55 results about "Quantum particle swarm optimization" patented technology

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention belongs to the technical field of control, and discloses a water surface robot high-precision trajectory tracking control method and related equipment, and the method comprises the steps: constructing a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm embedded into a water flow kinetic equation constraint is adopted to carry out global path planning, smooth parameterization processing is carried out on a global path obtained through planning, and a reference trajectory is generated; according to the vector information of the water flow field, flow field disturbance is predicted based on a fluid-structure interaction dynamics model, system residual disturbance is estimated by using an extended state observer, and feedforward control quantity is generated in combination; according to the real-time flow velocity of the water flow field and the trajectory tracking error, a nonlinear model prediction controller and an adaptive sliding mode controller are adaptively switched, and a control instruction is generated in combination with a feedforward control quantity to drive the water surface robot to track a reference trajectory; therefore, high-precision trajectory tracking control is realized.

The invention relates to a micro-grid photovoltaic energy storage capacity optimization method based on double-layer multi-target collaborative decision, and the method comprises the following steps: 1, analyzing the structure and operation mode of a new energy micro-grid in a rural region, and constructing a capacity optimization configuration model of an optical storage system; step 2, optimizing an objective function by an inner layer and an outer layer; the inner-layer optimization target is to minimize the daily volatility of the complementary power generation system and minimize the daily peak-valley difference; 3, solving an optical storage capacity optimal configuration model: obtaining an optimal Pareto solution set of the target function by adopting a quantum particle swarm algorithm; 4, adopting an interactive multi-criterion decision based on compromise solution selection; 5, outputting a capacity configuration scheme with the optimal comprehensive efficiency; the method has the advantages that intermittency and peak regulation capacity are comprehensively considered, stable power supply is ensured, power supply reliability is improved, energy utilization efficiency is improved, system construction and operation cost is reduced, and economic benefits are improved.

The invention relates to the technical field of unmanned aerial vehicle auxiliary edge computing task offloading, and particularly provides an edge computing task offloading strategy based on an improved quantum particle swarm optimization (IQP) SO (Inter Quantum Particle Swarm Optimization) algorithm, and relates to an edge computing task offloading method based on the IQP SO algorithm and an edge computing task offloading system based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm. According to the method, an MEC unloading structure of multi-user mobile equipment (UE) supported by an unmanned aerial vehicle is established; under the structure, communication, time delay and energy consumption models are formulated to evaluate time delay and energy consumption required by the unloading task of the mobile equipment; according to the improved quantum particle swarm optimization, the unloading efficiency is improved, and the time delay and energy consumption problems of tasks are considered in the optimization process; the algorithm combines quantum characteristics, has excellent global search capability and rapid convergence characteristics, and effectively avoids the problem of global optimal solution omission caused by premature convergence when optimizing an edge unloading strategy; according to the method, the average time delay and the energy consumption of the mobile edge computing task can be remarkably reduced, and the optimization of the system is realized.

The invention relates to a multi-modal time sequence fusion Transform power load prediction method based on quantum particle swarm optimization, and belongs to the field of power load prediction. According to the method, a density clustering algorithm and a time sequence weighted interpolation method are utilized to preprocess power load and associated influence factor data, a random forest feature screening model is constructed, model parameters are optimized through quantum particle swarm optimization, the model is trained, and a power load key influence feature set is determined according to a feature importance threshold. The power load prediction method comprises the following steps: performing modal classification and standardization on key features, then establishing a multi-modal time sequence feature matrix adaptive to Transform, constructing a multi-modal time sequence fusion Transform model containing an attention modal fusion layer, and predicting the power load in the future seven days by using the optimized model. According to the method, efficient fusion of the multi-modal data and intelligent optimization of the model parameters are realized, the prediction precision is remarkably improved, and the problems of low pre-processing precision of the multi-modal data, redundancy of feature screening, insufficient optimization of the model parameters and large long-time prediction errors of a traditional prediction method are solved.

