69 results about "Bat algorithm" patented technology

The invention discloses a thermal power generating unit deep peak regulation control system, and relates to the technical field of thermal power generating unit deep peak regulation, the thermal power generating unit deep peak regulation control system comprises a deep peak regulation control center, and the deep peak regulation control center is in communication connection with a data acquisition module, a load prediction module, a working condition monitoring module, a distributed control module and a safety monitoring early warning module. By introducing a distributed control architecture and bat algorithm optimization, each subsystem of the thermal power generating unit is divided into a plurality of local control units, independent and cooperative control is realized, the flexibility and the response speed of the thermal power generating unit in the deep peak regulation process are remarkably improved, and the reliability of the thermal power generating unit is improved. Distributed control ensures that each subsystem can quickly adjust operation parameters according to actual load requirements, and the global optimization capability of the bat algorithm further improves the self-adaptability and decision-making efficiency of the system in a complex working condition, so that the problem of response lag in a traditional centralized control mode is effectively solved.

The invention discloses an engineering project full-cycle cost intelligent accounting system based on a BIM technology, and relates to the technical field of constructional engineering informatization, and the system comprises a BIM visualization platform which is in communication connection with the following modules: a dynamic optimization scheduling module which is used for fusing a target engineering project BIM model and a real-time digital twinborn body, and introducing a bat algorithm to carry out dynamic optimization on construction resource nodes influenced by risks. According to the method, a dynamic resource topology network is constructed by fusing a BIM model and real-time digital twinborn data, accurate tracking and state perception of construction resources are realized, on the basis, a bat algorithm and a graph neural network are introduced to perform dynamic optimization and conflict coordination on resource configuration in a multi-risk concurrent scene, and the resource allocation efficiency is improved. And a rescheduling scheme and an optimal cost increment which conform to actual engineering logic are generated, the limitation of traditional static risk conduction simulation is broken through, and the adaptability of resource scheduling and the refinement level of cost control are remarkably improved.

The invention discloses a wireless sensor network (WSN) target coverage method and device based on a chaos adaptive bat algorithm, and belongs to the technical field of wireless sensor network coverage. Aiming at the problems of limited communication range, poor adaptive capability and the like of the existing WSN, the method comprises the following steps of: initializing parameters, constructing a monitoring relation matrix to determine a fitness value, designing an adaptive inertia weight strategy, updating various parameters of a bat individual, and carrying out operations such as local search, adaptive adjustment of loudness and pulse emissivity and the like. And judging a termination condition to obtain an optimal solution in combination with an elitist strategy and chaos updating. The device comprises a network model construction module and the like and is used for executing the method. According to the method, the adaptive bat algorithm is adopted to establish the target coverage model, the elite strategy and chaos updating are combined, the algorithm is prevented from falling into local optimum, the stability is improved, the sensor node coverage area is effectively increased, the convergence speed is higher, the optimization capacity is stable, and the method is suitable for a WSN target coverage scene.

The invention discloses a cathode protection test pile potential adaptive regulation and control system, device and method. The method comprises the following steps: S1, generating a standardized underground structure potential data set; s2, constructing a three-dimensional sparse convolutional neural network model based on the standardized underground structure potential data set and completing training; s3, performing real-time prediction on underground structure potential distribution under different cathode protection control parameter combinations by using the trained three-dimensional sparse convolutional neural network model; s4, initializing a bat algorithm population, and searching a globally optimal cathode protection control parameter combination; s5, inputting the globally optimal cathode protection control parameter combination into the three-dimensional sparse convolutional neural network model; and S6, applying the combination to the operation of an actual cathode protection test pile system. According to the method, priority learning of key nodes around the test pile is realized, and the problem of large potential prediction deviation of a traditional network in a deep buried region is effectively avoided.

A wind-light-storage collaborative planning method based on an improved bat algorithm comprises the steps that a double-layer optimization model is constructed, the upper layer is a planning model, and site selection and capacity configuration of a wind power system, a photovoltaic system and an energy storage system are optimized with the full-life-cycle net income maximization as the target; the lower layer is an operation model, and on the basis of receiving the upper layer planning result, the operation scheduling between the renewable energy source and the energy storage system is coordinated by taking wind abandoning, light abandoning and network loss minimization as targets so as to guarantee the economical efficiency and the stability of the system operation; secondly, using an improved bat algorithm to carry out iterative solution on the bilayer programming model; according to the method, the consumption capability of the power distribution network on the distributed renewable energy sources can be effectively improved, and the operation reliability and safety of the power grid under the high-proportion renewable energy source access condition are further improved.

