79 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 path planning and obstacle avoidance method for an unmanned ship formation, and relates to the technical field of formation path planning. Decoding the quantum chromosome into an actual path by using a quantum genetic algorithm, screening out an optimal path, and discretizing the optimal path to obtain a global navigation path; a follower navigation path is constructed according to formation characteristics and a reference formation, the initial formation is adjusted when the follower starts, a communication link with a navigator is established at the same time, and the follower carries out local obstacle avoidance by using an improved artificial potential field method to carry out local formation adjustment; when a dynamic obstacle is detected, the stress is recalculated by using an improved potential field method, and the local path of the re-planned road section is optimized; and judging whether a target point is reached or not, if so, ending, and if not, circularly executing until the target point is reached. According to the invention, the formation system keeps stable formation in a dynamic unknown environment, and the targets of safe obstacle avoidance and optimal path are achieved.

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 discloses a three-level urban and rural joint distribution network path planning method based on an adaptive hybrid algorithm, and particularly relates to a distribution path planning method which takes a time window, vehicle capacity and path coherence as constraint conditions and combines an adaptive firework-quantum genetic hybrid algorithm for optimization. The method comprises the following steps: firstly, acquiring basic information such as coordinates, demand quantity, time windows and the like of an urban common distribution center, county and rural transfer stations and rural end self-pickup points, and initializing vehicle resources; then, a mathematical model with the purpose of minimizing the total distribution cost is constructed based on common distribution, wherein the model comprises fixed transportation cost, variable transportation cost, time penalty cost and the like; and solving the model by adopting a self-adaptive fireworks-quantum genetic hybrid algorithm, which comprises the steps of generating an initial solution by quantum bit coding, optimizing the initial solution by an insertion algorithm, carrying out global search on a fireworks algorithm and carrying out local optimization on a quantum genetic algorithm. In the optimization process, spark disturbance operation, quantum bit revolving door dynamic adjustment and crossover mutation operator are combined to improve the diversity and convergence speed of solutions, and finally the optimal distribution path meeting the time window constraint is output. The method is suitable for path planning of a complex three-level urban and rural distribution network, the transportation cost can be remarkably reduced, the distribution efficiency is improved, and urban and rural logistics resource allocation is optimized.

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 present invention relates to the technical field of radar signal waveform design. Disclosed are a signal determination method based on a quantum genetic algorithm, a quantum computing device based on a quantum genetic algorithm, and a medium. The method comprises: constructing quantum bits on the basis of the number of phase coding radar signals transmitted by a radar system, the length of the phase coding radar signals transmitted by the radar system, and the number of chromosomes; determining gene sequences of first chromosomes on the basis of all the quantum bits; determining whether a first chromosome meeting a preset termination condition is present in all the first chromosomes; if yes, taking the first chromosome meeting the preset termination condition as a phase coding radar signal transmitted by the radar system; if not, updating all the first chromosomes on the basis of the mutation probability, a preset crossover strategy, a quantum catastrophe strategy, and a rotation angle to obtain second chromosomes; replacing all the first chromosomes in each population with all the second chromosomes, returning to the determination step, and ending the operation until the termination condition is met. The local optimal solution is avoided, and the search quality and the search efficiency are improved.

The invention discloses an electric power big data encryption method based on hyperchaos, and relates to the technical field of data encryption, and the method comprises the following steps: generating a hyperchaos sequence as a key stream, and carrying out bitwise operation on the key stream and a plaintext data stream to obtain a ciphertext data stream; plaintext data is processed by adopting a compressed sensing technology, data transmission and storage overhead is reduced, and parameters of the hyperchaotic encryption system are optimized by using a quantum genetic algorithm; privacy removal processing is carried out on a ciphertext data stream, a sequence formed by real numbers of the ciphertext data stream is converted into a pseudo-random sequence formed by integers through a mapping method, the pseudo-random sequence serves as an encryption key, and data safety and anti-cracking capacity are enhanced. According to the method, the compressed sensing technology and the quantum genetic algorithm are introduced to solve the problem that a traditional cryptosystem is prone to falling into a local extremum to cause ciphertext potential safety hazards, the hyperchaotic data encryption system is optimized, and the algorithm can remarkably improve the data protection capability of power data on the premise of guaranteeing the privacy safety of customers, and the security of the power data is improved. And the potential safety hazard of the ciphertext is effectively reduced.

