60 results about "Quantum genetic algorithm" patented technology

The invention relates to the technical field of structure optimization design, and particularly discloses a box-type beam structure lightweight design method based on topological optimization, which comprises the following steps: constructing a multi-physics field coupled three-dimensional parameterized model, and performing cross-scale analysis on integrated structure rigidity, thermal deformation, vibration mode and fatigue life to obtain a three-dimensional parameterized model; a variable density method and level set method mixed topological optimization strategy is adopted, and an initial material distribution scheme meeting the structural function integrity is generated by modeling the relationship between density and stress strain; starting a quantum genetic algorithm and deep reinforcement learning fused intelligent optimization engine; according to the method, through the multi-physics field coupled three-dimensional parameterized model, a variable density method and level set method mixed topological optimization strategy is combined, the relation between the structural lightweight target and the additive manufacturing process is effectively balanced, multi-physics field analysis of thermal response, vibration, fatigue and the like is introduced, and the structural lightweight target is obtained. And it is guaranteed that the box-type beam meets the mechanical property and other functional requirements in actual application.

The invention discloses a cloud side-end cooperative task scheduling and efficiency optimization method and system for heterogeneous patrol resources, and relates to the technical field of intelligent scheduling and resource optimization. According to the method, accurate perception of a resource state is realized by constructing a digital twinborn and federated learning mechanism, resource contention conflicts are solved by adopting a space-time diagram attention network and multi-agent reinforcement learning, and multi-target optimization and trusted execution are realized in combination with a quantum genetic algorithm and a block chain smart contract. Finally, the stability of the system is verified through Lyapunov optimization, a complete scheduling system from resource perception and conflict resolution to steady state maintenance is formed, and the task scheduling efficiency and the system stability in the heterogeneous resource environment are remarkably improved.

The invention relates to the technical field of charging parking space guiding control, and particularly discloses a parking space charging reservation and intelligent guiding method, device, equipment and medium, and the method comprises the steps: obtaining vehicle battery state data and a user reservation time window through an Internet of Things terminal; synchronously acquiring charging pile state data, real-time traffic flow data and power grid load information in the parking lot; fusing the obtained multi-source heterogeneous data based on a federated learning framework, constructing a dynamic charging demand prediction model, and predicting charging pile occupancy rate distribution in a preset time period by using a space-time diagram convolutional network; through the combination of the dynamic charging demand prediction model and the space-time diagram convolutional network, the occupancy rate distribution of the charging piles is accurately predicted, the scheduling strategy of the charging piles is adjusted in real time, the problem of unreasonable resource allocation is avoided, and the waiting time of a user is shortened; the application of the quantum genetic algorithm optimizes the matching process of the charging pile through global optimal solution search, thereby ensuring the balance of the power grid load and the rationality of the charging strategy.

The invention relates to the technical field of automobile automatic transmission, and discloses an AMT bearing operation state detection system and method under an active and passive switching working condition, and the method comprises the steps: collecting real-time data through a vibration acceleration sensor, a current sensor, a temperature sensor and a rotating speed encoder, carrying out the preprocessing of envelope demodulation, Kalman filtering and the like, and carrying out the detection of the operation state of an AMT bearing; and generating a time-frequency characteristic matrix by using variational mode decomposition and Hilbert transform. And inputting the matrix into a probabilistic neural network model adopting a sliding time window mechanism, and outputting a bearing health state probability value. A multi-parameter state evaluation model optimized by a quantum genetic algorithm is constructed, an optimal feature combination is obtained, and early warning levels and maintenance suggestions are output through a belief rule base inference device in combination with a hierarchical diagnosis control model (including an acquisition layer, an analysis layer and an execution layer). The system is further provided with a signal verification module to ensure data reliability. According to the invention, multi-source data fusion and dynamic adaptive diagnosis are realized, and the state detection precision and real-time performance of the AMT bearing under complex working conditions are improved.