The invention provides a heterogeneous unmanned aerial vehicle forest fire rescue cooperative scheduling method based on reinforcement learning and quantum particle swarm optimization, and relates to the field of cooperative scheduling, and the method comprises the steps: constructing a cellular automaton fire spread model based on wind speed, vegetation and gradient, and generating a combustion cell priority sequence; establishing a three-state conversion model of the multi-type unmanned aerial vehicle, and combining the combustion loss and the flight time to construct a comprehensive cost; task allocation and track solving are realized through a quantum particle swarm optimization algorithm enhanced by reinforcement learning; and a scheduling scheme is corrected in real time by adopting a rolling updating mechanism, and fault-tolerant re-planning is triggered under an abnormal condition, so that the cooperative fire extinguishing efficiency of the heterogeneous unmanned aerial vehicle group is improved.

The invention discloses a GIS fault intelligent identification and visual positioning method. According to the technical scheme, the method comprises the steps that S1, a multi-source data collection platform is built, S2, an adaptive wavelet packet decomposition algorithm is adopted, and an initial point cloud is obtained; s3, constructing a GIS equipment three-dimensional point cloud topology model based on the initial point cloud; s4, setting a correlation degree threshold, screening effective candidate areas, and preliminarily determining a defect position range; and S5, identifying a fault type by adopting an adaptive quantum particle swarm optimization-graph attention network model, and outputting a fault identification result and a three-dimensional visual positioning report. The method is mainly used for fault feature extraction, defect type identification, accurate positioning and visual operation and maintenance analysis of gas insulated switchgear of various voltage classes.

The invention relates to an organization member information acquisition management system, which comprises a self-adaptive multi-mode acquisition unit, a holographic correlation modeling unit, a predictive dynamic updating unit and a heterogeneous encryption storage unit, the self-adaptive multi-mode acquisition unit is used for starting voice acquisition after verifying identity through voiceprint recognition, switching to an image interaction mode when silence occurs, synchronously docking the wearable equipment to acquire physiological parameters, and pausing acquisition when abnormity occurs; the holographic correlation modeling unit constructs a three-dimensional holographic information model, and uses a quantum particle swarm optimization algorithm to screen strong correlation information combinations; the predictive dynamic updating unit establishes an information attenuation prediction model, pushes an updating prompt in advance and generates a pre-filling template; the heterogeneous encryption storage unit adopts a space-time fragmentation encryption method to store information. The invention aims to solve the problems of single information acquisition mode, low multi-source data association degree, information updating lagging and insufficient data storage security of a traditional organization member information acquisition management system.

The present application relates to a kind of organization member information collection management systems, including adaptive multi-modal acquisition unit, holographic correlation modeling unit, forecast formula dynamic updating unit and heterogeneous encryption storage unit;Adaptive multi-modal acquisition unit starts voice collection after identity is verified by voiceprint recognition, switches to image interaction mode in silence, synchronously interfaces wearable equipment and collects physiological parameters and pauses collection when abnormal;Holographic correlation modeling unit constructs three-dimensional holographic information model, and strong correlation information combination is filtered using quantum particle swarm optimization algorithm;Forecast formula dynamic updating unit establishes information attenuation prediction model, and generates pre-populated template by pushing update reminder in advance;Heterogeneous encryption storage unit stores information using space-time slicing encryption method, the purpose of the present application solves the problems that traditional organization member information collection management system is single in information collection mode, low in multi-source data correlation degree, information update lags behind and data storage security is insufficient.

The invention discloses a power electronic load-containing load equivalence and parameter identification method, which comprises the following steps of: preprocessing original power load data, and extracting data required by subsequent parameter identification; determining a load model structure, and performing aggregation equivalence on the power distribution network, the static load, the dynamic load and the power electronic load according to a principle that system response before and after aggregation remains unchanged; the method comprises the following steps: firstly, constructing a load model, then determining parameters, needing to be identified, of each type of load model, distinguishing dominant parameters and non-dominant parameters by adopting trajectory sensitivity, setting values of the non-dominant parameters as typical values, then identifying the dominant parameters through an improved quantum particle swarm algorithm, determining the values of the dominant parameters, and finally, performing accuracy evaluation on the established load model. According to the invention, a more accurate load model can be established for a modern power system containing a power electronic load, the simulation precision of the power system is improved, and the analysis and control of a power department on the power system are facilitated.

