130 results about "Firefly algorithm" patented technology

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention discloses a fan gearbox fault diagnosis and early warning system based on oil and vibration parameter fusion, and the system comprises an oil parameter and vibration signal collection module which is used for collecting oil parameters and vibration signals of a fan gearbox in real time through an oil sensor and a vibration sensor; the data fusion optimization module is used for optimizing the fusion weight of the oil and vibration data by adopting a firefly algorithm; the feature extraction and subset selection module is used for performing feature extraction and optimization on the fused oil parameters and vibration signals; the fault diagnosis module is used for inputting the optimized feature vector into a fault diagnosis model; the fault early warning generation module is used for generating a fault early warning signal in real time according to the output of the fault diagnosis model; and the closed-loop updating module is used for forming a closed-loop optimization-diagnosis-feedback iteration mechanism. According to the invention, fault diagnosis and early warning of the fan gearbox based on oil and vibration parameter fusion are realized for users.

The invention discloses a workshop production scheduling optimization method and system based on a digital model, and relates to the technical field of digital models, and the method comprises the following steps: S1, collecting data, and building an initial digital model; s2, collecting equipment data in real time; s3, generating a dynamically updated digital model based on the equipment data; s4, local optimization is carried out in combination with a collaborative particle swarm optimization algorithm; s5, performing global search by using a firefly algorithm; s6, performing simulation verification evaluation on the preliminary production scheduling scheme; s7, updating the dynamically updated digital model in real time; and S8, monitoring the execution effect and correcting the execution effect in real time. According to the invention, intelligent optimization and dynamic closed-loop control of workshop scheduling are realized, and the production efficiency and the resource utilization rate are remarkably improved.

The invention discloses a point cloud data-based steel structure virtual assembly prediction method and system, and relates to the technical field of steel structure virtual assembly prediction, and the method comprises the steps: collecting point cloud data, carrying out the preprocessing, dividing the point cloud data into subsets, calculating the geometric characteristics of the subsets according to the divided subsets, and carrying out the optimization based on the geometric characteristics, and obtaining an optimal feature vector; performing category classification based on the optimal feature vector, further analyzing component features through a classification result, constructing a path optimization model, and taking the optimal feature vector as input to obtain a final assembled path sequence; according to the method, the particle swarm optimization algorithm is combined with the weighted fusion method, high-precision extraction of the geometric characteristics of the point cloud is achieved, the classification and fitting precision is remarkably improved through covariance matrix characteristic decomposition and linear, plane and spherical surface characteristic construction, and then a path optimization model with the combination of the fire-fisch algorithm and the multi-layer perceptron is utilized, so that the classification and fitting precision is improved. And the error, the length and the stability of the assembly path are comprehensively optimized.

The invention discloses a smart watch health monitoring management and early warning method based on a multi-modal sensor, and the method comprises the following steps: S1, collecting and preprocessing multi-modal physiological data, and generating a time sequence health data set; s2, constructing a health state memory network, and outputting individualized health state characterization; s3, performing cross attention fusion on the layered time sequence features to form a global health feature vector; s4, performing health anomaly detection and scoring by adopting a firefly algorithm; s5, optimizing a firefly algorithm search process by using a genetic algorithm; s6, dynamically adjusting an early warning threshold value, and generating personalized health early warning information; and S7, early warning is triggered during anomaly detection, and a health alarm is sent through wireless communication. Through multi-modal data acquisition, deep health state modeling and an intelligent optimization algorithm, real-time accurate detection and personalized early warning of health abnormity by the smart watch are realized, so that an efficient and reliable health management solution is provided for a user.

The invention discloses a communication cabinet temperature adaptive control method and system, and relates to the technical field of temperature control. Determining a first environment parameter of the target communication cabinet, and performing adaptive optimization on the first environment parameter through an improved firefly algorithm to obtain a target environment parameter; simulating a target environment according to the target environment parameters to perform simulation operation to obtain PID control parameters; optimizing the PID control parameters through an improved sparrow algorithm to obtain target control parameters, and updating the temperature control system according to the target control parameters; the improved firefly algorithm optimizes environmental parameters, and the accuracy and the anti-interference capability are improved; a PID effect is verified based on an accurate parameter simulation environment, and dynamic tuning is realized; the improved sparrow algorithm is combined to optimize control parameters, temperature adaptive control precision and response speed are enhanced, stable and efficient operation of the communication cabinet under different working conditions is ensured, and equipment reliability is guaranteed.