The invention discloses a new energy medium-voltage switch intelligent control method based on multi-data interaction, and particularly relates to the field of switch intelligent control. According to the method, electrical, mechanical and temperature multi-dimensional operation data are collected in real time through a multi-source sensor, fault diagnosis is performed by adopting a hybrid reasoning architecture, features are extracted in combination with Hilbert-Huang transform and Mel cepstrum coefficient algorithms, and a three-level reasoning mechanism is constructed to realize accurate classification; based on a diagnosis result, a system dynamically optimizes a control strategy, the control strategy optimizes a switch-on and switch-off time sequence based on phase-space reconstruction and LSTM prediction, plasma impedance closed-loop adjustment and a quantum genetic algorithm are combined to realize arc suppression and mechanical compensation, and a six-dimensional compensation vector is introduced to correct mechanical operation deviation; the self-learning unit is based on a depth deterministic strategy gradient and an online knowledge distillation technology, verifies the validity of parameter update by using digital twin simulation, and forms a fault feature-control strategy-maintenance scheme triple knowledge base.

The invention discloses a switch load balancing method based on fuzzy logic control, and the method comprises the following steps: S1, monitoring a network state in real time through quantum sensing equipment, and obtaining related parameters; s2, transmitting the parameters to a fuzzy logic controller for processing; s3, generating a load distribution strategy according to a predefined rule; s4, transmitting the preliminary strategy to each switch node in the block chain network, and recording related data; s5, verifying the data by using a distributed consensus mechanism of the block chain; s6, dynamically evaluating a node trust value based on an enhanced fuzzy trust model, and selecting nodes meeting requirements to participate in a load balancing game decision; s7, carrying out cooperative calculation on the selected nodes by utilizing a game theory cooperation mechanism, and generating a globally optimized load distribution strategy; and S8, optimizing a load distribution strategy through a quantum genetic algorithm, and performing automatic execution through an intelligent contract. According to the method, the advantages of fuzzy logic, the block chain and quantum computing are combined, and the intelligence and safety of load balancing are improved.

The invention, which relates to the technical field of the logistics supply chain, discloses a digital intelligence park supply chain warehouse distribution integrated service system comprising a dynamic warehouse resource collaborative scheduling module, a multi-modal intelligent path planning module and a block chain federal learning security center module. Data are collected in real time through 5G-IoT, storage locations and equipment are dynamically allocated in combination with a quantum genetic algorithm, and the storage proportion of cold and hot regions is optimized; planning a path in real time by using an improved ant colony algorithm and a dynamic cost function, and supporting multi-carrier collaborative distribution of unmanned vehicles and unmanned aerial vehicles; a hierarchical block chain and federated learning technology is adopted to ensure data encryption storage and cross-enterprise security sharing; the system improves the storage efficiency and the inventory turnover rate, shortens the distribution time, reduces the energy consumption, guarantees the data privacy and compliance, and is suitable for the supply chain management of a modern park.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention provides a tunnel blasting vibration reduction method and system based on asymmetric slotting, relates to the technical field of geotechnical engineering numerical simulation, and provides an integrated intelligent blasting vibration reduction system of asymmetric slotting structure optimization-quantum genetic algorithm collaborative optimization-model dynamic regulation. Triple fusion of a physical mechanism, a mathematical algorithm and an information technology of proximity tunnel blasting vibration control is creatively achieved, the bottleneck that a traditional method depends on experience trial and error, model static lag and vibration control and construction efficiency are difficult to consider at the same time is broken through, vibration speed control precision is improved, tunneling efficiency is improved, and meanwhile the construction efficiency is improved. The dynamic adaptability to complex geological conditions is remarkably enhanced, and a new generation of integrated solution is provided for safe, efficient and intelligent construction of the adjacent tunnel.