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.

The invention relates to the technical field of short-term electric quantity regulation and control, in particular to an electric quantity regulation and control method and system based on a quantum algorithm and a neural network in an electric power market environment. According to the method, a plurality of quantum registers are constructed to form a population and are initialized, the quantum registers are individuals representing market decisions, and the market decisions are sets of declared quantity-price curves of all participants in the electricity market; the quantum code of one participant is selected from each quantum register to serve as a candidate solution, the fitness of the candidate solution is evaluated, and updating variation is conducted on half of the quantum registers with the smaller fitness of the candidate solution so as to iterate the population; when the number of population iterations reaches a set threshold value, selecting an individual which meets the constraint conditions of each participant and has the maximum optimization target as an optimal declaration strategy; obtaining a declaration quantity price decision of each participant through optimal declaration strategy inverse quantum coding; the optimization objective as the individual's market decision is to maximize the sum of producer and consumer residuals. According to the method, the defect of poor power resource configuration efficiency in the prior art is overcome, market decision optimization is performed in combination with the quantum genetic algorithm, declaration reference is provided for each participant, so that clearing power is improved, power configuration is optimized, and configuration efficiency is improved.

The invention provides a self-driven micro-robot for a liver tumor targeted therapy and real-time monitoring system, and relates to the technical field of biomedical engineering. The monitoring main system comprises a self-driven navigation and positioning module, an intelligent drug delivery module, a tumor monitoring and state evaluation module, a multi-modal data fusion module, a real-time self-learning decision module, an advanced molecular simulation and drug optimization module, a safety communication module, a power grid environment adaptation module and a fault detection and recovery module; the self-driven navigation and positioning module adopts a mode of combining a quantum genetic algorithm, a fuzzy constraint optimization algorithm and a self-adaptive simulated annealing algorithm, so that the navigation accuracy and positioning efficiency of the robot in a complex environment in the liver are remarkably improved, and the quantum genetic algorithm has the advantages that by means of the strong parallelism of quantum calculation, the positioning accuracy of the robot is improved; according to the method, numerous potential path combinations can be quickly explored, the path planning time is greatly shortened, and meanwhile, the path planning accuracy is improved.

The invention provides a heterogeneous cloud multi-target task scheduling method and system based on quantum collaborative optimization, and the method comprises the steps: generating a DAG graph based on a user request, and constructing a chromosome code of charging mechanism collaboration; an initial population is randomly generated, quantum fitness evaluation is performed, non-dominated sorting is accelerated by applying a Grover search algorithm, a quantum-assisted crowding distance is introduced, and a to-be-evolved individual is obtained. Performing crossover and mutation operation on the sample by using a quantum genetic algorithm, and stopping when a convergence condition is reached; and finally, converting a Pareto solution set obtained by the quantum genetic algorithm into pheromones, initializing by using an ant colony algorithm and carrying out routing updating to complete scheduling and distribution. According to the invention, global search is carried out through the quantum genetic algorithm, and local optimization is carried out through the ant colony algorithm, so that the overall efficiency of the task scheduling method is improved; meanwhile, by improving a genetic algorithm chromosome coding mode and redefining a virtual machine cost function, the problem of billing mechanism difference in heterogeneous clouds is solved.

The present invention discloses a method for path planning and obstacle avoidance of an unmanned boat formation, and relates to the technical field of formation path planning, including system initialization and basic settings; using a quantum genetic algorithm, decoding quantum chromosomes into actual paths, screening out the optimal path, and discretizing the optimal path to obtain a global navigation path; constructing a follower navigation path according to the formation characteristics and the reference formation, adjusting the initialization formation when the follower starts, and establishing a communication link with the navigator at the same time, and the follower using the improved artificial potential field method to avoid obstacles locally and adjust the local formation; detecting a dynamic obstacle, recalculating the force using the improved potential field method, and optimizing the local path of the replanned section; judging whether the target point has been reached, if it has been reached, then ending, if not, then executing in a loop until the target point has been reached. The present invention enables the formation system to maintain a stable formation in a dynamic unknown environment, and achieve the goals of safe obstacle avoidance and optimal path.