The invention belongs to the technical field of solid waste treatment, and particularly relates to a reprocessed plastic blow molding product quality stability self-adaptive adjusting system. And the blow molding process parameters are optimized by using neural network integration, firefly algorithm optimized fuzzy-PID control, deep reinforcement learning, Bayesian optimization and a chaos optimization algorithm. According to the algorithms, for enterprises with different scales and requirements, the product quality is effectively improved, the production efficiency is improved, the defective rate and the energy consumption are reduced, and diversified quality optimization strategies are provided for the regenerated plastic blow molding industry.

The invention provides a rolling quality parameter uncertainty quantification method based on mixed entropy-fuzzy clustering. The method comprises the following steps: S1, multi-source data fusion: integrating rolling compaction parameters, meteorological data and real-time monitoring data in an engineering construction process, and constructing a multi-dimensional feature matrix; s2, calculating mixed entropy, namely quantifying the randomness and fuzziness of parameter distribution in combination with information entropy and fuzzy entropy; s3, performing dynamic fuzzy clustering, namely, optimizing a clustering center based on an improved firefly algorithm, and dividing parameter uncertainty levels; step S4, uncertainty contribution degree analysis: quantifying the influence weight of each parameter on the rolling quality through an entropy weight-grey correlation method; according to the method, the information entropy and the fuzzy entropy are fused, and dynamic clustering and an intelligent optimization algorithm are combined, so that precise quantification and hierarchical management and control of the rolling parameter uncertainty are realized.

The invention relates to the technical field of automobile charging station load systems, in particular to a new energy automobile charging station dynamic load optimization integration system and method based on multi-source data collaboration and elastic compensation. According to the system, charging pile power, meteorological data and user behavior information are fused in real time through a multi-source time sequence data set construction module, an LSTM neural network optimized by an improved sparrow algorithm is adopted to predict the remaining power capacity of a charging station, and Bootstrap resampling is combined to generate a confidence interval to realize safety boundary control; the self-adaptive firefly algorithm dynamically adjusts the charging pile power, establishes a multi-dimensional priority queue including SOC emergency degree, compensation willingness and social value, and guides users to charge in a peak shifting manner; the system comprises a 5G communication gateway, an IGBT power control module and a three-level early warning mechanism, and millisecond-level flexible adjustment of charging power is realized. The utilization rate of the transformer is improved to 91%, the peak-valley load ratio is reduced to 1.7: 1, the user waiting time is shortened by 63.5%, and a dynamic load management system of power grid-station-user collaborative optimization is formed.

The invention discloses a multi-parameter identification method for a valve-controlled cylinder electro-hydraulic servo system, and belongs to the field of servo technology control. The method comprises the following steps: firstly, carrying out mathematical modeling according to components and a working principle of the valve-controlled cylinder electro-hydraulic servo system to obtain a system model and to-be-identified parameters; secondly, an improved multi-strategy firefly algorithm is provided, the randomness and uniformity of an initial solution are enhanced through an improved Chebyshev polynomial, the global search capability of the algorithm is improved, an updating algorithm is introduced to modify a step length factor of the firefly algorithm, and balance between local search and global exploration is achieved; finally, parameters of the established model are identified through an improved algorithm, simulation and experimental analysis are conducted, and according to the result, the accuracy of the model can be effectively improved, the robustness of the model can be improved, and reliable theoretical and practical support is provided for actual engineering application of the high-precision electro-hydraulic servo system.

The invention provides a pump station water level dynamic boundary adjusting method based on a fuzzy firefly algorithm, which comprises the following steps of: presetting a target water level in an intelligent water level monitoring system, calculating water level deviation and a water level change rate, and determining a sensitive parameter; presetting a linguistic variable of a fuzzy variable to obtain a fuzzy subset about a high-sensitivity parameter, a fuzzy subset about a low-sensitivity parameter, a fuzzy subset about a dynamic boundary adjustment value and a fuzzy subset about a flow adjustment value; determining a corresponding triangular membership function according to the input variables and the output variables of the different sensitive parameters; setting a fuzzy rule, and calculating the membership degree of each input variable; determining the fuzzy quantity output of the dynamic boundary adjustment value according to the fuzzy rule activation condition and the membership function value; and performing precision on fuzzy quantity output of the dynamic boundary adjustment value by adopting a gravity center method to obtain an accurate dynamic boundary adjustment value. According to the invention, the pump station can be controlled more accurately under various dynamic hydrological conditions.