The invention discloses an aerocar energy-saving resource adjusting method and system based on a large model, and the method comprises the steps: adding a depth separable convolution layer at the front end of a Transform encoder to extract local airflow features, carrying out the modeling of time sequence dependence through a GRU network, and replacing a part of the GRU network with a GLU convolution layer; optimizing hyper-parameters of the hybrid architecture model through an improved NRBO bat algorithm to obtain a target hybrid architecture model; inputting the data into the target hybrid architecture model to output pneumatic control parameters, optimizing the pneumatic control parameters by taking energy consumption minimization as a reward function based on a PPO reinforcement learning algorithm, and generating an energy-saving control instruction; and controlling a wing variable trailing edge flap angle, distributed propulsion motor power distribution and an energy recovery system of the hovercar according to the energy-saving control instruction. The energy consumption of the hovercar under different flight working conditions is reduced, and the resource adjusting efficiency of the hovercar is improved.

The application discloses a kind of based on worker skill number multi-objective parallel waste product disassembly line optimization method, and relates to a kind of waste product disassembly line optimization method, the product information and disassembly line information of the product to be disassembled and the disassembly skill information of worker are constructed disassembly line balance model;With the minimum worker skill number and the net profit of product disassembly maximization as target, construct objective function;Set the constraint condition of disassembly line balance optimization model;Coding mechanism based on priority relation matrix is coded to disassembly task, generates feasible solution, decoding;Get the task allocation scheme of the product to be disassembled;In actual case, IDBA is compared with NSGA-II, NSGA-III, SPEA-II, ESPEA, MOEA / D and five kinds of algorithms.The application proposes an improved discrete bat algorithm according to the actual case of disassembly line, optimizes multi-objective parallel disassembly line balance problem, and significantly improves task and skill allocation efficiency.

The application discloses a double-reliability RVFL bat optimization wind power interval prediction method and system, specifically for: preprocessing wind farm historical power data, constructing a data set; establishing a basic RVFL, a cascaded enhanced RVFL and a direct connection enhanced RVFL candidate model, expressing the upper and lower bounds of the prediction interval as a linear combination of enhanced features and output weights; in the first stage, the hidden layer parameters are fixed, the output weights are solved by using an opportunity constraint optimization model containing a training set and a validation set coverage constraint, and the optimal basic model is selected according to the validation set SCORE index; in the second stage, the model parameters are used as elite prior solutions, a mixed initialization bat population is constructed by combining random exploration individuals, and the global network parameters are optimized; and the prediction interval is output on the test set and evaluated. The application improves the generalization coverage ability by double-reliability constraint, enhances the scene adaptability, and optimizes the network parameters by using a mixed initialization bat algorithm.

This invention relates to the field of heating system technology, and more specifically, to a remote control method for a heating system, comprising: taking any area covered by the heating system as the target area, acquiring the outdoor temperature and hot water flow rate of the target area at the current moment in real time; using the Bat Algorithm to obtain all solutions for the hot water flow rate of the target area at the current moment obtained in each iteration, and determining the fitness value of each solution for the hot water flow rate of the target area at the current moment obtained in each iteration; determining the demand degree for the next iteration after the Bat Algorithm has completed a preset number of iterations; determining whether to proceed with the next iteration based on the demand degree, and obtaining the target value of the hot water flow rate at the current moment; and realizing remote control of the heating system. This invention improves the computational efficiency and accuracy of the global optimal solution output by adaptively adjusting the number of iterations of the Bat Algorithm through demand degree calculation.

The present application relates to the field of geological exploration, especially to a drilling speed modeling method based on improved bat algorithm and support vector regression. The method first identifies and corrects abnormal data according to the interval anomaly detection method. Then, the support vector regression method is used to build a drilling speed model. Finally, an improved bat algorithm is designed to determine the optimal parameter value of the drilling speed model. The drilling speed modeling method described in the present application can effectively eliminate abnormal drilling data and ensure drilling characteristics, while building a high-precision drilling speed model to accurately predict drilling speed and provide a reliable basis for drilling operation and adjustment.

