11 results about "Quantum evolutionary algorithm" patented technology

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

The invention provides a micro-grid group-containing AI active distribution network scheduling optimization method, a medium and a system, and belongs to the technical field of power grid scheduling. The method comprises the following steps: firstly, constructing a micro-grid group and main and distribution network interaction model, and determining boundary constraints; predicting key parameters of the micro-grid group by using deep reinforcement learning; constructing an active distribution network power balance equation and topology constraint conditions; solving by adopting mixed integer programming to obtain power flow distribution of the distribution network; constructing a power grid dispatching optimization objective function based on a quantum evolutionary algorithm; processing multi-source data by using an MGPN deep neural network to output an optimal scheduling strategy; monitoring a running state verification effect in real time through a state estimation technology; updating the strategy in real time by applying a rolling optimization mechanism; and establishing an evaluation system to dynamically optimize neural network model parameters, realizing efficient collaborative scheduling of the micro-grid group and the active power distribution network, and solving the technical problem of low distributed energy consumption rate in the collaborative scheduling optimization process of the micro-grid group and the active power distribution network.

The invention discloses a high-frequency transformer transmission extreme value frequency rapid calibration and tolerance analysis method, which comprises the following steps of collecting operation parameters of a high-frequency transformer in real time, performing standardized preprocessing, and integrating the preprocessed parameters to construct operation condition vectors in a unified format; defining a frequency response range of the high-frequency transformer, constructing a frequency search space based on the frequency response range, and establishing a target function for fitness evaluation based on the frequency search space; searching extreme value frequency points in the frequency search space by adopting a quantum evolutionary algorithm; based on the working condition vector and the extreme value frequency point, a working condition frequency deviation prediction model is constructed, and the working condition frequency deviation prediction model outputs an extreme value frequency shift offset; and calculating the dynamic tolerance bandwidth according to the predicted offset and the fluctuation range of the working condition vector. According to the invention, rapid and accurate calibration of the extreme value frequency of the high-frequency transformer and dynamic determination of the tolerance range can be realized.

The invention discloses a distributed energy virtual power plant optimization method based on ectopic quantum evolution, and relates to the technical field of virtual power plant control. At present, research on economic optimization of a virtual power plant is insufficient. The method comprises the following steps: constructing virtual power plant power generation main body cost according to distributed energy characteristics; according to the charging and discharging characteristics of the energy storage system, constructing the operation cost of the energy storage block of the virtual power plant; based on the cost of pollution to the environment and the cost of abandoning new energy, the environment cost is constructed in a combined manner; according to the real-time dynamic change of the electricity price to the electricity demand of the user, the demand response cost is constructed; building a cost objective function according to the cost factors of various virtual power plants, and constructing an optimization model of the low-cost virtual power plant by taking the cost objective function as an optimization objective; optimizing the low-cost virtual power plant optimization model by using an ectopic quantum evolutionary algorithm; and obtaining a virtual power plant system according to an optimization result. The diversity of the distributed energy and the system stability are both considered, and efficient utilization and economical efficiency improvement of the distributed energy are promoted.

The application relates to a user grouping method, device, equipment and storage medium. The method comprises the following steps: for a first group, performing an outer loop iteration step; the outer loop iteration step comprises the following steps: transforming the positions of target users in the first group according to a target displacement scheme to obtain a second group; updating the first group and an optimal group according to the target function value corresponding to the second group; encoding the first group and the optimal group into a first binary number and a second binary number respectively; updating a target quantum solution according to the first binary number and the second binary number based on a quantum evolution algorithm; performing quantum measurement on the target quantum solution to obtain a third binary number, and decoding the third binary number to replace the first group; and when the outer loop iteration step satisfies an outer loop stop condition, obtaining the optimal group. The user grouping method has low complexity, is suitable for a multi-user scene, and can improve the performance of a NOMA heterogeneous communication network system.

The invention discloses a park micro-grid regulation and control method based on an improved C-type equivalent circuit, and belongs to the park micro-grid regulation and control technology. The method comprises the following steps: constructing an energy storage optimization framework of electricity-hydrogen-heat multi-energy flow coupling; establishing a multi-energy flow coupling model based on the improved C-type equivalent circuit; an improved quantum evolutionary algorithm is adopted to solve an optimal solution, an objective function optimal solution meeting constraint conditions is searched, and an optimal equivalent circuit parameter and a scheduling strategy are output; and each unit sends an operation control instruction to coordinate the operation state of each device so as to realize collaborative optimization regulation and control of electricity-hydrogen-heat multi-energy flow in the park microgrid. According to the method, the C-type equivalent circuit model is improved, three energy flows of electricity, hydrogen and heat are innovatively incorporated into a unified modeling framework, a hydrogen energy storage equivalent resistor and a heat recovery equivalent capacitor are newly added, complete mathematical description of the multi-energy flow coupling characteristic is achieved, and the multi-energy cooperative regulation efficiency and the energy comprehensive utilization degree of the park microgrid are effectively improved.