The invention provides a cooperative scheduling method for an electric medical waste transfer vehicle and a mobile battery exchange vehicle, and relates to the field of scheduling optimization, and the method comprises the steps: obtaining client data, and initializing algorithm parameters; constructing an initial feasible solution, calculating the total cost and the infection risk of the path of each firefly, and selecting the firefly path with the minimum objective function value as a current global optimal solution; selecting mutation operation to generate a neighborhood solution, calculating a corresponding target function value, and comparing the target function value with the current globally optimal solution to update the globally optimal solution; and updating the position of each firefly in the population according to the attraction mechanism of the firefly algorithm, performing adjustment and feasibility test on the solution in combination with mutation operation in the position updating process, and performing iterative optimization until termination. According to the method, the firefly algorithm and the variable neighborhood search algorithm are combined to realize efficient path optimization and charging scheduling, so that balance between total cost minimization and infection risk minimization is achieved, the medical waste transfer efficiency is improved, and the operation cost is reduced.

The invention relates to an MPPT (Maximum Power Point Tracking) method, system and equipment based on distributed photovoltaic and a medium, and relates to the technical field of distributed photovoltaic power generation, and the method comprises the following steps: preprocessing photovoltaic array data, and obtaining irradiance, temperature and MPP voltage data sets; determining the optimal number of hidden layer nodes of the ElmanNN by adopting K-fold cross validation and RMSE evaluation, and dividing a data set into a training set, a validation set and a test set; inputting the training set to firefly algorithm global search and ant colony optimization algorithm local optimization, and defining a stable solution as an FA-EAS optimal parameter after convergence; updating an ElmanNN weight threshold based on the optimal parameter, and deploying a training model to carry out MPP voltage online prediction; and the difference value of the predicted voltage and the reference voltage is input into a PID controller, the control quantity is calculated through incremental PID, a PWM signal is generated to control the duty ratio of the Boost converter, and maximum power point tracking is achieved. According to the invention, the tracking speed, stability and success rate of MPPT can be improved.

The invention discloses an unmanned aerial vehicle path deception jamming method based on an intelligent optimization algorithm, belongs to the technical field of unmanned aerial vehicle path deception jamming, is used for unmanned aerial vehicle path deception jamming, and comprises the steps of establishing an unmanned aerial vehicle kinetic equation and a kinematics equation, and obtaining an expected flight trajectory of an unmanned aerial vehicle; a particle filtering method based on firefly algorithm optimization is introduced, and the state of the unmanned aerial vehicle is tracked and estimated; an improved particle swarm optimization algorithm is introduced to calculate the public delay amount in the forwarding interference, and the positioning information of the unmanned aerial vehicle is interfered and guided. Compared with the prior art, the real flight process and maneuvering change of the unmanned aerial vehicle are simulated, and the attitude change and dynamic response of the unmanned aerial vehicle are reflected; the problems of particle degradation and sample depletion are relieved, and the tracking stability and precision under noisy observation are improved; the deception positioning precision and the system robustness are improved, and the overall deception jamming effect is enhanced; and a technical support is provided for improving the deception jamming capability of a tail end defense system.

The invention discloses a load flow calculation method and device based on a hybrid wing optimization algorithm, and belongs to the technical field of power system operation and maintenance, and the method comprises the steps: employing an improved firefly algorithm to combine with k-means clustering to achieve power data cleaning and noise reduction; secondly, constructing a mixed rattan model, and capturing the output data correlation of photovoltaic power generation and wind power generation through rattan structure decomposition and a connection function; and finally, designing a hybrid wing optimization algorithm, and solving a power flow optimization problem through guidance of a leader and a Cauchy variation strategy. According to the method, the complex dependency relationship in power flow calculation of the power system can be effectively processed through the combination of the hybrid rattan model and the hybrid wing algorithm, the calculation precision and efficiency are remarkably improved, and the adaptability to a complex power network is enhanced.