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 invention provides a distributed generation (DG) optimal configuration method based on a quantum genetic algorithm, and aims to realize power loss minimization and voltage stability improvement of a power distribution system. The method comprises the following steps: firstly, collecting basic data of a power distribution network, including bus numbers, voltage grades, line impedance, bus load requirements, capacity limitation of distributed power supplies and the like; thirdly, initializing a group of populations containing different DG configuration schemes by adopting a random number generation algorithm, and carrying out iterative optimization on the populations through a quantum genetic algorithm; in the optimization process, a new generation of configuration schemes is generated by using quantum selection, crossover and mutation operations, and the power loss and the voltage level of each scheme are evaluated through a fitness function so as to gradually approach the optimal solution. After an optimal configuration scheme is obtained, different load conditions (such as load increase or decrease) are simulated so as to verify and optimize the adaptability of the configuration and ensure that the system can still maintain low power loss and stable voltage under load fluctuation. The finally output optimal DG configuration scheme comprises the bus position, the capacity and the optimized power loss and voltage curve, and can adapt to the dynamic change of the load. The method can effectively reduce the power loss of the power distribution system, improves the voltage stability, and provides technical support for constructing an efficient and reliable power distribution system.

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 an ultra-low coupling shimming coil design method based on a quantum genetic algorithm. Five pairs of axial cylindrical coils which have the same radius and are symmetrical about a plane with z being equal to 0 are included. The current directions and the number of turns are different and are obtained through a simplified multi-pole moment expansion formula, the coil design problem is converted into a nonlinear optimization design problem with coil structure parameters as constraint conditions and the coil uniformity and the coupling rate as optimization targets, the problem is solved by adopting a quantum genetic algorithm, and the nonlinear optimization design problem is obtained. Therefore, coil structure parameters with excellent performance can be efficiently obtained with high quality, and the designed coil can achieve the excellent performance that compared with a traditional self-shielding coil, the uniformity is improved by one order of magnitude and the coupling rate is reduced by one order of magnitude in a large target area under the same-radius configuration. The method is suitable for application scenes such as precise magnetic field metering test and biological low-intensity magnetic field detection.

The invention discloses an electric energy quality responsibility subject positioning and model building method and system. The method comprises the steps of obtaining electric energy quality detection data, decomposing and reconstructing current and voltage in the electric energy quality detection data through discrete wavelet Mallat transformation, extracting components of high-frequency disturbance, and judging the disturbance direction based on positive and negative high-frequency disturbance energy; an improved quantum genetic algorithm is adopted, quantum crossover and quantum mutation operation is introduced, a disturbance weight factor is set according to high-frequency disturbance energy, and a disturbance source is accurately positioned; and establishing a corresponding mathematical model based on a responsibility subject positioning result, and evaluating and improving the electric energy quality through the mathematical model. According to the method, the local features of the signals can be analyzed in time and frequency, the source direction of power quality disturbance can be accurately judged, excellent individuals can be more effectively reserved, and the diversity of populations can be more effectively improved.

The invention relates to a power grid relay protection constant value optimization method based on a quantum genetic algorithm. The method comprises the following steps: constructing a setting optimization model by taking the total action time of all main protection devices of a power grid for coping and processing a near-zone fault or a far-zone fault as a target function; constructing a quantum genetic algorithm model to carry out iterative optimization on the setting optimization model, designing a quantum bit representation method according to quantum state characteristics, and realizing improvement of coverage and convergence performance through linear superposition of different state probabilities of each analytic solution; an operation process is established, dynamic iteration is carried out on a quantum bit chromosome population and a two-state solution based on a quantum gate, the problems that a traditional algorithm is complex in operation process and low in calculation efficiency are solved, and therefore the safety and reliability of a relay protection system are further promoted.

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 invention relates to the technical field of data filtering and data fusion, in particular to an adaptive Kalman filtering photovoltaic power generation electric energy efficiency monitoring method. The monitoring method comprises the following steps: preprocessing measurement data, identifying an abnormal value, and removing the abnormal value; carrying out abnormal value restoration on the rejected measurement data; a single-individual quantum genetic algorithm QEAS is introduced for processing; optimizing a Kalman filtering hyper-parameter; and carrying out filtering operation and state updating on the data by using a Kalman filtering algorithm after parameter optimization. According to the self-adaptive Kalman filtering photovoltaic power generation electric energy efficiency monitoring method provided by the invention, the Kalman filtering algorithm is improved by introducing the single-individual quantum genetic algorithm, so that the optimal Q and R values of Kalman filtering are obtained, and the data precision and accuracy are improved.

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.