The invention relates to the technical field of heating systems, in particular to a remote control method for a heating system, and the method comprises the steps: taking any region covered by the heating system as a target region, and obtaining the outdoor temperature and hot water flow value of the target region at the current moment in real time; using a bat algorithm to obtain all solutions of the hot water flow value of the target area at the current moment obtained by each iteration, and determining the fitness value of each solution of the hot water flow value of the target area at the current moment obtained by each iteration; determining the demand degree for the next iteration after the current iteration is completed after the bat algorithm completes the preset number of iterations; determining whether the next iteration is carried out according to the demand degree to obtain a target value of the hot water flow value at the current moment; and remote control of the heating system is realized. According to the method, the iteration times of the bat algorithm are adaptively adjusted by calculating the demand degree, so that the calculation efficiency of the algorithm and the output accuracy of the globally optimal solution are improved.

The invention discloses an optical fiber coupling efficiency improving method based on a bat algorithm. The method comprises the following steps that optical power data are collected and preprocessed; constructing a coupling parameter search space model; initializing an improved submerged space enhancement-bat algorithm, and outputting an initial coupling parameter vector; introducing countercurrent coding to generate a submerged space vector; calculating and outputting an optimal action vector through a differentiable strategy flow mapping mechanism; obtaining a current iteration optimal bat individual; outputting a coupling parameter vector of the global optimal bat individual; and the improved submerged space enhancement-bat algorithm is recalled to perform parameter optimization calculation, so that the optimization speed and the global optimal stability of optical fiber coupling are effectively improved, the coupling loss is remarkably reduced, and the automatic adjustment precision and the optical power stability of the system are improved.

The present invention belongs to the field of robotic arm control technology, and more specifically, is a method for optimal trajectory planning for a robotic arm based on a cascade filter. The method comprises the following steps: Step 1: Using a quintic Bezier curve to locally smooth corners of small line segments; Step 2: Establishing an optimal model for curvature extrema within a constraint range, using the bat algorithm to solve for the optimal control point and complete curve optimization; Step 3: Using a cascaded second-order filter and a sliding window filter to perform dynamic speed planning. The present invention optimizes curvature and employs dynamic speed planning to improve the stability of the robotic arm and the maximum allowable corner feed rate, thereby resolving the issues with traditional speed planning algorithms, which are difficult to modify online and suffer from large speed fluctuations.

The invention discloses an agricultural machinery operation plan adjustment method considering dynamic weather change, belongs to the field of agricultural machinery scheduling, and aims to solve the problems of frequent failure, cost increase and agricultural time delay in actual execution of an operation plan due to the fact that the dynamic weather change is not fully considered in an existing agricultural machinery scheduling method. The method comprises the steps that firstly, an initial scheduling model with the minimum total cost as the target is constructed based on predicted weather, the model is fused with a farmland operation time window and path continuity constraint, an improved bat algorithm is adopted for solving, and an initial optimal scheme containing the agricultural machinery operation path, sequence and time is output; in the operation process, the system monitors weather in real time, and when an original time window loses efficacy or a path is not feasible due to changes, dynamic adjustment is automatically triggered. And then, based on the real-time position of the agricultural machine and the updated weather information, rapidly reconstructing and solving a dynamic optimization model, and outputting an optimal adjustment scheme of the remaining tasks.

The application provides a terminal counter cabinet foot compression strength optimization method and device and electronic equipment, and relates to the technical field of network communication. The method comprises the following steps: constructing a compression strength model of a terminal counter cabinet foot; and solving the compression strength model of the terminal counter cabinet foot by using a new bat algorithm to obtain optimal parameters of the terminal counter cabinet foot. The bat algorithm is improved in the application, the compression strength of the terminal counter cabinet foot is optimized, and the compression strength of the terminal counter cabinet foot is ensured to be strong.

The application discloses a behavior decision model optimization, a behavior decision method, a device, equipment and a medium. The optimization method of the vehicle behavior decision model comprises the following steps: obtaining a vehicle behavior decision model to be optimized and a standard verification sample set matched with the vehicle behavior decision model, wherein the standard verification sample set comprises a plurality of vehicle environment description characteristic values and a standard behavior decision result; performing data preprocessing on the standard verification sample set to obtain a normalized sample set; setting parameters based on a bat algorithm, and performing multi-round iteration processing on a bat colony by using the bat algorithm; when a bat iteration end condition is met, a global optimal bat is obtained; and the global optimal bat is used to update the vehicle behavior decision model to obtain an optimized vehicle behavior decision model. By using the above technical solution, the vehicle behavior decision model can be efficiently optimized, and the accuracy of the vehicle behavior decision model can be improved.