The invention provides an error correction method suitable for a three-component magnetic sensor, and the method comprises the steps: building a comprehensive error model of a non-orthogonal error, a sensitivity error and a zero offset error, and solving an error parameter through an intelligent optimization strategy fusing a particle swarm optimization algorithm and a firefly algorithm, thereby achieving the error correction of the three-component magnetic sensor. The method specifically comprises the steps of using a magnetometer to synchronously measure a geomagnetic field module value as a reference standard, combining collected three-component magnetic field data to construct a fitness function, searching an optimal error parameter set by using a particle swarm optimization and firefly fusion algorithm, and compensating the magnetic field data; according to the method, an intelligent optimization strategy based on particle swarm optimization and firefly algorithm fusion is adopted, the magnetic field measurement precision can be effectively improved, and high-precision solving of error parameters and accurate compensation of magnetic field data are achieved.

The invention discloses an ultrasonic image processing method based on image analysis, and the method comprises the following steps: S1, carrying out the noise suppression, enhancement and normalization of an original ultrasonic image, and improving the image quality; s2, performing initial boundary positioning on the image by adopting a reverse firefly algorithm, and determining a target area; s3, extracting an accurate organization structure based on the deep segmentation network in combination with boundary guidance information; s4, extracting form, texture, gray scale and depth features of the segmented region, and constructing a high-dimensional medical feature vector; s5, selecting an optimal feature subset by using a grey wolf optimization algorithm, and removing redundancy; s6, training the diagnosis model and optimizing hyper-parameters of the diagnosis model through a grey wolf algorithm; and S7, performing identification and risk grading on the new image by using the model, and completing auxiliary diagnosis. According to the invention, accurate recognition and intelligent diagnosis of the ultrasonic image are realized, and the processing efficiency and the diagnosis accuracy are remarkably improved.

The invention discloses a manufacturing process quality diagnosis method based on an adaptive multivariate control chart. The method comprises the following steps: step 1, constructing an adaptive multivariate exponential weighted moving average control chart; and step 2, constructing a fuzzy support vector machine diagnosis model of firefly algorithm collaborative optimization, and using the model to complete manufacturing process quality diagnosis. According to the method, normal fluctuation and abnormal fluctuation areas in the manufacturing process can be identified, and accurate tracing of abnormal modes is realized, so that quality diagnosis of the manufacturing process is completed.

The invention relates to the technical field of state prediction, in particular to an electric energy meter battery life prediction method for optimizing XGBoost through an improved firefly algorithm, and the method comprises the steps: collecting the temperature T, the voltage V and the use frequency F of an electric energy meter and the service life L of a battery, carrying out the normalization processing of collected data, dividing a data set into a training set and a test set, and carrying out the prediction of the service life of the electric energy meter. The method comprises the following steps: using a minimum square error (MSE) as a target function to evaluate the performance of a model, initializing firefly algorithm parameters, iteratively optimizing XGBoost, after executing all iterations, selecting the position xbest of a firefly with the optimal fitness as an optimal hyper-parameter combination, training a final XGBoost regression model by using the optimal hyper-parameter combination, and evaluating the performance of the model on a test set. Calculating MSE (mean square error); by predicting the remaining life of the battery, the battery can be replaced before failure, the situation of loss of a power-off reading is avoided, the labor cost is reduced, and the metering, operation and maintenance efficiency is improved.

The invention discloses a charging pile abnormity identification method based on deep learning. The method comprises the following steps: S1, obtaining operation data of a charging pile for preprocessing; s2, based on the preprocessed data, using a deep learning algorithm to construct a charging pile abnormity identification model; s3, adopting a firefly algorithm to optimize hyper-parameters of the model; s4, adjusting a model structure based on the optimized hyper-parameter; s5, collecting data of the charging pile in real time, inputting the data into the optimized model for abnormal state recognition, and generating abnormal alarm information; s6, according to the abnormal alarm information, automatically triggering an alarm and feeding back the alarm to maintenance personnel for processing; and S7, continuously collecting new charging pile data, and performing incremental learning and updating by using the optimized model. According to the invention, efficient and accurate identification of charging pile anomaly detection is realized, the fault processing speed and the system stability are improved, and the method is widely applied to charging pile management and maintenance.

