35 results about "Quantum genetic algorithm" patented technology

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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of power equipment fault analysis, and particularly discloses a power equipment fault diagnosis and early warning method and system based on a quantum genetic algorithm, and the method comprises the steps: collecting the equipment state data, working condition parameters and environment data of power equipment, and carrying out the working condition self-adaptive preprocessing of the data, new energy exclusive fault features are extracted; and executing working condition dynamic quantum coding on the new energy exclusive fault features, wherein the working condition dynamic quantum coding comprises generating quantum bit representation according to working condition vectors. According to the method, by introducing a working condition self-adaptive preprocessing and similarity pre-optimization initialization mechanism, the pertinence of fault feature extraction and the algorithm convergence speed are greatly improved; by constructing a quantum rotation strategy based on convergence degree and diversity dual dynamic adjustment, the conflict problem that a traditional quantum genetic algorithm is prone to falling into local optimum under the complex new energy working condition is successfully solved, and the global optimization capacity and diagnosis precision of the algorithm are remarkably improved.

The invention discloses a near-shore wind power plant site selection auxiliary decision-making and dynamic control method and device and a medium, and relates to the technical field of near-shore wind power plants, and the method comprises the steps: randomly generating an initial population of a quantum genetic algorithm, and for each quantum chromosome in the initial population, obtaining a candidate region based on an electromagnetic interference thermodynamic diagram of the candidate region; determining the electromagnetic interference intensity of each selected candidate point location in the quantum chromosomes, calculating the radar interference indexes corresponding to the quantum chromosomes, further calculating the fitness values corresponding to the quantum chromosomes, and based on the fitness value corresponding to each quantum chromosome in the initial population, calculating the electromagnetic interference intensity of each selected candidate point location in the quantum chromosomes; and updating the initial population by using a quantum genetic algorithm to obtain an updated population, carrying out continuous iteration until an iteration termination condition is reached, and determining a plurality of candidate site selection schemes based on the fitness value corresponding to each quantum chromosome in the updated population. The application can meet the requirements of efficient, safe and sustainable construction and operation of the offshore wind power plant.

The invention provides a ground source heat pump air conditioner load prediction method based on hybrid optimization and deep learning, and the method comprises the steps: carrying out the dynamic parameter adjustment and intelligent optimization of key hyper-parameters of a bidirectional long-short-term memory neural network model through employing a hybrid optimization algorithm combining an improved quantum genetic algorithm and an improved adaptive differential evolution algorithm; an improved BiLSTM network integrated with residual connection and a multi-head attention mechanism is constructed, and air conditioner load delay response characteristics caused by thermal inertia are accurately captured; using the optimized hyper-parameters to train the constructed improved BiLSTM network; external parameters to be predicted and historical parameters of the system are input into the trained model, a ground source heat pump air conditioner load prediction result is obtained, and an efficient solution is provided for intelligent regulation and control.

The invention discloses an automobile charging station construction method based on a quantum genetic algorithm and without an alternative site. The method comprises the following steps: S1, constructing an electric automobile charging station site selection and sizing model; s2, acquiring related data of a to-be-planned area; s3, dividing the to-be-planned area into m charging planning service sub-areas; s4, locating and sizing the electric vehicle charging station to be built in the charging planning service area; and S5, determining an optimal charging station locating and sizing scheme in the to-be-planned area. According to the method, the quantum calculation theory and the genetic algorithm are combined, the problems that in the genetic algorithm, due to improper selection, crossover and variation modes, the number of iterations is large, the convergence speed is low, and the system is prone to falling into a local extreme value are solved, and finally low-cost and high-benefit planning of the electric vehicle is achieved. Service range overlapping among the stations is reduced as much as possible, and resource waste is avoided.

An automatic design and parameter optimization system of SRAM circuit in in-memory computing architecture, first data collection and input are carried out, then simulation and model training are carried out, the artificial neural network after training convergence is used as a proxy model, and is combined with a quantum genetic algorithm optimization engine to obtain a QGA-ANN joint optimization system, the optimal design parameters of the candidate are obtained through global extreme value optimization, and after verification and evaluation, the globally optimal design parameters are output. The application is based on the high stability in-memory computing SRAM circuit and optimization system of quantum genetic algorithm and neural network, the BP proxy model with extremely low time delay is used to perfectly replace the traditional SPICE physical simulation, and the quantum genetic algorithm with the elite reservation mechanism is used to completely solve the bottleneck of local extreme value and slow convergence. The time-consuming and long analog integrated circuit "trial and error" design is converted into efficient and accurate "one-key" automatic optimization design, and the efficiency and stability of the in-memory computing macro cell reliability design are effectively improved.