The invention provides a new energy power generation parameter prediction model training method and a parameter prediction method, and the training method comprises the following steps: obtaining wind turbine generator operation data collected by a data collection and monitoring system, and carrying out the preprocessing and feature extraction of the wind turbine generator operation data; on the basis of the LSTNet model, replacing the existing convolution layer with a double-attention convolution module, and introducing a time attention coefficient to improve a loss function of an output layer to obtain an improved LSTNet model; wherein the double-attention convolution module comprises a deep convolution unit, a channel attention unit, a point-by-point convolution unit and a time attention unit; training the improved LSTNet model based on the extracted features to obtain a new energy power generation parameter prediction model; in the training process, the improved LSTNet model is optimized based on a bat algorithm.

The application provides a marine oil spill detection method and system based on reinforcement learning and an improved bat algorithm, and relates to the technical field of target detection.The technical points of the application include: preprocessing the collected radar image;generating a pseudo label for the preprocessed image using a K-means clustering algorithm;extracting features of the preprocessed image using a self-encoder;classifying the preprocessed image using reinforcement learning, and extracting a region with an oil spill category in the classification result as an interesting region, thereby obtaining an interesting image;obtaining an optimal segmentation threshold value using an improved bat algorithm, and segmenting the interesting image using the optimal segmentation threshold value to obtain a segmented image based on the optimal segmentation threshold value, thereby realizing oil spill detection.The application solves the technical pain points of weak robustness and insufficient precision of traditional algorithms in oil spill segmentation, and provides reliable technical support for rapid monitoring and emergency response of marine oil spills.

The application discloses a kind of based on bat algorithm's optical fiber coupling efficiency promotion method, including the following steps: collecting optical power data and preprocessing;Coupling parameter search space model is constructed;Improved latent space reinforcement-bat algorithm is initialized, and initial coupling parameter vector is output;Introduce reversible flow coding to generate latent space vector;The optimal action vector is calculated by differentiable strategy flow mapping mechanism and output;Current iteration optimal bat individual is obtained;The coupling parameter vector of global optimal bat individual is output;Improved latent space reinforcement-bat algorithm is recalled again to carry out parameter optimization calculation, effectively improves the optimization speed and global optimal stability of optical fiber coupling, significantly reduces coupling loss, improves system automation adjustment precision and optical power stability.

The present invention discloses a method for predicting the strength of geopolymer concrete, which comprises the following steps: step 1: collecting geopolymer concrete strength experimental sample data to train a convolutional neural network, and constructing an initial model for predicting the strength of geopolymer concrete; step 2: optimizing the initial meta-parameters in the initial model for predicting the strength of geopolymer concrete by using a bat algorithm to obtain the optimal values ​​of the meta-parameters; step 3: updating the initial prediction model according to the optimal values ​​of the meta-parameters, and constructing an optimized geopolymer concrete strength prediction model; step 4: outputting a predicted value of the compressive strength of the predicted concrete according to the geopolymer concrete strength prediction model; the present invention is conducive to accurately predicting and evaluating the compressive strength value of geopolymer concrete in industrial production, thereby promoting production.

The present invention relates to the field of data processing technology, and in particular to an acoustic decoking control system and method for use inside a boiler. The method comprises: real-time collection of coke scale thickness data inside the boiler; optimizing a bat algorithm based on the coke scale thickness data, including: determining the degree of change in the coke scale data within a reference period at the current moment, sequentially determining the fitness value of each solution in each iteration at the current moment, the search frequency range optimization factor, and optimizing the search frequency range upper limit to complete the optimization of the bat algorithm; utilizing the optimized bat algorithm to control the acoustic wave frequency during the acoustic decoking process, so as to achieve acoustic decoking control inside the boiler. The present invention realizes intelligent control of the acoustic wave frequency based on the real-time boiler coke scale status through adaptive optimization of the bat algorithm, thereby significantly improving the intelligence level, control effect, and operating efficiency of the boiler decoking process.

