the technical field of power distribution in a power system. Obtaining power distribution scene data, loading a convex neural network model, constructing an objective function based on the power distribution scene data, and constructing constraint conditions of power configuration, including physical operation constraints of power resources in a power distribution scene and output range constraints of the convex neural network model, and calculating an optimal power distribution parameter value of the power distribution equipment so as to carry out power configuration. Therefore, the most complex non-convex security constraint in power configuration is converted into a convex constraint represented by a data-driven and input convex neural network model, and the conversion enables a non-convex optimization problem which is difficult to solve originally to be changed into a convex optimization problem which can be efficiently solved, so that on the premise of ensuring the calculation efficiency, the calculation efficiency is improved, and the calculation cost is reduced. And meanwhile, the economical efficiency, the safety and the global optimality of scheduling are considered.

The invention discloses a low-altitude communication and sensing integrated resource collaborative optimization method, which comprises the following steps: firstly, carrying out three-dimensional space modeling on a base station, a cooperative unmanned aerial vehicle and a non-cooperative unmanned aerial vehicle in a system, defining communication and sensing dual-function attributes of the base station, and establishing a communication channel model and a sensing echo model; establishing a communication and perception echo model between the base station and the unmanned aerial vehicle, defining an association relationship and a power distribution optimization variable, constructing a joint performance objective function, and introducing power constraint, communication rate and positioning precision system constraint to form a mixed integer non-convex optimization problem; and finally, decomposing a mixed integer non-convex optimization problem into two sub-problems, and realizing double improvement of system communication efficiency and sensing precision through joint optimization. According to the method, high-precision positioning of the non-cooperative unmanned aerial vehicle can be realized while the communication quality of the cooperative unmanned aerial vehicle is guaranteed, so that the communication resource utilization efficiency and the airspace management safety are effectively improved, and the method is suitable for the intelligent deployment requirement of a future complex urban air network.

The invention provides a k-plane clustering roof segmentation method based on multiple geometric consistency constraints. The k-plane clustering roof segmentation method comprises the steps of performing initialization preprocessing on input original building roof point cloud data; based on each point in the original building roof point cloud data and a candidate fitting plane cluster, establishing a clustering label optimization objective function fused with triple geometric constraints; iteratively updating the clustering label and the candidate fitting plane parameter of each point by adopting an alternating minimization optimization strategy, and solving a mixed integer non-convex optimization problem until the clustering label optimization objective function is converged; and based on the converged clustering tag optimization objective function, outputting a building roof point cloud instance segmentation result, the building roof point cloud instance segmentation result comprising the clustering tag of each point and the corresponding candidate fitting plane geometric parameters. According to the method, the segmentation precision and the calculation efficiency of the building roof point cloud instance are improved.

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 wireless communication, and particularly discloses a beamforming optimization method of an active-RIS (Radio Information System) assisted integrated sensing communication and energy transfer system, which aims at maximizing radar sensing SINR (Signal to Interference plus Noise Ratio) weighted sum in an active-RIS assisted multi-integrated sensing communication and energy transfer system. Constructing a non-convex optimization problem by taking an SINR demand of a communication user, an energy acquisition demand of an energy receiving user and power constraints of a base station and an Active RIS as constraint conditions; after the constructed non-convex optimization problem is decomposed into sub-problems of a receiving filter coefficient, MFBS transmitting beam forming and an active-RIS reflection coefficient, alternate iterative optimization is carried out, so that the transmitting power of the MFBS can be reasonably distributed to communication and radar beams, and the active-RIS is controlled to select an optimal reflection phase and an amplification coefficient. Therefore, under the condition that the SINR requirement of the communication user, the EH requirement of the energy receiving user and the power constraint are met, the multi-target sensing performance of the radar is effectively improved, and meanwhile the communication quality and the energy transmission efficiency are guaranteed.

The invention relates to a Lamb wave frequency dispersion characteristic sparse reconstruction method based on non-convex optimization, and belongs to the technical field of high-end equipment perception and test.The method comprises the steps that excitation is conducted on a U-shaped suspension arm, and time domain sampling signals at the junction of a U-shaped suspension arm structure are obtained; the time domain sampling signals are preprocessed; constructing a non-convex sparse frequency dispersion curve estimation model based on the preprocessed signal; and converting the non-convex sparse frequency dispersion curve estimation model into an optimization problem, searching a convex preserving condition of a target function, selecting a forward and backward splitting algorithm for solving, obtaining frequency-wave number distribution, and further extracting a frequency dispersion curve. The method solves the problems that a traditional convex regularization method is limited in induction sparse capability and underestimates signal amplitude. Through a non-convex optimization method, the purpose of accurate estimation of the frequency dispersion curve based on fewer measurement points is achieved.

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.

An energy efficiency optimization method for multi-cell communication systems based on intelligent reflector-assisted rate division multiple access (RSMA) combines RSMA with intelligent reflector RIS. RIS intelligently regulates signal transmission links to enhance signal transmission quality, thereby improving signal quality for users at cell edges. RSMA effectively suppresses co-channel interference between users by flexibly controlling rate division strategies and beamforming. To this end, an optimization problem is constructed with the goal of maximizing system energy efficiency, jointly optimizing the beamforming vector, rate division multiple access matrix, and phase shift matrix on the base station side and the RIS side. To solve this non-convex optimization problem, an iterative solution is proposed for the beamforming, rate division, and phase shift optimization subproblems. Continuous convex optimization is used for the former, and semidefinite programming and penalty function methods are used for the latter. Compared with traditional space division multiple access and non-orthogonal frequency division multiple access, this method can achieve energy efficiency improvements of 28.5% and 10.2% for multi-cell communication systems, respectively.