The application discloses a fluid antenna assisted ISAC system joint beam forming and position optimization method based on a quantum particle swarm optimization algorithm. Specifically, an ISAC multi-user system model equipped with a two-dimensional fluid antenna is established, and a beam forming optimization problem is constructed; a weighted least mean square error algorithm is used to convert a communication and rate target function, so that a complex fractional problem is converted into an integral problem, and solving is simplified; an alternating optimization algorithm is used to decompose the original joint optimization problem into multiple sub-problems; an auxiliary variable and a receiving beam former are alternately solved, an iterative solution of a beam forming vector is solved by using a successive convex approximation algorithm, and a fluid antenna position problem is solved by using a quantum particle swarm optimization algorithm. The method of the application effectively improves the communication and rate under the restrictions of the fluid antenna position, the minimum perceived beam pattern gain and the maximum base station transmission power, and is superior to a traditional ISAC system equipped with a fixed antenna.

The invention discloses a multi-source collaborative traction power supply system adaptive protection method and system, multi-source data of each end of a traction power supply system is synchronously collected, and the multi-source data comprises SVG data of a power supply side, PMU data of a traction network and TCMS data of a locomotive end; performing data cleaning and space-time alignment on the multi-source data to obtain multi-source heterogeneous data; performing nonlinear feature decoupling extraction on the multi-source heterogeneous data to obtain mixed features; constructing a cooperative control model for cooperative work of the LSTM-RF hybrid model and the quantum particle swarm optimization model, and inputting the hybrid features into the cooperative control model to obtain predicted fixed values corresponding to various faults; and cooperatively controlling compensation of the SVG at the power supply side, action of the circuit breaker of the traction network and locking of the locomotive according to the predicted fixed value.

The invention provides a low-voltage active transformer area abnormal data monitoring method and device and electronic equipment, and relates to the technical field of data monitoring. The method comprises the following steps: acquiring monitoring data of a to-be-monitored low-voltage active transformer area; feature extraction is carried out on the monitoring data; wherein the extracted features comprise a physical topology feature, a load distribution feature and an electric energy quality feature; inputting the physical topology characteristics, the load distribution characteristics and the electric energy quality characteristics into a trained theoretical line loss prediction model to obtain a theoretical line loss prediction value; according to the theoretical line loss predicted value, the actually measured line loss value and the dynamic threshold value, judging whether the monitoring data is abnormal or not; wherein the theoretical line loss prediction model comprises model parameters, the model parameters comprise kernel function parameters and coupling strength coefficients, and the theoretical line loss prediction model is obtained through training based on a quantum particle swarm optimization algorithm. According to the invention, the accuracy of theoretical line loss prediction can be improved, so that abnormal data monitoring is accurate.

This invention belongs to the field of data transmission technology and discloses a privacy-preserving network security data transmission method, including the following steps: S1. Data privacy preprocessing: Constructing a personalized differential privacy protection mechanism to perform local privatization processing on the original data to balance data privacy protection and availability; S2. Intelligent scheduling of transmission tasks: For scenarios with multiple network nodes and multiple transmission links, establishing a scheduling model with transmission latency and energy consumption as optimization objectives, and using a quantum particle swarm optimization algorithm that integrates chaotic perturbations to solve the model; S3. Trusted verification of transmitted data: Dividing the network into several sub-regions and constructing a tree-like key management structure, and using key priority allocation and cross-region node verification mechanisms. This solution, through a three-step collaborative mechanism, constructs an end-to-end network security protection system covering the entire data lifecycle, achieving the organic unity of personalized privacy protection, collaborative optimization of transmission efficiency and energy consumption, and decentralized trusted verification.

