166 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 video transmission method and system based on streaming processing, and relates to the technical field of video transmission, and the method comprises the steps: collecting a video stream, carrying out the preprocessing, extracting a video frame, packaging the video frame into a picture stream, carrying out the data fragmentation, distributing an initialization path for fragmented data, and calculating an initial traffic proportion; and optimizing path selection and adjusting path flow distribution according to network feedback to obtain a path optimization result, and transmitting the fragmented data according to the path optimization result. Through multi-level feature extraction and key frame selection of video frames, fragmented packaging of video data is realized, a dynamic path optimization technology is combined, path selection and flow distribution are dynamically adjusted based on a Q learning algorithm and a luminescence firefly algorithm, the transmission efficiency is improved, video image processing and a dynamic network optimization technology are fully combined, and the video image processing efficiency is improved. And a brand new solution is provided for efficient and stable transmission of high-definition video streams.

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 provides a dynamic monitoring and management platform based on vision and sensing fusion, and relates to the technical field of dynamic monitoring and management, and the platform comprises a vision sensor module which is used for collecting image data of a target object; the physical sensor module is used for collecting physical parameter data of a target object, and the physical parameter data comprises temperature, humidity, pressure, acceleration and angular velocity; and the data processing module is used for carrying out feature extraction on the visual and physical sensor data, creating a category score matrix, fusing classification results and weights of images and physical features, taking a final category label as a fusion result, and obtaining dynamic state information of the target object according to the fusion result. Through fusion of visual and sensing data, real-time dynamic monitoring and an optimization management strategy based on a firefly algorithm, accurate and efficient state monitoring, early warning, evaluation and maintenance decision making of the target object are realized.

The invention provides a carbon peak-reaching path planning method, system and equipment considering energy conservation and carbon reduction cost, and is applied to the technical field of energy conservation and environmental protection. The method comprises the following steps: for a carbon peak-reaching planning period, considering the energy activity of a planning area and the carbon emission influence of industrial production, and constructing a carbon emission model; considering the influence of energy conservation and carbon reduction on the economy of the planning area, and constructing a carbon emission economic disturbance model; taking minimization of carbon emission of the planning area and economic disturbance of the carbon emission as targets, and based on a carbon emission model of the planning area, a carbon emission economic disturbance model and a coupling relation between the carbon emission and economic indexes, constructing a carbon peak multi-target optimization model; based on an improved multi-target firefly algorithm, carrying out optimization solution on the carbon peak reaching multi-target optimization model of the planning area; and planning a carbon peak-reaching path of the planning area in the carbon peak-reaching planning period based on a solving result. According to the invention, the problem of poor implementability of the existing carbon-to-peak path is solved.

The invention provides a task unloading method for an emergency rescue scene under a space-air-ground integrated network, and relates to the technical field of calculation unloading, and the method comprises the steps: unifying the feature description of task unloading; establishing a joint optimization problem model by taking minimization of unloading time delay and power consumption as an optimization target; determining a task unloading priority based on a firefly algorithm; constructing a reward mechanism of a deep reinforcement learning model according to the task unloading priority, and solving the joint optimization problem model by using a deep reinforcement learning algorithm to obtain a plurality of task unloading schemes; performing simulation verification based on the task unloading scheme to obtain evaluation data; parameters of the joint optimization problem model are corrected according to the evaluation data, and a task unloading optimal model is obtained; and solving the optimal task unloading model to obtain a final task unloading scheme. Reasonable and effective resource allocation is carried out on a large number of tasks which emerge in an emergency rescue scene and need to be processed in real time, and the scheduling efficiency is improved.

The invention relates to the technical field of intelligent power grids, in particular to a power chip computing power resource scheduling method, system and device and a medium, and the method comprises the steps: obtaining original computing power resource data needed by a power chip to execute a computing task from a power system in real time, and preprocessing the original computing power resource data to obtain to-be-processed computing power resource data; according to the to-be-processed computing power resource data, obtaining the total load capacity of service computing data; in response to the chip computing power distribution instruction, determining a fitness function according to the total load of service computing data, and obtaining an optimal computing power resource scheduling strategy by using a firefly algorithm according to the fitness function; and dynamically scheduling the computing power resources on the power chip according to the optimal computing power resource scheduling strategy, and allocating the computing power resources to different computing tasks. According to the method, the calculation power resource of the power chip is optimized and dispatched by adopting the firefly algorithm, so that the resource utilization rate is improved, and the adaptability and reliability of the power chip in a complex power grid environment are enhanced.

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 discloses a hybrid swarm intelligence algorithm-based photovoltaic parameter modeling method, which relates to the technical field of photovoltaic parameter modeling, and is characterized in that a Latin hypercube design is utilized to determine a sampling range of feature weights, n feature weight vectors are generated, a parameter space is effectively covered, the quality and diversity of population initialization are improved, and the modeling efficiency is improved. And the global exploration capability of the algorithm is enhanced. Through dynamic judgment of a real-time photovoltaic power prediction value and fitness, whether an error range of a current model is acceptable or not is quickly evaluated, so that unnecessary iterative calculation time is reduced. And an adaptive optimization mechanism: in a local optimization stage, dynamically updating particle positions by using a firefly algorithm, and cooperatively improving optimization precision and search efficiency in combination with a balance index fitted by a balance prediction model, so that the stability and convergence speed of the algorithm in the optimization process are ensured. And through comparative analysis of the balance index and the evaluation threshold, a relatively optimal particle position is selected.

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 an oil well working condition fault early warning method and system, and relates to the technical field of oil well control. According to the technical key points, the method comprises the following steps: firstly, extracting a 10-dimensional feature vector of oil well production data, and identifying an oil well which may have a fault through a fault early warning model based on improved fuzzy C-means clustering by using the 10-dimensional feature vector, according to the improved fuzzy C-means clustering algorithm, by purposefully selecting an initial clustering center and improving a target function, the defect that an FCM clustering algorithm is prone to falling into local optimum is overcome, and meanwhile the separation between the clustering centers is ensured; furthermore, a global search method-firefly algorithm is added on the basis, a calculation method of the clustering center is optimized, and a global optimal solution can be effectively found. The oil well fault can be timely and accurately found and treated in time, manual inspection is not needed, the working efficiency is improved, and management and supervision are facilitated.

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 a temperature and strain bimodal flexible sensor and a preparation method thereof, and the sensor achieves the efficient detection of temperature and strain signals through the combination of an ink direct writing process and a screen printing process. The sensor is unique in design and comprises a rigid area substrate and a soft area substrate which are respectively provided with a thermal resistance type temperature sensor and a piezoresistive strain sensor, so that effective separation and accurate measurement of temperature and strain signals are ensured. A flexible material and an improved FA-BP neural network algorithm based on a firefly algorithm are adopted, so that the stability and the measurement precision in a variable environment are improved. In addition, the sensor can still work normally in a high-temperature environment, and is superior to a common flexible thin film material in the market. The sensor disclosed by the invention shows huge application potential in the fields of intelligent wearable equipment, health monitoring, industrial automation and the like, and a new solution is provided for realizing high-precision and high-stability temperature and strain monitoring.

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.