91 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 relates to the technical field of resource optimization, in particular to an unmanned aerial vehicle distribution task real-time optimization scheduling management system, which comprises a bat sound wave calculation module for performing echo positioning frequency conversion calculation on the distance between the current position of an unmanned aerial vehicle and a target distribution point, a time window and a carrying weight upper limit value through a bat algorithm, and obtaining a task sound wave waveform matrix. According to the invention, the application of the bat algorithm enables the unmanned aerial vehicle to accurately calculate the task search space when the unmanned aerial vehicle executes the distribution task, and avoids the resource waste caused by a static distribution mode. The task search vector is formed based on the comparison operation of the task waveform amplitude and the distribution distance, so that the unmanned aerial vehicle can quickly adjust the task priority in a dynamic environment, and the execution accuracy of the distribution task is improved. The task group is optimized through the subspace decomposition operation, so that the unmanned aerial vehicle can execute multiple tasks in a parallel mode, the load pressure of a single unmanned aerial vehicle is reduced, and the cooperation efficiency of the overall tasks is improved.

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 an unmanned vehicle path planning method based on an improved bat algorithm, and belongs to the field of robot navigation, and the method comprises the following steps: carrying out the modeling of an environment through a grid method; in combination with the initial position, the target position and the fitness function of the unmanned vehicle, bat algorithm parameters are initialized; the invention provides an improved bat algorithm based on the bat algorithm. Chaotic mapping is introduced in a population initialization stage, so that the diversity of the population is increased; in a global search stage, a ranking-based adaptive inertia weight is introduced to constrain the speed of a bat individual, so that the global exploration and local development capabilities of the algorithm are coordinated; in the local search stage, fusing golden sine constraint local search and generation of a new solution, and enhancing the capability of jumping out of local optimum of the algorithm; and processing the path trajectory based on a cubic spline interpolation method. Simulation shows that compared with a bat algorithm, the improved bat algorithm is shorter in path trajectory length and shorter in planning time, and meanwhile, the path is smoother.

The invention discloses a craft IPA beer flora optimization method based on an improved bat algorithm. The method comprises the following steps: S1, constructing an initial state space of yeast flora proportion dynamic optimization; s2, standardized real-time process data are formed; s3, fusing the standardized real-time process data with the initial state space to obtain an updated comprehensive state vector, and dividing the whole fermentation process into a sugar collection stage, a main fermentation stage and an after-ripening stage according to a time sequence; s4, obtaining a staged entropy weight vector; s5, forming a multi-objective optimization problem; s6, obtaining an optimal flora proportion parameter set and an optimal fermentation regulation parameter set; and S7, transmitting the optimal flora proportion parameter set and the optimal fermentation regulation and control parameter set to a microfluidic bacterium throwing device, a material supplementing control unit and a frequency conversion temperature control unit to realize the optimization of the crafted IPA beer flora. According to the method, an optimization path can be effectively guided to be close to a flavor synergistic expression area, and the spatial consistency of yeast metabolism and aroma generation is greatly 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 wireless positioning blood pressure transducer real-time monitoring and analyzing method based on the Internet of Things. The method comprises the following steps that S1, invasive pressure measuring signals are collected, wirelessly transmitted and preprocessed; s2, a Transform invasive pressure measurement signal analysis model is constructed; s3, performing local adaptive optimization on the hyper-parameters by adopting a bat algorithm; s4, performing global optimization on the bat algorithm hyper-parameters by using a sparrow search algorithm; s5, performing multi-sensor positioning data acquisition and Kalman filtering fusion to obtain real-time positioning; s6, the blood pressure anomaly detection result and the positioning data are fused, and a combined health risk assessment report is formed; and S7, the remote early warning module transmits the joint evaluation report and the real-time positioning information. The system has the advantages that real-time monitoring and accurate positioning of the blood pressure transducer are achieved by integrating the Internet of Things, multi-sensor positioning and intelligent data analysis, and efficient and accurate data support is provided for remote medical treatment and intelligent health management.

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.