The invention specifically discloses a compliance control method and device for a power transmission tower operation and maintenance robot and a medium, and the method comprises the steps: determining an impedance control model of a robot impedance controller, carrying out the adaptive optimization of impedance control parameters based on an improved firefly algorithm, and enabling the improved firefly algorithm to be based on a standard firefly algorithm, and a self-adaptive step length factor is adopted for fusion, meanwhile, an improved algorithm of an annealing mechanism is introduced, and finally, the robot is controlled based on optimized parameters. The dynamic response characteristic of the mechanical arm is continuously tracked, the improved firefly algorithm is used for updating impedance parameter configuration in real time, the improved firefly algorithm improves the global search capability and the convergence speed, the optimal control effect is achieved, the adaptive capacity of the system to diversified operation scenes is improved, and the system performance is improved. A parameter optimization strategy based on real-time data analysis successfully overcomes the adaptability problem caused by parameter solidification in conventional impedance control, and the stability and safety of the power transmission tower operation and maintenance robot in the movement process are improved.

The present invention relates to an improved sparrow search method based on penalty function constraint optimization. This method is an improved sparrow search method based on penalty function constraint optimization that integrates the sine-cosine algorithm and the light-sensing strategy of fireflies. By generating the initial population in combination with Cubic chaotic mapping in the initial population initialization stage, the accuracy and distribution of the initial population are improved; in the iterative process of updating the position of the followers, the sine-cosine algorithm is added to improve the position update strategy of the followers, making the search mechanism of the followers more flexible, expanding the global exploration range, increasing the population diversity, increasing the probability of finding the optimal solution, and accelerating the convergence speed; finally, the light-sensing idea of the firefly algorithm is used to refine and optimize the method, helping the method to jump out of local extrema, providing a better parental population for the next iteration, providing an optimization direction for the method, avoiding blind search, accelerating convergence, and thus improving the optimization quality of the method.

This invention provides a pipeline leak location method based on tensor completion, comprising: collecting leakage vibration data of a leaking pipeline using wireless accelerometers, wherein the leakage vibration data includes data sampled by N wireless accelerometers over M time periods; constructing a tensor completion model using a truncated tensor weighted norm algorithm, and constructing an optimal three-dimensional tensor structure using the firefly algorithm based on the leakage vibration data of the leaking pipeline and the tensor completion model; inputting the optimal three-dimensional tensor structure into the tensor completion model for completion; and calculating the leakage location of the leaking pipeline based on the completed three-dimensional tensor. This invention utilizes a truncated tensor weighted norm to construct a tensor completion model, which efficiently preserves effective information in the missing data, improves the tensor completion accuracy, ensures effective recovery of effective feature information from the pipeline leak data, and improves location accuracy.

The invention provides an unmanned aerial vehicle countering decision control method based on multi-target identification distribution, relates to the technical field of unmanned aerial vehicle countering, and solves the limitation problems of low detection precision, unstable tracking, rigid task distribution and the like in the existing unmanned aerial vehicle countering technology. The method comprises the steps that firstly, an RF-DETR model is used for detecting and recognizing all target unmanned aerial vehicles invading a preset airspace, and initial parameters of targets are obtained; predicting and tracking a target trajectory by using a DeepSORT algorithm, generating dynamic labeling information with a unique ID for each target, and maintaining a tracking state; dynamic environment parameters are obtained, the threat degree of each target is corrected according to the dynamic environment parameters, and a countering task corresponding to each target is distributed to at least one countering unmanned aerial vehicle through an adaptive firefly algorithm; and according to the distribution result, applying a differential countering strategy to each target, and monitoring a countering effect in real time. According to the invention, efficient, rapid and highly intelligent unmanned aerial vehicle countering decision can be realized, and countering tasks can be assisted in real time.

The invention provides a sustainable earthwork optimization and allocation method based on DC-FACO, and relates to the technical field of optimization and scheduling. The method comprises the following steps: collecting site construction data of a navigation channel project, and constructing a multi-dimensional data matrix based on the site construction data; constructing a plurality of constraint conditions of earth-rock deployment based on earth-rock deployment logic and engineering characteristics of the navigation channel engineering, and constructing a collaborative optimization model based on the plurality of constraint conditions; and the collaborative optimization model is optimized based on a sustainable earthwork allocation model optimization mode of DC-FACO. According to the method, a core concept of sustainable construction can be met by constructing an economical efficiency and ecological benefit collaborative optimization model, a two-stage hybrid optimization framework is adopted, the global optimization ability is enhanced by improving an ant colony algorithm and fusing a dynamic pheromone updating mechanism and a firefly algorithm, and collaborative optimization of the global search ability and stability is achieved.