The invention relates to the technical field of cable line operation and maintenance, in particular to a cable fault diagnosis method and system based on quantum optimization variational mode decomposition and multi-scale evaluation, and the method comprises the steps: employing a quantum genetic algorithm to optimize the modal number and penalty factor of variational mode decomposition through a multi-target fitness function; constructing a multi-scale evaluation system fusing time domain, frequency domain and wavelet domain features, and screening an optimal modal component; and carrying out multi-feature fusion wave head calibration on the optimal modal component to calibrate the arrival time of the wave head, and realizing accurate positioning of a fault point in combination with a double-end traveling wave method. According to the method, the ranging error can be effectively reduced under the conditions of high transition resistance and low signal-to-noise ratio, the robustness and engineering applicability of cable fault diagnosis are improved, and the problems of attenuation distortion of fault traveling wave signals and difficulty in wave head calibration under the conditions of strong noise and high transition resistance are solved.

The invention belongs to the technical field of wireless communication, and particularly relates to an Internet of Things oriented 5G communication module data transmission system, which comprises a communication decision module, a channel state prediction module and a data transmission module which are in communication connection in sequence, and is characterized in that the communication decision module adopts a quantum genetic algorithm, encodes decision variables by quantum bits, and transmits the decision variables to the channel state prediction module; a lightweight scheduling strategy is generated through population iteration, the too high requirement of a complex optimization algorithm on the computing power of an equipment end is avoided, and a low-power-consumption microprocessor is adapted; meanwhile, the collection frequency is dynamically adjusted according to edge node requirements, invalid data collection is reduced, and equipment energy consumption is reduced. The channel state prediction module trains a model by maximizing an average reward value and realizes accurate prediction in combination with an LSTM model of an attention mechanism, and the data transmission module dynamically adjusts a compression encryption strategy according to a channel state, so that the transmission energy consumption is reduced while the safety is guaranteed, and the energy consumption upside down is avoided.

The invention relates to the technical field of spraying control, and discloses a spraying process self-adaptive adjustment method and system based on temperature measurement, and the method comprises the steps: presetting multi-sensor parameters, calculating a dynamic weight, calibrating sensors, and solving the problems of real-time interference and cross-process sensitivity difference; acquiring environmental data of a target spraying process, generating a global temperature field thermodynamic diagram, associating the film thickness with an environmental interference label, and providing a three-dimensional reference; coating workpiece heat flux and empty workpiece heat flux are collected, net heat flux and real-time reaction enthalpy change are calculated, a three-dimensional curing degree model is constructed, and the real-time curing degree is calculated to reflect the coating film curing progress; based on real-time curing degree fusion temperature spatial-temporal characteristics and phase change characteristics, core characteristics are extracted, a digital twin model is established to preview a process adjustment effect, an optimal control vector is output by constructing a spray gun three-dimensional optimization function and an improved quantum genetic algorithm, a targeted actuator is triggered for regulation and control, and a coating thermodynamic parameter library is constructed. And updating model parameters to optimize the overall situation.

This invention provides a distributed generation (DG) optimization configuration method based on a quantum genetic algorithm, aiming to minimize power loss and improve voltage stability in power distribution systems. The method first collects basic data of the power distribution network, including bus numbers, voltage levels, line impedances, bus load demands, and capacity limitations of distributed generation sources. Next, a population containing different DG configuration schemes is initialized using a random number generation algorithm, and the population is iteratively optimized using a quantum genetic algorithm. During the optimization process, quantum selection, crossover, and mutation operations are used to generate a new generation of configuration schemes, and the power loss and voltage level of each scheme are evaluated using a fitness function to gradually approach the optimal solution. After obtaining the optimal configuration scheme, different load conditions (such as load increases or decreases) are simulated to verify and optimize the adaptability of the configuration, ensuring that the system can maintain low power loss and voltage stability under load fluctuations. The final output optimal DG configuration scheme includes bus location, capacity, and optimized power loss and voltage curves, capable of adapting to dynamic load changes. This method can effectively reduce power loss and improve voltage stability in power distribution systems, providing technical support for building efficient and reliable power distribution systems.