This invention discloses a bearing fault diagnosis method based on dual-state feedback stochastic resonance and sparse resonance decomposition. First, sparse resonance decomposition (RSSD) is used to preprocess the bearing signal under strong noise to extract low-resonance components. Then, a dual-state unsaturated piecewise tristable stochastic resonance (DF-APTSR) model is constructed. By introducing a dual-state feedback mechanism, the system's potential well structure is flexibly adjusted to solve the output saturation problem of traditional stochastic resonance systems. Simultaneously, a novel composite index, SFSI, is defined and used in conjunction with a quantum genetic algorithm to achieve adaptive matching of system parameters. Simulation results show that this invention can effectively separate background noise and significantly enhance early, weak fault characteristics.

The invention discloses a production scheduling optimization method based on dynamic bottleneck identification and balanced distribution, relates to the field of industrial production scheduling and intelligent manufacturing, and aims to solve the problems of overstocked products, insufficient delivery ability, uneven equipment load and the like caused by bottleneck dynamic transfer in multi-variety and small-batch production. The invention provides a production scheduling and resource allocation method based on dynamic bottleneck equalization. The method comprises the steps of firstly constructing an equipment capability group operation time model, then determining work order effective production scheduling weeks and dynamically identifying bottleneck processes of each week, distributing operations based on an expected load rate, if invalid weeks with negative operation distribution quantity exist, removing the invalid weeks and recalculating the invalid weeks, and then updating the model in a rolling manner. And the work order scheduling sequence can be optimized through a quantum genetic algorithm. The method can dynamically respond to bottleneck change, balance equipment load, reduce work-in-process overstock and improve order delivery punctuality rate and equipment utilization rate, and is suitable for flexible production requirements of discrete manufacturing enterprises.

The invention discloses a power system fault rapid diagnosis and recovery electrical control system, and relates to the technical field of power system automation control, the system comprises the following components: a data acquisition module, a fault diagnosis module, an equipment residual life prediction module, a recovery decision module and a control execution module; the method carries out the prediction of the residual life of the equipment through a deep learning and physical model fusion method, extracts the operation data characteristics before and after the fault of the equipment through an improved gating circulation unit-space-time attention fusion network, and predicts the residual life of the fault equipment in real time through combining with a mathematical model based on the physical characteristics and working principles of the equipment. Besides, the recovery decision module adopts a recovery decision optimization method based on a multi-objective quantum genetic algorithm, takes a power supply recovery rate, a network loss reduction rate and a voltage stability index as optimization objectives, and generates an optimal recovery decision considering multiple objectives.

The invention discloses an improved quantum genetic optimization method, and relates to the field of nonlinear optimization of a quantum genetic algorithm. For a certain problem, in different stages of the quantum genetic algorithm, a rapid optimization strategy based on a staged coding length is adopted for a to-be-optimized parameter to obtain an optimal parameter; according to the problem characteristics, in the iteration process of the quantum genetic algorithm, a Gaussian weight field setting method based on key points is used for carrying out weight diffusion processing on non-optimized environmental parameters, the reasonability of weight distribution is guaranteed, and a final fitness function is obtained. And when the quantum genetic algorithm stops iteration but is a non-optimal solution and under the condition of convergence of a fitness function, implementing a to-be-optimized parameter adjustment strategy, and performing different quadratic optimization quantum bit coding adjustment according to different to-be-optimized parameter adjustment schemes. And finally, iteration of the quantum genetic algorithm is carried out again to obtain an optimal solution. The convergence efficiency of the fitness function is improved, and the adaptability and the calculation efficiency of the quantum genetic algorithm are improved.

The application relates to the technical field of structural optimization design, and specifically discloses a box beam structure lightweight design method based on topology optimization, which comprises the following steps: a three-dimensional parameterized model of multi-physical field coupling is constructed; cross-scale analysis of structural stiffness, thermal deformation, vibration mode and fatigue life is integrated; a variable density method and a level set method mixed topology optimization strategy is adopted; the relationship between density and stress and strain is modeled; and an initial material distribution scheme meeting the structural functional integrity is generated; an intelligent optimization engine of quantum genetic algorithm and deep reinforcement learning fusion is started; through the three-dimensional parameterized model of multi-physical field coupling, the variable density method and the level set method mixed topology optimization strategy are combined, the relationship between the structural lightweight target and the additive manufacturing process is effectively balanced, multi-physical field analysis such as thermal response, vibration and fatigue is introduced, and the box beam can meet the mechanical performance and other functional requirements in actual application.

