10results about How to "Solve the small size" patented technology

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

Aiming at the problem of missile terminal guidance with no-fly zone and impact angle constraints, a model predictive static programming guidance law based on control barrier function is proposed. The proposed algorithm inherits the high efficiency of the traditional model predictive static programming while ensuring the no-fly zone constraints. The main reasons are as follows: first, the projection operator under the linear inequality constraints has an explicit expression, avoiding additional optimization solving; second, due to the forward invariance of the control barrier function, the no-fly zone constraints do not need to be imposed at all discrete nodes, but only at one or a few discrete nodes, reducing the scale of solving. The simulation results show that the proposed algorithm can meet the no-fly zone and impact angle constraints while ensuring high computational efficiency. Subsequent research will focus on trajectory optimization and guidance problems with multiple no-fly zone constraints, even dynamic no-fly zone constraints.

The invention relates to a satellite multi-beam rapid forming method based on a genetic algorithm, and the method comprises the steps: inputting a constraint condition, and carrying out the array amplitude tapering, so as to meet the maximum sidelobe level demand of each beam; calculating the size of an array required for forming each wave beam so as to meet the requirement of the wave beam width; and optimizing the position of an array needing to be activated for forming each beam and the corresponding array element amplitude and phase coefficient based on a genetic algorithm. According to the invention, shaping of a plurality of circular wave beams with different sizes and proportional distribution of wave beam power resources are realized, so that the total radiation power of the array is maximized, and meanwhile, the calculation complexity is reduced.

The application discloses a provincial power grid day-ahead plan hierarchical progressive optimization method containing multiple pumped storage power stations, which comprises the following steps: obtaining basic data of a power system; establishing a power system day-ahead plan optimization model containing multiple pumped storage power stations; calculating day-ahead plan output values of wind, light and conventional units; further calculating start-stop plan of each pumped storage unit according to the plan output values; and finally obtaining day-ahead output plan values of each unit by solving the pumped storage start-stop plan. The application can overcome the difficulty in convergence when the scale of integer problems and linear programming problems is large even if mature optimization solvers such as CPLEX and GLPK are used, make large-scale integer linear mixed programming problems converge better, meet the online application requirements of day-ahead plan making, and have important practical significance for fully utilizing new energy and efficiently operating pumped storage power stations.

The invention relates to the technical field of power systems, and particularly discloses a time sequence operation simulation method considering the comprehensive benefit of a new energy delivery base, and the method comprises the following steps: S1, constructing a new energy delivery base time sequence operation simulation model; s2, calculating comprehensive benefits of the new energy delivery base; and S3, solving the new energy delivery base time sequence operation simulation model to obtain an optimal new energy delivery base operation scheduling scheme. The fast solving method for the time sequence operation simulation model has the advantages that a non-dominated dung beetle optimization algorithm and a decomposition solving method are utilized, the solving scale and the solving time are reduced, and the purpose of fast solving is achieved.

The invention discloses a wind power electric power system optimization operation method based on cloud edge collaboration and a related device, and belongs to the technical field of electric power system operation scheduling. The method comprises the following steps of: establishing an upper cloud regulation and control model by taking the minimum total operation cost in a day-ahead scheduling operation period as an objective function and taking power balance constraint, transmission capacity constraint, thermal power generating unit output constraint and energy storage constraint as constraint conditions; establishing a lower-layer side node model by taking the lowest wind curtailment penalty cost as an objective function and taking the output constraint of the wind turbine generator as a constraint condition; and solving the upper-layer cloud regulation and control model and the lower-layer edge node model to obtain a cloud-edge collaborative double-layer distributed optimization operation scheme. According to the scheduling method provided by the invention, the uncertainty of wind power generation can be effectively handled, the coping cost of the system is reduced, and the economical efficiency is improved; according to the invention, collaborative optimization of wind power output of edge nodes and charging and discharging of energy storage equipment can be realized, economical efficiency of operation of an interconnection system is realized, and privacy of data transmission can be ensured.

The invention provides a moment method solving method for an electromagnetic field of an IBC object in a layered medium based on SDIE. The method comprises the following steps that modeling is conducted on a scatterer to be simulated and a background layered medium of the scatterer in COMSOL; mesh generation is carried out on the surface of the scatterer, meshes cannot be distributed in a cross-layer mode, and for areas in different layers, the corresponding meshes adopt different sampling rates to guarantee the calculation accuracy of results; compared with a finite element method for solving such problems, the method has the advantages that mesh generation does not need to be performed on a background medium, and the degree of freedom and memory used for solving an integral equation are far smaller than those used by the finite element method; the method shows excellent calculation efficiency and calculation precision in actual calculation examples.

The invention discloses an automatic home layout method and device and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the house type structure information of a target space and the commodity information of candidate home commodities, and then determining an effective layout region of each candidate home commodity, screening out a feasible home commodity set from the candidate home commodities based on the effective layout area; based on a preset sorting strategy, sorting the commodities in the feasible household commodity set to obtain a sorting result; determining a to-be-arranged target commodity combination from the feasible household commodity set according to a sorting result; and performing layout optimization on the to-be-arranged target commodity combination by taking the layout region set as a search space, and outputting a layout scheme meeting a preset cost threshold value. According to the method and the device, the problems of low generation efficiency and slow response speed of the automatic home layout scheme in the prior art are solved, and the technical effect of remarkably improving the scheme generation speed on the premise of ensuring the layout rationality is realized.

The invention relates to the technical field of air conditioner loads, and particularly discloses an air conditioner load parameter credible aggregation method based on cloud-edge encryption, which is based on a cloud-edge collaborative architecture, uses a public cloud server as a cloud platform, and uses edge computing equipment as a user side intelligent terminal. A data transmission mode based on a file transfer protocol is adopted to carry out cloud side communication, the cloud-side collaborative architecture comprises three cloud servers, the cloud server 3 is responsible for distributing encryption keys to users and distributing decryption keys to the cloud server 1, the cloud server 2 is responsible for clustering user parameters, and the cloud server 3 is responsible for distributing encryption keys to the users and distributing decryption keys to the cloud server 2. And the cloud server 1 is responsible for decrypting the clustered parameters and solving a demand response load reduction optimization problem to determine an optimal strategy. In this way, it is ensured that each cloud server can only master local information of all users, the security of data circulation is enhanced, and the user data privacy is protected and the solving scale of the load reduction optimization problem in the demand response is reduced by encrypting and clustering the user parameters at the cloud.

The present application discloses a point cloud data processing method, device, equipment and storage medium, the method comprising: obtaining a set of two-dimensional images to be reconstructed, determining multiple groups of feature matching pairs in the two-dimensional image set based on the two-dimensional feature points in each two-dimensional image; wherein each group of feature matching pairs corresponds to a three-dimensional space point, and the feature matching pairs include multiple matching two-dimensional feature points; meshing the two-dimensional image according to a preset number of feature points, determining the two-dimensional feature points in each grid, and determining the three-dimensional space points corresponding to the grid according to the three-dimensional space points corresponding to the two-dimensional feature points; selecting the target three-dimensional space points of the grid from the three-dimensional space points corresponding to the grid as point cloud data for constructing a three-dimensional scene according to the number of observations of the three-dimensional space points and the preset internal point conditions, and the number of observations is equal to the number of two-dimensional feature points corresponding to the three-dimensional space points. Through the above technical means, the problems of low construction efficiency and poor construction quality of the existing three-dimensional reconstruction methods are solved.