The application discloses a flexible load intelligent optimization scheduling method for improving the economy of photovoltaic access distribution networks. A multi-type flexible load grading and scheduling optimization model is established. On the one hand, according to the demand side response demand, the flexible load is divided into three categories, namely interruptible load, translatable load and adjustable load, a flexible load operation optimization model is constructed, the reasonable allocation of various flexible loads in different periods is realized, the peak regulation pressure and resource waste problems in the distribution network are effectively alleviated, the purchase of electricity during the power consumption peak can be effectively avoided, and the economy of the distribution network is improved. On the other hand, for the discrete and continuous mixed data distribution environment, a Latin square quantum-inspired genetic algorithm (LSQGA) is used to process the nonlinear optimization problem in the environment, so that the optimal feasible region in the macro data space can be quickly searched, and the feasibility of the optimization model under the extreme scenario is improved.

The application discloses a three-level urban and rural common distribution network path planning method based on an adaptive hybrid algorithm, and particularly relates to a distribution path planning method which is optimized by combining a time window, vehicle capacity and path continuity as constraint conditions and an adaptive firework-quantum genetic hybrid algorithm. The application comprises the following steps: firstly, basic information such as coordinates, demand and time window of a city common distribution center, a county and township transfer station and a rural end self-pickup point is obtained, and vehicle resources are initialized; then, a mathematical model with the minimum total distribution cost as the target is constructed based on common distribution, and the model comprises fixed transportation cost, variable transportation cost and time penalty cost. The model is solved by using the adaptive firework-quantum genetic hybrid algorithm, which comprises quantum bit coding to generate an initial solution, an insertion algorithm to optimize the initial solution, a firework algorithm for global search and a quantum genetic algorithm for local optimization. In the optimization process, the diversity and convergence speed of the solution are improved by combining spark disturbance operation, quantum bit rotating gate dynamic adjustment and crossover mutation operator, and finally, the optimal distribution path meeting the time window constraint is output. The application is suitable for path planning of a complex three-level urban and rural distribution network, can significantly reduce transportation cost, improve distribution efficiency and optimize urban and rural logistics resource allocation.

The invention discloses a new energy battery anti-static foam box flaw detection method based on artificial intelligence, and relates to the technical field of foam box flaw detection. The method comprises the steps that foam box charge dissipation data is collected and preprocessed, a dimension reduction feature vector is obtained and input into a support vector machine model, an internal structure deviation degree score is output, and a three-dimensional feature map is generated through mapping of a three-dimensional visualization algorithm; collecting surface response data, calculating an optimal threshold through a multi-target threshold optimization method, and recognizing surface abnormal data in combination with the three-dimensional feature map; constructing a fusion feature vector based on the surface abnormal data, inputting a support vector machine model optimized by an improved quantum genetic algorithm, and outputting a defect classification result; and collecting foam box state data, combining a defect classification result, performing weighted summation by adopting an analytic hierarchy process to obtain a risk value, and dividing safety levels according to the risk value to complete the defect detection of the anti-static foam box.

The present invention provides a hydrogen fuel cell stack inspection method and system. The method comprises: dividing the hydrogen fuel cell stack into several groups, each group consisting of several single cells connected in series, each group corresponding to a differential amplifier, and the differential amplifiers of the several groups operating simultaneously for inspection; during inspection, each group uses an optical coupler switch to connect the positive and negative poles of each single cell in the group to the corresponding differential amplifier, respectively, and by controlling the on and off terminals of the optical coupler switch, the differential amplifier inspects the voltages of all single cells in the group; performing analog-to-digital conversion on the voltages of all inspected single cells; and performing SG filtering on the voltages of all the single cells after the analog-to-digital conversion to obtain the filtered voltages of all the single cells; wherein the window size of the SG filter and the fitting order of the polynomial are determined by an improved quantum genetic algorithm. The present invention can improve data accuracy and reduce costs.