This invention discloses a 5G base station deployment optimization method based on an improved quantum evolution algorithm, comprising: acquiring geographical data and defining initial conditions; constructing a multi-level candidate site pool; establishing an objective function that balances coverage performance and construction economy; performing population encoding based on qubit probability amplitude to achieve an equal-probability superposition state; obtaining discrete base station deployment schemes by observing the superposition state during quantum evolution, and ensuring that the schemes meet preset construction indicators through heuristic repair logic; guiding the population to evolve towards the optimal region by updating the probability amplitude of qubits based on an adaptive quantum rotation gate update mechanism; outputting the global optimal solution and performing spatial mapping to obtain the optimized deployment scheme. This invention, through the combination of differentiated modeling of heterogeneous networks and quantum search logic, can effectively reduce signal overlap redundancy between base stations and achieve optimal network coverage performance configuration with limited construction resources.

The invention relates to a k3s cluster-oriented container resource scheduling optimization method, which comprises the following steps of: reading user application information, k3s cluster information, resource information of cluster nodes and container configuration information in a processing process, and inputting the read information into a discrete quantum evolutionary algorithm to obtain a scheduling scheme of container deployment. The generated container deployment scheduling scheme at least comprises cluster node selection for deploying application containers and computing and storage resources deployed for each application container, the discrete quantum evolutionary algorithm at least comprises an initial feasible solution generation strategy, a greedy thought-based task allocation strategy and a discrete individual update rule optimization strategy. According to the method, a reasonable scheduling decision can be made for deployment and resource allocation of the application container according to the requirements of the user application in a resource-limited k3s cluster environment, so that the average completion time of the user application is effectively shortened, and the service quality of the k3s cluster is guaranteed.

The invention provides a quantum LSTM-based photovoltaic array composite fault diagnosis method and system. The method comprises the steps of obtaining to-be-diagnosed data in an operation process of a photovoltaic array; inputting the to-be-diagnosed data into a pre-constructed quantum LSTM network model for fault diagnosis to obtain a fault diagnosis result of the photovoltaic array; the quantum LSTM network model is obtained by optimizing hyper-parameters in the LSTM network model on the basis of typical feature data in the operation process of a photovoltaic array in combination with a quantum evolutionary algorithm, a quantum entanglement effect can capture potential association in complex faults, the LSTM network can efficiently recognize and diagnose under incomplete data, and the fault diagnosis efficiency is improved. Therefore, the diagnosis efficiency and the diagnosis precision of the model can be improved by using the quantum algorithm to optimize the hyper-parameter of the LSTM; and the optimized LSTM network model is trained based on the typical feature data, so that the model can identify the fault in the operation data, and the purpose of fault diagnosis is achieved.

The invention discloses a plateau lake and reservoir fish proliferation quota method and system based on ecological bearing analysis, and the method comprises the steps: obtaining lake and reservoir environment perception information, carrying out the preprocessing of the lake and reservoir environment perception information, and constructing a lake and reservoir environment parameter distribution diagram; performing multi-modal feature extraction based on the lake and reservoir environment parameter distribution map to generate multi-modal fusion features, constructing an ecological bearing capacity analysis model, and performing ecological bearing capacity analysis on the target plateau lake and reservoir to obtain ecological bearing capacity analysis information; a quantum evolutionary algorithm is introduced, and lake and reservoir fish proliferation quota analysis is carried out in combination with the ecological bearing capacity analysis information and the multi-modal fusion features, so that a proliferation quota scheme is generated; and performing spatial value evaluation on the target plateau lake and reservoir to generate a value thermodynamic diagram, performing proliferation quota release path optimization by using an ant colony optimization algorithm, and obtaining a proliferation release path network for lake and reservoir fish proliferation quota assistance. Therefore, the effectiveness and ecological stability of plateau lake and reservoir fish proliferation are improved.

This invention discloses a method and system for allocating fish stock enhancement quotas in plateau lakes and reservoirs based on ecological carrying capacity analysis. The method includes: acquiring lake / reservoir environmental perception information; preprocessing the information and constructing a distribution map of lake / reservoir environmental parameters; extracting multimodal features from the environmental parameter distribution map to generate multimodal fusion features, constructing an ecological carrying capacity analysis model, and performing ecological carrying capacity analysis on the target plateau lake / reservoir to obtain ecological carrying capacity analysis information; introducing a quantum evolutionary algorithm to combine the ecological carrying capacity analysis information and the multimodal fusion features to perform fish stock enhancement quota analysis and generate a stock enhancement quota scheme; conducting spatial value assessment of the target plateau lake / reservoir to generate a value heatmap; and using an ant colony optimization algorithm to optimize the stock enhancement quota allocation path to obtain a stock enhancement allocation path network to assist in fish stock enhancement quota allocation in the lake / reservoir. This improves the effectiveness and ecological stability of fish stock enhancement in plateau lakes and reservoirs.