The invention discloses a data-driven production line layout intelligent optimization and reconstruction method, and relates to the field of intelligent manufacturing engineering. According to the method, the cuckoo search algorithm, the group collaborative search mechanism and the firefly algorithm are fused, and intelligent optimization and dynamic reconstruction of the production line layout are realized. According to the invention, the stability of layout optimization and the performability of the scheme can be improved.

The application discloses a network intrusion detection method and system, electronic equipment and a storage medium. The method inputs to-be-detected data into a plurality of trained classifiers to perform network intrusion detection, and obtains an intrusion detection result output by each trained classifier. A firefly algorithm is used to perform firefly position optimization on each first firefly population to obtain a target firefly individual. The target firefly individuals of each first firefly population are combined to form a new population, and a target firefly individual meeting a target condition in the new population is found out. The target firefly individual in the new population is added to each first firefly population as a second firefly population for next iteration until the firefly algorithm reaches a maximum iteration number, and the position of the target firefly individual is obtained. According to a plurality of weight coefficients and the intrusion detection result output by each trained classifier, a final intrusion detection result is calculated. The application can improve the accuracy of network intrusion detection.

Disclosed is a shield tail gap image two-dimensional Otsu threshold segmentation method based on an improved firefly algorithm, which comprises the following steps: generating a population randomly in the gray scale [0, 255] range of a pipe segment image; obtaining the optimal two-dimensional firefly position in the population through iteration, which comprises the following steps: calculating the maximum brightness of each firefly in the population; sorting the maximum brightness of the fireflies in the population; for each firefly, forming a local colony with several fireflies whose brightness is lower than that of the firefly; performing an attraction operation between the fireflies in the local colony; updating the position of the firefly, and introducing an attraction force weight to strengthen the connection between the local colonies when updating the position; updating the maximum brightness of the firefly according to the position of the firefly after movement; and outputting the optimal two-dimensional firefly position after multiple iterations, so as to perform threshold segmentation on the pipe segment gray scale image.

The application discloses a kind of gas boiler main steam temperature self-disturbance decoupling control methods, comprising: signal detection device measures various signal data involved in the present application, then data is processed;Using the data after processing, the system model of double-input double-output is established, and the system model is identified;The system model after identification is decoupled, and the system is decoupled into two single-input single-output systems;For system, design linear self-disturbance controller to realize boiler main steam temperature stable control;Firefly algorithm is used to optimize linear self-disturbance controller parameter, and the control quality of main steam temperature control system is improved, the present application is aimed at the problems, such as nonlinearity, multiple disturbance and the like existing in the process of gas power generation boiler main steam temperature control, adopts the control strategy that linear self-disturbance control and decoupling control are combined, and the mutual coupling action between furnace gas flow and low-temperature superheater, desuperheating water flow and high-temperature superheater outlet temperature is decoupled and eliminated using Gauss elimination method.

The invention discloses a surge chamber area optimization method of a pumped storage power station and a related device. The method comprises the following steps: firstly, establishing a mathematical model of the pumped storage power station provided with upstream and downstream surge chambers; then, based on the mathematical model, establishing decision variables for optimizing the surge chamber system, determining constraint conditions of optimization parameters, and establishing a surge chamber fitness objective function about comprehensive optimization of the maximum water level fluctuation value and the water level change attenuation degree of the upstream and downstream surge chamber system; and finally, solving through a firefly algorithm to obtain an optimal upstream and downstream surge chamber area combination. According to the method, the pumped storage power station model with the upstream and downstream surge chambers is established, and the firefly algorithm is applied to the surge chamber area optimization of the pumped storage power station with the upstream and downstream surge chambers, so that the calculation speed is higher and the convergence rate is higher in the optimization process of the optimization parameters, and the problems that the prior art is not comprehensive enough in consideration, and the convergence rate is higher are solved. And the optimal upstream and downstream surge chamber area combination cannot be quickly and accurately obtained.