The invention belongs to a thermal power plant allocation method, and provides a thermal power plant load dynamic allocation method and a related device in order to solve the technical problems that an existing thermal power plant load allocation model neglects unit response delay, an adjustment period is fixed and multi-source uncertainty is insufficient in coping. Static parameters, dynamic response parameters and real-time and uncertain data of a thermal power generating unit are obtained, modeling parameters are dynamically constructed, an objective function fusing economical efficiency, environmental protection performance, adjustment smoothness and multi-source uncertain cost is combined, an improved self-adaptive multi-objective quantum genetic algorithm is adopted to solve a model, and the model is optimized. And optimization and upgrading of load distribution of the thermal power plant can be realized from multiple dimensions. According to the method, by means of the adjustment smoothness design in the target function and in cooperation with subsequent extensible freezing period constraints, frequent instructions for load adjustment are effectively reduced, and the operation intensity of operators is greatly reduced. And multi-target collaborative optimization is realized by integrating economic, environmental protection and uncertainty coping requirements into a target function.

The application provides a ground source heat pump air conditioner intelligent energy-saving control method and system, belongs to the technical field of building energy saving and intelligent control, collects historical load data and environmental parameters of building operation, and constructs a time sequence feature set; an initial quantum population is generated based on Tent chaotic mapping, population observation and updating are carried out in combination with an improved quantum genetic algorithm, and differential variation and crossover operation of individual fitness are carried out by using an improved self-adaptive differential evolution algorithm, so that the hyperparameters of the IADE-IQGA-BiLSTM model are optimized, multiple rounds of iterative training are carried out by using a training set, and an early stopping strategy is implemented by using a verification set; the trained model is used for load prediction of a test set, the prediction result is inversely normalized into an actual load value, and a performance evaluation index is calculated; the IADE-IQGA-BiLSTM model is packaged as a RESTful API interface and deployed on an intelligent energy-saving control software to receive real-time data flow and regularly generate future load prediction; based on the load prediction result, the water pump frequency and the heat exchanger shape of the ground source heat pump system are dynamically adjusted.

The invention discloses a cooperative vehicle-mounted edge computing power network-based dependent task computing unloading method, which belongs to the field of vehicle-mounted edge computing and comprises the following steps of: constructing a dynamic computing power network pool fusing a fixed edge server and a mobile opportunity vehicle; repeated subtasks in a task directed acyclic graph are identified and merged to eliminate redundant calculation, and a two-stage collaborative optimization strategy is adopted: firstly, an improved quantum genetic algorithm introducing a load balancing mechanism is utilized to solve and calculate an unloading decision; and then task scheduling is modeled as a Markov decision process, topological features are extracted by using a graph convolutional neural network, and adaptive scheduling is performed based on a near-end strategy optimization algorithm. The method can effectively reduce the average execution delay of tasks and the total energy consumption of the system, and improve the overall utilization efficiency and scheduling stability of resources in a dynamic network environment.

The application relates to the technical field of resource scheduling and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, which comprises the following steps: acquiring core heterogeneous data, performing exponential smoothing denoising to obtain a smooth state value, combining an HAT extended tuple, and analyzing to obtain a comprehensive capability score; automatically adjusting contribution degree by similarity of static characteristics and dynamic characteristics; constructing a coupling correction function, calculating a hybrid precision matching degree, combining an exponential product to reflect nonlinear correlation between targets; constructing a dynamic penalty function, optimizing a quantum genetic algorithm fitness function, and performing multi-target collaborative optimization; constructing a complexity coupling and dynamic adaptation mechanism to dynamically adjust a task segmentation ratio; designing a frequency and reconfiguration time index ratio to optimize FPGA bit stream preloading; constructing a synchronous delay coupling model to perform state synchronization optimization; balancing multidimensional feedback through a combined reward function; and improving explainability by quantifying the influence of decision factors through contribution degree entropy.