The invention discloses an intelligent slicing and conveying system for shower room glass products and a control method, and relates to the technical field of glass processing and intelligent manufacturing, the intelligent slicing and conveying system comprises a visual identification module, glass is detected through a multi-view camera and structured light, and defects are identified in combination with deep learning; the intelligent fragmentation module plans a path by means of a quantum genetic algorithm and performs laser marking; the vacuum adsorption conveying module achieves stable high-speed transfer through a bionic suction cup and a magnetic suspension track. The intelligent cache module comprises magnetic suspension support and temperature and humidity control; and the control system integrates edge calculation and digital twinning to realize collaboration of each module. Through multi-module innovation, full-process intelligentization of glass product production is achieved, small defects are accurately detected, excess material waste is reduced through a quantum algorithm, stable conveying is ensured through a bionic suction cup, the efficiency is improved through a magnetic suspension technology, module cooperation is achieved through intelligent control, the production quality and efficiency are greatly improved, and energy consumption is reduced.

The invention discloses a self-adaptive vector network scanning method, a vector network analyzer, a medium and a product, and relates to the field of electrical variable measurement. The method comprises the following steps: generating a sparse frequency point sequence through chaotic mapping to preliminarily detect an S parameter; calculating the time domain reflection peak, frequency domain curvature, information entropy, wavelet transform and other characteristics of the data; based on the characteristics, intelligently dividing a frequency band into a high-sensitivity area and a flat area, and mapping a time domain key area into a frequency domain ripple area; distributing optimized frequency step length and points for each region by using a quantum genetic algorithm to form a scanning strategy; performing segmented scanning according to the strategy, and performing real-time verification based on a local fitting residual error; if the residual exceeds the threshold, dynamically inserting a supplementary frequency point and executing supplementary scanning; and integrating all data to generate a complete and accurate S parameter curve. According to the invention, the contradiction between the vector network scanning speed and precision can be relieved in the test of new energy automobile parts.

The invention belongs to the technical field of wireless communication networks, and particularly relates to a multi-service wireless communication endogenous security method and system based on quantization isolation potential energy and a medium, and the method comprises the steps: building a quantization isolation demand model of a communication service, and enabling the model to convert the security demand of the service into an isolation potential energy value Ereq based on an information theory security criterion; the method comprises the following steps: constructing a space-time-frequency three-dimensional isolation map of wireless channel resources, and calculating inherent isolation potential energy Eres of each resource unit; based on the isolation potential energy value Ereq and the inherent isolation potential energy Eres, constructing a multi-objective optimization matching function of services and resources; solving the matching function by adopting an improved quantum genetic algorithm to obtain an optimal service-resource three-dimensional allocation scheme; when data transmission is executed, quantum randomness is introduced to perform physical layer security enhancement, transmission security is ensured on an information theory level, and normal form transformation of security isolation from static passive to dynamic active can be realized.

The invention provides a hybrid flow shop scheduling problem optimization method and system considering personalized procedures, and the method comprises the steps: firstly obtaining workpiece processing data and a machine set according to initial data, then defining constraint conditions of a hybrid flow shop scheduling problem considering personalized procedures, and appointing multiple optimization targets according to key factors of a production process, and encoding and decoding the processing scheme, optimizing the processing scheme by using a multi-target quantum genetic algorithm based on a cooperative game, and finally outputting an optimal scheduling scheme. The method can effectively solve the scheduling problem of the hybrid flow shop considering the personalized process, provides a scheduling scheme, and provides a method for improving the production efficiency of an enterprise.

The invention provides a compression part temperature rise efficiency measurement precision test control method based on quantum heredity. The method comprises the following steps: S1, determining core parameters, constraint conditions and data recording standards of compression part temperature rise efficiency measurement; s2, a compression part temperature rise efficiency measurement test platform is built, and calibration is completed; s3, collecting initial measurement data of the compression part under different working conditions; s4, constructing and training a quantum genetic algorithm-back propagation neural network-particle swarm optimization measurement precision fusion model; s5, performing measurement control parameter optimization based on the fusion algorithm model; s6, a temperature rise efficiency measurement test is executed based on the optimized control parameters, and the precision is verified; and S7, establishing a measurement precision feedback control mechanism and solidifying a test process. According to the invention, through algorithm deep cooperation and closed-loop control, dual improvement of measurement precision and efficiency is realized, and reliable test control technical support is provided for performance evaluation of the compression part.