The application belongs to the technical field of control, and discloses a high-precision trajectory tracking control method for a water surface robot and related equipment, the method comprising: constructing a dynamic multi-modal environment map containing three-dimensional geometric topological information and water flow field vector information of the water surface; performing global path planning by using an improved quantum particle swarm optimization algorithm embedded with a water flow dynamics equation constraint according to the dynamic multi-modal environment map, and performing smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory; predicting flow field disturbance based on a fluid-structure coupling dynamics model according to the water flow field vector information, estimating system residual disturbance by using an extended state observer, and combining a generated feedforward control amount; adaptively switching a nonlinear model predictive controller and an adaptive sliding mode controller according to real-time water flow field flow velocity and trajectory tracking error, combining the feedforward control amount, and generating a control instruction to drive the water surface robot to track the reference trajectory; and thus high-precision trajectory tracking control is achieved.

The invention discloses a power load prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical power load data and feature data related to the power load data, and preprocessing the historical power load data and the feature data; performing feature extraction and feature decoupling processing on the preprocessed historical power load data based on a variational mode decomposition technology to obtain target features; introducing a self-attention mechanism to perform multi-modal cross attention dynamic fusion on the target features and the preprocessed feature data, and constructing a power load prediction model; performing global hyper-parameter optimization on the power load prediction model by adopting a quantum particle swarm optimization algorithm to obtain a target hyper-parameter; training the power load prediction model by adopting the target hyper-parameter to obtain a target power load prediction model, and performing power load prediction by adopting the target power load prediction model; the causal reasoning capability of the model on complex business logic is enhanced, and the prediction accuracy is improved.

The invention discloses a wind turbine generator health state assessment method and system. The method comprises the following steps: S1, collecting detection data of related parts of a wind turbine generator; s2, dynamically representing the operation mode of the key component based on a deformable convolution and attention mechanism combined driven feature extraction network; s3, generating health state score distribution by adopting an improved generative adversarial network, and identifying potential anomalies through a discriminator; s4, performing local health assessment on different parts of the wind turbine generator based on a multi-agent reinforcement learning collaborative decision-making mechanism, and completing overall health judgment by using centralized training, a distributed execution mechanism and an attention module; and S5, introducing quantum particle swarm optimization to carry out joint optimization on the agent cooperation strategy and the GAN parameters. The method solves the problems that feature extraction is insufficient, samples are unbalanced and fault propagation is difficult to model in existing wind turbine generator health state assessment.

The invention discloses a bevel gear power closed transmission system variable speed loading optimization method, and belongs to the field of gear transmission system loading test method optimization. The problem that according to an existing variable speed loading method, constant load loading or simple program control step loading exists, and consequently loading time is long is solved. According to the method, the dynamic characteristics of the bevel gear closed power system are analyzed, and a novel dynamic characteristic value is calculated by collecting a vibration signal and a vibration acceleration signal of the system. The optimal test loading sequence is determined by ingeniously applying a quantum particle swarm optimization method, and the method is mainly applied to the bevel gear power closed loading test.

The invention discloses an unmanned aerial vehicle task allocation method based on an anti-cosine attenuation quantum particle swarm, and relates to the technical field of intelligent optimization algorithms, and the method comprises the steps: constructing an unmanned aerial vehicle task allocation model; performing iterative solution on the unmanned aerial vehicle task allocation model by adopting a preset quantum particle swarm algorithm to obtain an optimal unmanned aerial vehicle task allocation scheme; in the iterative solution process, an anti-cosine attenuation mechanism is introduced, and real-time dynamic optimization configuration is carried out on the time-varying contraction and expansion coefficient; according to the preset quantum particle swarm algorithm, the quantum space is introduced into the particle swarm algorithm, and iteration of the quantum space particle swarm position is represented. According to the invention, the optimal unmanned aerial vehicle task allocation scheme can be quickly and efficiently obtained.