The invention relates to the technical field of industrial design, in particular to a design optimization method for an airport oil depot vertical vault cone-bottom storage tank, which comprises the following steps: determining basic parameters of a to-be-designed storage tank, and constructing a weld joint calculation mathematical model of the to-be-designed storage tank based on the basic parameters; constructing an objective function, determining an optimization term and a penalty term, and performing optimization calculation on the weld joint calculation mathematical model based on a quantum genetic algorithm; and drawing an optimized welding drawing of the to-be-designed storage tank based on the optimized weld joint calculation mathematical model, and performing optimal design of the to-be-designed storage tank according to the optimized welding drawing. Optimized design of the vertical vault cone-bottom oil tank can be achieved based on the quantum genetic algorithm, the tedious mode that drawing is conducted firstly and then measurement and judgment are conducted in a traditional design method is avoided, the design efficiency is greatly improved, meanwhile, the problem of thermal stress concentration of welding seams of the storage tank can be solved to a certain degree, the service life of the storage tank is prolonged, and engineering investment is saved; and the optimal design of the storage tank is realized.

The invention relates to the technical field of resource scheduling, and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, and the method comprises the steps: obtaining core heterogeneous data, carrying out the exponential smoothing denoising, obtaining a smooth state value, combining an HAT extension tuple, and carrying out the analysis to obtain a comprehensive capability score; the contribution degree is automatically adjusted through the similarity between the static features and the dynamic features; constructing a coupling correction function, calculating a mixing precision matching degree, and reflecting nonlinear correlation between targets in combination with an exponential product; a dynamic penalty function is constructed, a quantum genetic algorithm fitness function is optimized, and multi-objective collaborative optimization is carried out; constructing a complexity coupling and dynamic adaptation mechanism, and dynamically adjusting a task segmentation proportion; optimizing FPGA bit stream preloading according to the index ratio of the design frequency to the reconfiguration time; constructing a synchronous delay coupling model, and performing state synchronization optimization; balancing multi-dimensional feedback through a combined reward function; the influence of decision factors is quantified through contribution degree entropy, and interpretability is improved.

The application discloses a kind of intelligent slicing and conveying system and control method of shower room glass product, it is related to glass processing intelligent manufacturing technical field, contains visual identification module, detects glass with multi-view camera and structured light, in combination with deep learning to identify defect;Intelligent slicing module borrows quantum genetic algorithm to plan path, and laser marking mark;Vacuum adsorption conveying module realizes stable high-speed transfer with bionic suction cup and magnetic levitation track;Intelligent cache module contains magnetic levitation support and temperature and humidity control;Control system integrates edge computing and digital twin, realizes the cooperation of each module.The application realizes the intelligentization of glass product production full process by multi-module innovation, accurately detects small defect, quantum algorithm reduces waste, bionic suction cup ensures stable conveying, magnetic levitation technology improves efficiency, intelligent control realizes module cooperation, greatly improves production quality and efficiency, reduces energy consumption.

The application discloses a network-assisted free duplex non-cell wireless access network user association and mode selection joint optimization method, which comprises the following steps: establishing a network-assisted free duplex architecture system; establishing a user QoS constraint, user antenna mode selection and EDU-UE association problem with the target of maximizing the total spectrum efficiency of the system; determining the association relationship between the user and the EDU through a matching game algorithm; and dynamically scheduling the uplink and downlink working modes of the UE antenna by using a quantum genetic algorithm; and the application has good scalability.