The invention discloses an electric power system virtual inertia configuration method and device, terminal equipment and a storage medium, and belongs to the field of electric power systems.The method comprises the steps that according to electric power system parameters and the total virtual inertia amount, the total investment cost is minimized, and the frequency stability index is maximized; constructing an objective function, a critical inertia constraint, a system frequency change rate constraint, a maximum frequency deviation constraint and a system steady-state frequency deviation constraint of the virtual inertia optimal distribution model; solving the virtual inertia optimal distribution model through a bat algorithm to obtain a target virtual inertia; and performing virtual inertia configuration according to the target virtual inertia of each unit. According to the method, the minimization of the total investment cost and the maximization of the frequency stability index are taken as objective functions, and the economic benefit of the power system and the maximization of the virtual inertia configuration efficiency are considered at the same time. Therefore, by implementing the method, the technical vacancy of virtual inertia configuration for considering economic benefits in the prior art can be filled.

The application discloses a kind of contact resistance analysis method and related device of single-core cable joint, by extending improvement to single-core cable joint contact resistance sensing mode, based on SVM (support vector machine) algorithm development cable contact resistance inversion sensing model, utilize BA (bat) algorithm optimization inversion model parameter, improve the accuracy of model inversion. The surface hotspot temperature of single-core cable joint monitored in real time in actual field is used to quickly judge the interval of cable joint contact resistance, greatly simplify the sensing process and flow of single-core cable joint contact resistance in industrial field, reduce the serious misjudgment or misjudgment of single-core cable joint thermal fault in industrial application. Thus, the technical problems that the existing technology cannot quickly judge the contact resistance of single-core cable joint due to the complexity of the calculation process, resulting in the inability to achieve online monitoring are solved.

The invention relates to an Internet of Things time series data prediction method and system based on intelligent group optimization deep learning, and belongs to the technical field of Internet of Things. The method comprises the following steps: standardizing time series data of the Internet of Things; decomposing the standardized data into high-frequency, intermediate-frequency and low-frequency component groups by adopting an empirical mode decomposition method; an improved bat algorithm is constructed, wherein the improvement comprises the steps of introducing a dynamically changing inertia weight, adopting a Levy flight strategy, introducing Cauchy random disturbance and adopting linear progressive decrease adjustment on loudness and pulse emissivity; performing adaptive optimization on hyper-parameters of a deep learning prediction model by using the improved bat algorithm; and finally, training independent prediction models for the high-frequency component group, the medium-frequency component group and the low-frequency component group by using the optimal hyper-parameter combination, performing linear superposition reconstruction on the prediction results of the components, and executing a destandardization operation to obtain a final prediction value. According to the method, the accuracy of time series data prediction of the Internet of Things and the optimization efficiency of the model are remarkably improved.

The invention discloses a distributed photovoltaic grid-connected broadband oscillation suppression method. The method comprises the following steps: acquiring data information of a target power system; calculating to obtain a probability small signal stability margin value based on the combined cumulant and a Gram-Charlier expansion scheme; with the margin maximization of the power damped oscillation controller as an objective function, constructing a parameter optimization model of the power damped oscillation controller; solving the constructed model based on a bat algorithm and a deep neural network; and according to a solving result, an electric damping oscillation controller is adopted to control the target electric power system so as to realize distributed photovoltaic grid-connected broadband oscillation suppression. The invention also discloses a system for realizing the distributed photovoltaic grid-connected broadband oscillation suppression method, and a storage medium comprising the distributed photovoltaic grid-connected broadband oscillation suppression method. According to the distributed photovoltaic grid-connected broadband oscillation suppression method, suppression of distributed photovoltaic grid-connected broadband oscillation can be realized, the reliability is higher, the accuracy is better, and the effect is better.

