112 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 a multi-objective parallel waste product disassembly line optimization method based on the number of skills of workers, and relates to a waste product disassembly line optimization method, which comprises the following steps of: constructing a disassembly line balance model according to product information of a product to be disassembled, disassembly line information and disassembly skill information of the workers; constructing a target function by taking the minimum skill number of chemical workers and the maximum net profit of product disassembly as targets; setting constraint conditions of the disassembly line balance optimization model; encoding the disassembly task based on an encoding mechanism of a priority relation matrix, generating a feasible solution, and decoding; obtaining a task allocation scheme of the to-be-disassembled product; in an actual case, the IDBA is compared with five algorithms including NSGA-II, NSGA-III, SPEA-II, ESPEA, MOEA / D and the like. The improved discrete bat algorithm is provided according to the actual case of the disassembly line, the multi-target parallel disassembly line balance problem is optimized, and the task and skill distribution efficiency is 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 task scheduling method based on an improved bat algorithm in a mobile edge computing environment. The method comprises the following steps: acquiring user task information and edge server information, and acquiring a to-be-unloaded task sequence according to a user track; the user track, the user task information and the edge server information serve as input, an optimal solution of a current population is solved through a bat search algorithm, and each individual in the bat population is mapped to a corresponding task sequence; judging whether the task sequence needs to be subjected to local search or not, and if yes, performing local search by utilizing local sensitive hashing to obtain a global optimal solution after the local search; and according to the searched global optimal solution, dynamically updating the individual positions of the bat population until a set stop criterion is met, and outputting a final task scheduling decision. According to the method, the task scheduling quality is improved while the calculation efficiency is ensured, and the method has relatively high universality and expandability.

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.

The invention relates to the technical field of electric vehicle charging scheduling, in particular to a charging scheduling method, device and equipment considering the utilization rate of charging piles and a medium, and the method comprises the steps: building a charging station charging scheduling double-layer game architecture comprising a charging operator layer and a user layer; a user charging time cost and a charging economic cost are considered, and an electric vehicle user charging scheduling strategy of a user layer based on the comprehensive charging cost is established; the charging station utilization rate is considered, and a charging station charging dispatching model is established with the purpose of maximizing the charging station charging pile utilization rate; and based on an adaptive improved bat algorithm, solving the charging scheduling model to obtain an optimal scheduling method. According to the invention, the electric vehicles are guided to go to different charging stations to participate in the power system demand response, and the utilization efficiency of the charging piles of the charging stations is maximized. Compared with other types of mechanisms, the scheduling strategy is more flexible, the change of the demand capacity of the charging station can be reflected in a short time, and the method is more suitable for the demand response of the charging station aggregator.

The invention discloses a self-propelled sprayer scheduling method for soil mechanical compaction and reduction and water and fertilizer integration, and relates to the field of agricultural machinery scheduling. The method aims at meeting the requirement for the compaction degree of the black soil in the field and reducing the dispatching cost. According to the self-propelled sprayer scheduling method for soil mechanical compaction reduction and water and fertilizer integration, a target function is constructed with the lowest total scheduling cost and the maximum compaction satisfaction degree after operation as targets according to the actual operation requirements of a self-propelled sprayer; and taking the target function as a fitness function of a bat algorithm, and scheduling the self-propelled sprayer by adopting the bat algorithm. The method is suitable for the field of agricultural machinery dispatching and outputs the dispatching scheme of the self-propelled sprayer.

The invention discloses a power transmission line discharge voiceprint feature recognition method based on a WPE-BA-CNN optimization model. The method comprises the following steps: S1, carrying out preprocessing and noise reduction on a fault data set; s2, constructing a convolutional neural network (CNN) model; three convolutional layers and a maximum pooling layer are used for feature extraction, and then classification is carried out through a full connection layer to train a model; s3, the filtered signals are imported into a BA-CNN algorithm for voiceprint feature recognition and classification, the BA algorithm is used for CNN convolution kernel number parameter optimization, population parameters of the BA algorithm are initialized, the BA-CNN algorithm adopts a bat algorithm to optimize hyper-parameters of a convolutional neural network, and the number of three layers of convolution kernels serves as position parameters of bat individuals for optimization; according to the power transmission line discharge voiceprint feature recognition method based on the WPE-BA-CNN optimization model, on the basis of adopting the CNN model, wavelet packet transformation is combined to carry out frequency band energy analysis; and optimizing the hyper-parameters of the convolutional neural network by using a bat algorithm.

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 invention discloses a small current grounding system fault line selection method based on a feature fusion improved bat algorithm optimized support vector machine, and belongs to the field of medium voltage power distribution system fault line selection. The method comprises the following steps: firstly, processing a line zero-sequence current signal by using a denoising method in which S transformation threshold filtering and a time-frequency filter based on time-frequency spectrum distribution are combined, then performing line selection by taking S transformation energy relative entropy and singular entropy as double characteristic quantities by using a characteristic fusion technology, and then optimizing a support vector machine (SVM) by using an improved bat algorithm (IBA). And an IBA-SVM optimization classifier is constructed to carry out classification processing on the fault feature data set, and finally identification of the fault line is realized. Simulation and experimental data verification show that the method has high accuracy and practicability.

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 belongs to the technical field of gaseous pollutant simulation, and provides a gaseous pollutant space sampling point layout method and system, electronic equipment and a storage medium. The method comprises the following steps: simulating a gas flow field of a target space through fluid dynamics simulation, and establishing a pollutant diffusion model according to a gas diffusion rule in the gas flow field; defining a fitness function of sampling point layout according to the pollutant diffusion model, and reflecting pollutant concentration and redundancy of a sampling point coverage area; and iteratively optimizing the fitness function by using a bat algorithm, improving the pollutant concentration and reducing the redundancy to obtain an optimal sampling point layout. The optimal sampling point layout is achieved, redundant sampling points are reduced, the monitoring coverage rate is maximized, and it is ensured that pollutant leakage can be detected and responded in time.

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 invention relates to the technical field of water-light-storage complementary operation, and discloses a method for evaluating the power generation influence on a water-light-storage complementary system from a small hydropower transformation pumped storage unit, which comprises the following steps: constructing a cascade small hydropower-oriented water-light-storage complementary system joint optimization scheduling model; converting the multi-target optimization scheduling model into a single-target optimization scheduling model; solving and calculating the processed cascade small hydropower-oriented water-light-storage complementary system joint optimization scheduling model by adopting a bat algorithm; constructing a cascade small hydropower station-oriented water-light complementary system joint optimization scheduling model, and performing solving calculation of the model by adopting the bat algorithm; and analyzing the power generation influence of the small hydropower transformation hybrid pumped storage unit on the water-light complementary system by comparing the instance calculation results of the two joint optimization scheduling models in typical days of different weather scenes. The method has the advantages that power generation benefit comprehensive analysis of the pumped storage unit transformed from the small hydropower station is realized, and important technical support can be provided for new energy development and utilization for the cascade small hydropower station.

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.