This invention relates to the field of control, and in particular to a method and system for controlling the operation of a reactor used in resin production. The method involves acquiring real-time operating parameters of the reactor, iteratively training a fuzzy neural network model using a hierarchical collaborative hybrid optimization strategy. The antecedent membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the consequent is a nonlinear polynomial function of the input variables. The training aims to minimize the comprehensive objective function. The optimization strategy involves using an improved quantum particle swarm optimization algorithm for global search to determine the Gaussian mixture model parameters, and using a recursive least squares algorithm for local search after each iteration to determine the consequent coefficients. During training, importance indices are calculated based on the average activation degree of the fuzzy rules and their contribution to the prediction error. Rules with importance consistently below a preset threshold are removed. After training, control commands for controlling the heating system power and material feed rate of the reactor at the next moment are generated and executed based on the real-time parameters.

This application provides a fault recovery method, fault recovery device, and electronic device for power systems. The method includes: acquiring fault characteristics in the current power system and inputting these characteristics into a fault model to obtain the current fault type. The fault model is trained using multiple sets of historical data, each set including historical fault characteristics and corresponding historical fault types. The fault characteristics are abnormal changes in the power system. Based on the current fault type, a corresponding fault recovery model is determined. The fault recovery model includes an objective function and constraints. An adaptive quantum particle swarm optimization algorithm is used to solve the fault recovery model to obtain the optimal fault location. Based on the fault type and the optimal fault location, a fault recovery scheme is determined and executed. This method solves the problem of low accuracy in existing fault detection methods.

The invention discloses a temperature drift compensation and precision calibration method for an ultrasonic scanning mechanical arm, and relates to the technical field of temperature drift compensation. Through multi-dimensional temperature field monitoring, a fiber bragg grating and a digital sensor are used for collecting data to construct a three-dimensional model. A temperature-deformation mapping model of the time-varying thermal resistance network is established, and finite element analysis is combined; a double-time-scale dynamic error compensation algorithm is adopted, and a compensation instruction is transmitted through an EtherCAT bus; and adaptive Kalman filtering calibration of quantum particle swarm optimization is introduced, so that the convergence speed and precision are improved. The positioning precision of the mechanical arm is improved, the temperature sudden change response is shortened, the thermal deformation of the carbon fiber component is reduced, the self-learning calibration time is shortened, and a high-precision scheme is provided for nondestructive testing.

This invention discloses a bearing fault diagnosis method based on GA-VMD and adaptive stochastic resonance, belonging to the field of early fault diagnosis of rotating machinery. The implementation method is as follows: First, the original rolling bearing vibration signal is acquired. Second, using envelope entropy as the comprehensive objective function, a genetic algorithm is used to search for the minimum value of the comprehensive objective function, determining the optimal combination of the penalty parameter α and the number of modes k in the variational mode decomposition algorithm. Third, the original bearing signal is initially denoised using the optimized variational mode decomposition algorithm. Fourth, using the signal-to-noise ratio of the bearing signal as the comprehensive objective function, a quantum particle swarm optimization algorithm is used to search for the maximum value of the comprehensive objective function, determining the optimal combination of the nonlinear system potential function parameters a and b and the damping coefficient μ, obtaining an optimal parameter-adaptive stochastic resonance system. Fifth, this stochastic resonance system is used to perform stochastic resonance on the bearing vibration signal to improve the signal-to-noise ratio of the bearing signal. Sixth, spectral analysis is performed on the stochastic resonance output signal to achieve accurate extraction of weak fault features and accurate fault identification.