The application discloses an ISAC system security transmission method for an illegal RIS scene, and the method constructs an ISAC system based on RIS assistance to simultaneously complete communication and sensing tasks. The sensing signal can effectively interfere with the IRIS-assisted eavesdropping link while completing the sensing task. In order to more effectively cope with the system security performance deterioration challenge brought by IRIS, a beamforming matrix of the radar signal is designed. Considering the uncertainty of the accurate position of the eavesdropper (Eve), the ergodic security rate is used to represent the security performance of the system, and an approximate ergodic eavesdropping rate is derived. In order to efficiently solve the two non-convex optimization problems, the ergodic objective function is appropriately converted, and then an efficient alternating optimization algorithm is proposed to solve each optimization variable. It is verified that the joint beamforming and RIS reflection design of the application is more effective in coping with the eavesdropping process of IRIS, and it is revealed that the radar beamforming design plays a key role in alleviating the influence of IRIS.

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 invention provides a high resource efficiency allocation method in an intelligent reflecting surface assisted cellular-free large-scale MIMO (Multiple Input Multiple Output) system. The method comprises the following steps: establishing an intelligent reflecting surface assisted cellular-free large-scale MIMO system model under a Rician fading channel; based on the system model, performing channel estimation by using a linear minimum mean square error method; based on the system model, constructing a resource efficiency closed expression when the zero-forcing receiving scheme is used; according to the expression, constructing a non-convex optimization problem by taking maximization of system resource efficiency as a target, and decomposing the non-convex optimization problem into a power control sub-problem and a phase optimization sub-problem; and designing an alternative optimization algorithm according to the optimized sub-problems, and solving the power control sub-problem and the phase optimization sub-problem to obtain a power control coefficient and intelligent reflector shift. When a ZF receiving scheme is used, the resource efficiency optimization algorithm has excellent signal processing capacity, the system resource efficiency is remarkably improved, operation steps are convenient to implement, and the dual requirements of a future wireless communication system for high data rate and low energy consumption are met.

The invention provides an on-line resource management system and method oriented to a requirement of integration of communication, inductance and calculation, and the method comprises the steps: carrying out the correlation scheduling constraint between a sensing target and edge intelligent equipment under the constraint of transmitting power resources, the constraint of calculation resources of a base station end, the constraint of long-term queue backlog and the constraint of average power; constructing a non-convex optimization problem of the common inductance calculation integrated system under a long-term optimization framework; and solving the non-convex optimization problem to obtain an optimal perception scheduling decision, an optimal transmitting beam forming vector of perception and communication, an optimal receiving beam forming variable of perception echoes and calculation resource allocation of a base station end. The online resource management strategy provided by the invention aims to maximize the long-term weighted average rate, meet the requirements of queue stability, average power constraint and quality of service (QoS) at the same time, and explore the cooperative gain of the communication, inductance and calculation integrated system from a long-term perspective.

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.

This invention discloses a task offloading and resource allocation method under D2D-assisted MEC, belonging to the field of wireless communication and computing resource management technology. The method introduces service-oriented devices assisted by terminal direct transmission communication technology and rationally selects task processing modes, achieving efficient resource utilization and avoiding resource waste. By jointly optimizing computation offloading, D2D selection, computing resource allocation, and spectrum resource allocation, it can both guarantee service quality and reduce network congestion, thereby improving user experience. The two-stage iterative algorithm of this invention combines block coordinate descent, reconstruction linearization technology, and convex optimization methods, effectively solving non-convex optimization problems, and has a fast convergence speed and low computational complexity. Furthermore, the D2D-assisted MEC architecture and optimization method are applicable to different types of intelligent devices and diverse application scenarios, possessing broad applicability and flexibility, and can meet the needs of future mobile edge computing development.

The invention discloses an energy efficiency optimization method for an active RIS auxiliary cell-free mMIMO system, and the method comprises the steps: firstly, building a downlink transmission data and system consumption total power model of the active RIS auxiliary cell-free mMIMO system, and giving a downlink rate and energy efficiency expression; then, designing a communication node downlink transmission power allocation scheme by formulating a multi-objective function optimization problem, including maximizing a downlink rate and minimizing the total consumed power of the system; in order to solve the non-convex optimization problem, a weighted sum method and an arithmetic mean inequality are used for converting the non-convex optimization problem into a generalized geometric programming problem, and then an iteration mode is adopted for solving the non-convex optimization problem; and finally, taking the obtained optimal solution as a final power distribution scheme and calculating the energy efficiency of the system. According to the method, the total power consumed by the system is reduced while the downlink rate sum is improved, the energy efficiency of the system is improved, and the operation cost of the system is reduced.

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 two-dimensional wave field reconstruction method based on compressed sampling, and belongs to the technical field of signal processing and sensors. According to the method, original time domain signals of a two-dimensional wave field are expressed as a three-dimensional matrix, random sampling is carried out through a Bernoulli distribution random sampling matrix, after the three-dimensional matrix is reduced into a two-dimensional matrix, a group sparse perception convolutional neural network is constructed to serve as a non-convex optimization solver, a real part and an imaginary part of a complex signal are processed, group sparse constraint is applied, and a non-convex optimization solver is constructed. And reconstructing the basis coefficient matrix to obtain an original signal reconstruction result, and finally raising the dimension to restore the original signal into a three-dimensional wave field. According to the method, the sampling rate is reduced, the reconstruction precision is improved, large-scale data set training is not needed, each training is an independent reconstruction process, and the method is suitable for the fields of acoustic imaging, seismic exploration and the like.

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.