The invention discloses a microgrid energy storage scheduling implementation method based on an improved genetic algorithm model, relates to the technical field of new energy, and solves the problem of insufficient microgrid energy storage scheduling capability. According to the scheme, a micro-grid energy storage system model is established to determine a charging and discharging strategy of energy storage equipment, a corresponding objective function is established according to a set objective, constraint conditions of charging and discharging limitation, load capacity and power supply and demand balance of the energy storage equipment are considered, and implementation feasibility is ensured; and searching an optimal solution of the improved genetic algorithm model according to a multi-target genetic algorithm and a quantum genetic algorithm so as to formulate a charging and discharging strategy of the energy storage equipment. The energy scheduling efficiency can be improved, and the energy storage utilization is optimized.

The invention relates to a tourist route intelligent planning method based on multi-source data fusion, and the method comprises the steps: constructing multi-dimensional feature vectors of a user and a scenic spot through integrating explicit demands, implicit behaviors and real-time environment data of the user; analyzing the contradiction between the explicit demand and the implicit preference by adopting an adversarial neural network, and generating a dynamic weight coefficient to quantify the priority; based on a quantum genetic algorithm, the dynamic weight and a multi-objective optimization function are fused, and efficient solving of a large-scale scenic spot combination is achieved; traffic jam and people flow density data are processed in real time in combination with a flow-oriented computing technology, and a route is dynamically adjusted to respond to environmental changes. According to the method, the defects of difficulty in balancing demand contradictions, low calculation efficiency and insufficient real-time performance in traditional planning can be solved, so that the accuracy, optimization speed and dynamic adaptive capacity of tourist route recommendation are improved.

The application discloses a kind of near-shore wind farm site selection auxiliary decision and dynamic control method, equipment and medium, it is related to near-shore wind farm technical field, the method includes: initial population of quantum genetic algorithm is randomly generated, for each quantum chromosome in initial population, based on the electromagnetic interference thermodynamic diagram of candidate area, determine the electromagnetic interference intensity of each selected candidate point in quantum chromosome, calculate the radar interference index corresponding to quantum chromosome, further calculate the fitness value corresponding to quantum chromosome, based on the fitness value corresponding to each quantum chromosome in initial population, update initial population using quantum genetic algorithm, obtain updated population, constantly iterate until reaching iteration termination condition, based on the fitness value corresponding to each quantum chromosome in updated population, determine several candidate site selection schemes, the application can meet the demand of near-shore wind farm efficient, safe, sustainable construction and operation.

This invention belongs to the field of aircraft operation support systems and equipment technology, and discloses a method and system for aircraft ground infrared de-icing based on multi-parameter collaborative optimization. The invention constructs an aircraft ground infrared de-icing system based on multi-parameter collaborative optimization, collaboratively optimizes infrared de-icing parameters, and builds a multi-objective optimization model. An improved quantum genetic algorithm is used to solve the collaborative multi-objective optimization model and generate parameters, obtaining the optimal set of de-icing parameters. Based on real-world conditions, repeated experiments are conducted in the parameter validity verification module to clarify whether the de-icing effect meets the de-icing requirements. The obtained experimental data is processed and analyzed in the historical data module. This invention realizes the transformation of the aircraft ground infrared de-icing process from experience-based operation to intelligent, optimized decision-making, providing an advanced technical solution for efficient, energy-saving, and safe green de-icing operations.

The invention discloses a power flow optimization method and device based on deep learning and a quantum genetic algorithm, terminal equipment and a storage medium, and belongs to the technical field of power flow optimization of a power system. Constructing a power flow model and constraint conditions by taking minimization of fuel cost, power loss and voltage deviation of a load bus as targets, and generating an initial quantum population which meets the constraint conditions and is provided with a plurality of quantum bits; wherein each quantum bit represents the output active power variation of one group of generators; and then, according to the initial power flow data and the initial quantum population, power flow solving operation is repeatedly executed, the optimal output active power variation of each generator is obtained, and output adjustment is carried out on each generator according to the optimal output active power variation. By implementing the method, the problem that the power flow optimization result cannot meet the overall optimization requirement when a traditional optimization method is used in the prior art can be solved.