The invention discloses a resource allocation method based on a weighted chaotic bat algorithm in an ultra-dense network. Firstly, the traditional bat algorithm is mainly used to solve the nonlinear problem of continuous solution, but it is a discrete solution for resource block allocation, so the original algorithm needs to be modified to find the optimal solution for the discrete solution problem; in addition, the invention also uses the ergodicity and randomness of chaotic mapping to generate a chaotic sequence to replace the uniformly distributed random number in the original bat algorithm, thereby increasing the diversity of the population; in addition, the logarithmic decreasing inertia weight is used to accelerate the convergence speed; finally, the invention not only considers the situation that the number of resource blocks is greater than the number of home base station user equipment, but also considers the situation that under the state of resource scarcity, that is, when the number of resource blocks is less than the number of home base station user equipment, different initial population encoding methods are used, so as to make full use of resources and improve the robustness of the algorithm; in general, the method can effectively solve the resource allocation problem in the ultra-dense network and improve the throughput of the system by optimizing the 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 a construction method for automatically generating a three-dimensional VR scene based on a multi-view two-dimensional drawing. The construction method comprises the following steps: S1, collecting and preprocessing multi-view two-dimensional drawing data; s2, performing global feature extraction by using a bat algorithm to generate a spatial distribution feature vector; s3, establishing a feature point matching relationship, fusing the feature vectors, optimizing coordinates by using perspective transformation, and generating a global feature point cloud; s4, constructing a three-dimensional point cloud model based on the initial feature point cloud by using geometric mapping and spatial interpolation; s5, a simulated annealing mechanism is embedded in the position updating process, and local disturbance and energy evaluation are carried out on the candidate positions; s6, iteratively updating the candidate positions after local disturbance to obtain a global optimal feature point set; and S7, refining the preliminary three-dimensional point cloud model, and loading the preliminary three-dimensional point cloud model to a virtual reality platform to generate a three-dimensional VR scene. According to the method, the automatic conversion from the multi-view two-dimensional drawing to the high-precision three-dimensional VR scene is realized, and the modeling efficiency and precision are remarkably 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 invention discloses a high-temperature superconducting transient electromagnetic nonlinear inversion method and system. The method comprises the following steps: acquiring actual transient electromagnetic data of the earth surface; constructing a layered resistivity model according to the actual earth surface electromagnetic data and preset theoretical earth surface electromagnetic data, wherein model parameters in the layered resistivity model comprise resistivity and layer thickness; electromagnetic data inversion is conducted on the layered resistivity model through a preset improved bat algorithm, stratum parameters of the exploration area are obtained, and the stratum parameters are the resistivity and the layer thickness of each horizontal layered medium. The global and local search of the algorithm in the evolutionary process is balanced through the spiral flight strategy, the solving precision of the algorithm is improved, the algorithm is prevented from falling into local optimum too early, and the calculation efficiency and the inversion precision are greatly 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 present invention provides a method for diagnosing centrifugal pump acoustic signal faults, comprising: obtaining acoustic signals of a centrifugal pump under different operating conditions; extracting Mel-frequency cepstral coefficients of the acoustic signals as the initial feature matrix of the acoustic signals; obtaining a Mel-frequency cepstral scatter entropy feature matrix as a sample set using a scatter entropy algorithm; training a pre-trained support vector machine classifier using the sample set, and optimizing the parameters of the pre-trained support vector machine classifier using a bat algorithm during the training process to obtain an acoustic signal fault diagnosis model; and diagnosing centrifugal pump faults using the trained acoustic signal fault diagnosis model. The present invention incorporates scatter entropy into the Mel-frequency cepstral coefficient feature extraction process to deeply mine the signal, thereby improving the accuracy of centrifugal pump fault diagnosis based on acoustic signals. At the same time, the bat optimization algorithm is used to optimize the kernel function and penalty factor of the classifier, thereby determining various fault conditions of the centrifugal pump.

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 invention relates to the field of power grid intelligent planning, and discloses a power grid investment scale prediction method, computing equipment and a storage medium, and the method comprises the steps: constructing a least square support vector machine model according to a plurality of key influence factors of the power grid investment scale; setting an optimization function of the least square support vector machine model, and enabling each training sample in the training sample set to be on a hyperplane constructed by the least square support vector machine model; determining an adjustment factor of the least square support vector machine model and a kernel parameter of the kernel function according to a bat algorithm to obtain an optimized least square support vector machine model; and training the optimized least square support vector machine model according to the training sample set. Obtaining a trained least square support vector machine model; and in response to received to-be-predicted key influence factor data, inputting the received to-be-predicted key influence factor data into the trained least square support vector machine model, and determining a predicted power grid investment scale.

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.