The invention discloses an underwater robot lithium ion battery state collaborative estimation and service life optimization management system and method, and relates to the field of underwater robot battery management, and the method comprises the steps: building an Rint equivalent circuit model, calibrating the nonlinear function relation and internal resistance characteristics between an open-circuit voltage and a state of charge through an experiment, and calculating the service life of an underwater robot lithium ion battery. Adopting an ampere-hour integral method fused with dynamic capacity correction to realize state-of-charge estimation; performing collaborative optimization on parameters of the deep extreme learning machine by using a BA-PSO hybrid optimization algorithm fused by a bat algorithm and a particle swarm algorithm, constructing a battery health state prediction model, and performing health state estimation; the battery capacity attenuation is quantized into economic cost, and the economic cost and system operation energy consumption cost jointly form a multi-objective optimization function; designing a constraint processing mechanism based on a penalty function, and ensuring a battery power safety boundary; and an optimal power distribution strategy is solved by adopting a BA-PSO hybrid optimization algorithm, and cooperative control of the lithium ion battery and the super capacitor is realized.

The invention relates to the technical field of control adjustment, in particular to a control method and system based on a refrigerator shell production line. The method comprises the steps that power values and current values of an injection molding motor used for controlling the refrigerator shell production line at all moments are obtained; acquiring a self-adaptive loudness attenuation coefficient before the bat algorithm outputs an optimal solution for controlling the power value of the injection molding motor every time, including: determining a power data reference intensity and a power data real intensity of a time period to which each moment belongs, so as to obtain the self-adaptive loudness attenuation coefficient; constructing an objective function according to the energy consumption of each candidate solution before the optimal solution is output each time and the qualified rate of the product; and based on the adaptive loudness attenuation coefficient and the target function, screening the optimal solution output each time, and realizing control of the refrigerator shell production line. The injection molding motor data is collected in real time, the loudness attenuation coefficient of the bat algorithm is dynamically adjusted, and therefore the stability, efficiency and product quality of the refrigerator shell production line are improved.

The application discloses a pure phase array pattern synthesis method and system based on an improved bat algorithm, which comprises the following steps: setting an array model constructed by a plurality of antenna units; constructing an optimization objective function of the array model; minimizing the objective function according to a bat algorithm to complete optimization training of the array model; and performing pure phase array pattern synthesis according to the optimized array model. The embodiment of the application is aimed at pure phase symmetric pattern synthesis, an initial phase distribution is created by using a parabolic function, the distribution has the characteristic of an approximately flat-top mode, thereby accelerating the optimization process, in addition, the excitation distribution is expressed in a polynomial form, the optimization problem is converted into optimization of polynomial coefficients, thereby realizing antenna pattern synthesis, and the application can be widely applied to the technical field of computers.

The invention relates to the technical field of bat algorithms, and particularly discloses a radar emission angle adjusting method for a radar level meter based on the bat algorithm, which comprises the following steps: S1, acquiring an emission angle range of the radar level meter, and presetting a bat population and a maximum number of iterations; s2, performing iterative optimization on the bat to adjust the frequency, calculating the fitness value, the updating speed and the angle of the echo signal, and generating a local new solution; s3, issuing the local new solution, collecting echo data, calculating a fitness value corresponding to the local new solution, and calculating the pulse emissivity and loudness of the bats after iteration; and S4, updating the globally optimal solution and the fitness value, outputting the globally optimal solution and setting parameters of the radar level meter. According to the scheme, the optimization model is constructed based on the bat algorithm, the emission angle is converted into the algorithm search space, the search efficiency is balanced by dynamically adjusting the frequency, the emissivity and the loudness, the optimization parameters are verified in combination with a hardware closed loop, and the optimal parameters are solidified to realize stable measurement, so that the precision is improved.

The invention provides a high-voltage circuit breaker mechanical fault diagnosis method based on an improved bat algorithm optimized double-subsupport vector machine, and belongs to the technical field of high-voltage circuit breaker mechanical fault diagnosis. In order to solve the technical problems of low execution efficiency and low accuracy of diagnosing the fault of the high-voltage circuit breaker by adopting an expert system, a neural network or a support vector machine at present, the adopted technical scheme is as follows: collecting sample data of the mechanical fault of the high-voltage circuit breaker; the characteristic quantity of the sample data comprises a current time point and a current value in a circuit breaker opening and closing coil current curve, and the collected sample data is used as an initial sample; transforming the initial sample by using a principal component analysis method, extracting key features to form an optimal feature sample, and dividing the optimal feature sample into a training sample and a test sample; establishing two hyperplane equations by taking a Gaussian function as a kernel function, and solving to obtain a classification decision function so as to construct a gemini support vector machine model; the method is applied to mechanical fault diagnosis of the high-voltage circuit breaker.