The invention discloses a software quality measurement method based on FERMATEAN fuzzy PROMETHEE and quantum particle swarm optimization. The method comprises the following steps: acquiring an original characteristic value of a software class diagram and constructing a composite fuzzy matter element; the fuzzy number is converted into a Fermatean fuzzy number meeting cubic constraint so as to expand an uncertainty representation space; calculating a score function and an optimal membership degree to form a standardized matrix; constructing a fitness function fusing the distinction degree and the balance penalty, and adopting a quantum particle swarm optimization algorithm to adaptively learn an optimal index weight; on the basis of the optimal weight and the optimal membership degree, a net flow value of each object is calculated by using a PROMETHEE method; and calculating a comprehensive evaluation index according to the net flow value and dividing quality grades. According to the method, the limitation of a traditional method in the aspects of fuzzy information description, static empowerment and linear compensation is overcome, and the accuracy, the adaptability and the decision reasonability of software quality measurement are improved.

The invention discloses a grouting test piece twin maintenance method and system, electronic equipment and a storage medium, and relates to the field of constructional engineering maintenance. According to the method, a three-dimensional temperature and humidity strain field distribution map is generated according to historical detection data of a prefabricated wall, real-time meteorological data and a historical database are obtained, and a dynamic environment map is constructed; inputting the three-dimensional temperature and humidity strain field distribution map into a preset finite element analysis model to generate a virtual wall state map; predicting first detection data at the current moment according to the virtual wall body state map, starting an adversarial generative network when a difference value between the first detection data and the second detection data is greater than a preset proportion, and generating a restoration environment curve according to the dynamic environment map and the second detection data; according to the restoration environment curve, a quantum particle swarm optimization algorithm is adopted to determine multi-target energy consumption optimal control parameters, and the grouting test piece is maintained through the multi-target energy consumption optimal control parameters. By implementing the technical scheme provided by the invention, the grouting test piece is efficiently maintained in an energy-saving manner.

The application discloses a trajectory planning and tracking method suitable for a small underwater robot, and belongs to the technical field of automatic control, which comprises the following steps: generating a three-dimensional underwater environment global path of the small underwater robot by using an improved adaptive ant colony algorithm; considering dynamic obstacles, fusing a dynamic window method to locally optimize the generated global path to obtain an optimized path; considering an ocean current field environment, using a quantum particle swarm optimization algorithm to optimize the generated global path to obtain an optimized path; using an improved particle swarm optimization algorithm to improve an MPC model, and tracking the trajectory of the small underwater robot by using the improved MPC model. The application adopts the above method, solves the problems of low trajectory planning efficiency, poor path smoothness, high energy consumption and insufficient trajectory tracking precision in the prior art under a dynamic environment, and significantly improves the task execution capability and adaptability of the underwater robot in a complex environment.

The invention discloses a microwave noninvasive blood glucose prediction method, device, equipment, medium and product, and relates to the field of microwave sensing, the method comprises the following steps: constructing a data set according to a blood glucose time sequence collected by a microwave noninvasive blood glucose sensing system; based on a convolution block attention module and an improved quantum particle swarm optimization algorithm, constructing an optimal bidirectional gating loop network model according to the data set; the optimal two-way gating circulation network model is a two-way gating circulation network model fusing a convolution block attention module and an improved quantum particle swarm optimization algorithm; and predicting the blood sugar of the to-be-tested person according to the optimal bidirectional gating circulation network model. According to the application, accurate prediction and effective management of the blood sugar level of the human body are realized.

This invention provides an energy prediction system based on quantum particle swarm optimization (QPSO) federated spatiotemporal coupling, comprising an edge sensing layer, a federated computing layer, a quantum optimization layer, and a spatiotemporal prediction layer. The edge sensing layer collects operational data from distributed energy nodes and performs data cleaning and feature encoding. The federated computing layer jointly models the spatial relationships between energy nodes and the temporal evolution characteristics of operational data, generating model update information. The quantum optimization layer triggers a federated aggregation process when preset conditions are met and updates the model aggregation weights based on the QPSO mechanism, generating global model parameters. The spatiotemporal prediction layer predicts and analyzes the operating status of the energy system based on the global model parameters and outputs risk assessment information. This invention achieves multi-node collaborative modeling without centralizing raw energy data, which is beneficial for improving the adaptability and application feasibility of the energy prediction system in complex operating scenarios.