52 results about "Firefly algorithm" patented technology

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 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 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 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.

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 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.

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 an electric agricultural machine operation scheduling method, and belongs to the technical field of intelligent agriculture and agricultural machine automatic scheduling. In order to solve the problems of difficult scheduling, high operation cost, low electric quantity utilization rate and unreasonable path planning caused by electric quantity limitation in multi-field continuous operation of electric agricultural machinery, the method comprises the following steps: constructing an electric agricultural machinery operation scheduling model which comprises a target function and constraint conditions, wherein the objective function is total scheduling cost minimization and electric quantity utilization rate maximization, and the constraint conditions comprise path constraint, electric quantity constraint, task allocation constraint and cost constraint; solving the scheduling model based on a firefly algorithm to obtain an optimal operation path and a scheduling scheme of the electric agricultural machinery; and outputting the optimal path and the corresponding target function value. According to the invention, various kinds of electric agricultural equipment in a large-scale farm can operate in a multi-field continuous operation scene.

The invention discloses a Bayesian network structure learning method based on an improved MMHC algorithm, and relates to the technical field of Bayesian networks, the MMPC algorithm obtains a data set, determines a father-child node set of each variable through a conditional independence test and correlation judgment, and obtains an undirected graph; converting the undirected graph into a directed graph; performing random operation on the directed graphs to obtain a series of directed graphs and an initial population of a firefly algorithm; the fitness value of each firefly in the population is calculated, and the positions of the fireflies are updated; performing local optimization operation on the fireflies after position updating; and after multiple iterations, selecting the firefly with the highest fitness value in the population as a final Bayesian network structure. The improvement measures of the invention help to enhance the overall performance of the algorithm.

The present application relates to the field of water pollution control and treatment, in particular to an ecological filter rural water purification system, which comprises a data acquisition module, an optimal state feedback module, a dynamic modeling module and an optimal control parameter output module. The data acquisition module collects water quality parameters through multiple sensors, generates water quality time series data through synchronization, anomaly identification and distortion data reconstruction, and simultaneously collects and standardizes structural parameters; the optimal state feedback module obtains relevant indexes based on the fuzzy PID algorithm, and then outputs them through firefly algorithm optimization; the dynamic modeling module respectively constructs ammonia nitrogen degradation models in the aerobic zone and COD degradation models in the anaerobic zone, correlates the two zones by hydraulic retention time, and incorporates environmental disturbance correction models; the optimal control parameter output module outputs control parameter intervals through a decision tree, and particle swarm optimization algorithm is used to search for optimal parameters in the intervals and output them; the system can efficiently purify rural water sources and provide precise control basis for the water purification process.

The invention discloses a spanning frame structure size optimization method and system based on a firefly algorithm, and relates to the technical field of electric power engineering construction, and the method comprises the steps: firstly building a parameterized finite element model, implementing normalized hierarchical loading, obtaining a tangent stiffness matrix evolved along with a load, calculating the modal participation degree of a component, and constructing a dynamic participation degree track; and according to the quantitative trajectory smoothing index, the modal dominance and the key component suppression index, constructing a brightness function with coupled rigidity and trajectory features. The firefly attraction degree is corrected by means of trajectory similarity based on modal energy fingerprints, and nonlinear search is executed through a characteristic resonance mechanism. The method solves the problem that traditional static optimization neglects geometric nonlinear evolution to cause modal abrupt change, and the dynamic stability and evolution robustness of the spanning frame in the full stress process are remarkably improved while the structural light weight is achieved.

The application discloses a kind of high elasticity power distribution network optimal load transfer method considering source load storage coordination, including, with the network topology structure of power distribution network and source load storage resource calling plan as the position vector of firefly, randomly initialize each firefly;For each firefly individual, based on the network topology structure and source load storage resource calling method corresponding to the individual position of power distribution network, load transfer is carried out, the objective function of model is obtained, and it is converted into firefly brightness;Judge whether optimal firefly brightness converges to set accuracy and meets the constraint requirement, if yes, output result as the final load transfer method of power distribution network, algorithm ends;If not converge but reaches the maximum iteration number, also end program;Otherwise return to the step of converting objective function into firefly brightness;The application solves power distribution network load transfer model using firefly algorithm, reduces solving time, and improves optimization performance.

The invention relates to an active-disturbance-rejection control method for a solid oxide battery, and belongs to the technical field of automatic control, and the method comprises the steps: randomly generating a plurality of target parameter combinations according to a candidate solution space of each target parameter of LADRC, and taking each target parameter combination as a firefly position, calculating the maximum brightness of the fireflies according to the positions of the fireflies; updating the firefly position of the target firefly with the highest brightness ranking, and then updating the firefly position of each firefly according to the inertia weight and the adaptive step length factor; then, control parameters of the SOC system are generated based on the firefly positions through LADRC, and when performance indexes of the SOC system running according to the control parameters meet index requirements, it is determined that LADRC parameter optimization is completed. According to the invention, the improved firefly algorithm is applied to the active-disturbance-rejection controller of the SOC system for parameter optimization, so that the anti-interference capability of the output voltage of the solid oxide battery is improved.

The application provides a design method of glowworm swarm optimization algorithm based on a ternary optical computer, relates to the field of glowworm swarm optimization algorithm, and improves the operation speed and communication mode of the algorithm by using the parallelism of the ternary optical computer, and different parallel strategies are used for the algorithm in different stages to reasonably allocate resources, wherein the inter-process communication between processors can also make the population individuals in different processors communicate with each other, and the mutation and cross fusion are performed on the basis of each other, the application is based on the characteristics of the ternary optical computer, such as the huge potential, parallelism and bit allocation, and the classical glowworm swarm optimization algorithm is improved, an improved glowworm swarm optimization algorithm based on the ternary optical computer is designed mainly by means of chaotic mapping randomization of the initial population position, introduction of adaptive step length, and balance of boundary adjustment, and the like, and the experimental results show that the improved glowworm swarm optimization algorithm based on the ternary optical computer has enhanced optimization ability and increased convergence speed.

This invention provides a method, apparatus, and system for monitoring the concentration of dissolved gases in transformer oil. The concentration monitoring method includes: obtaining initial model parameters of a gas detection model using the Firefly algorithm; training the gas detection model based on the initial model parameters and a sample training set to obtain the trained gas detection model; acquiring in real-time gas concentration feature values ​​of at least one gas in the transformer oil detected by a gas concentration feature value detection system; and applying the trained gas detection model to monitor the liquid-phase gas concentration of dissolved gases in the transformer oil in real-time based on the gas concentration feature values. This invention avoids cross-interference of dissolved gases in transformer oil and reduces the computational complexity of dissolving gas concentration in transformer oil.

This invention discloses a UAV trajectory planning method based on an improved firefly algorithm, comprising the following steps: S1, constructing an environment model and a threat model for UAV trajectory planning; S2, setting constraints and an objective function for UAV trajectory planning based on the environment model and the threat model; S3, optimizing the objective function using the improved firefly algorithm to obtain the UAV trajectory route. This invention models the environment of the UAV trajectory planning scenario, analyzes the static domain dynamic threat costs that the UAV may face, and then analyzes the remaining constraints and objective function. It optimizes the objective function using an improved firefly algorithm, adaptively adjusting the light intensity absorption coefficient and firefly position update of the firefly algorithm, and introducing a perturbation mechanism to expand the algorithm's search space, improving the global search capability and convergence efficiency of the firefly algorithm, and ensuring better global optimality of the generated path.

The application discloses a method and system for predicting deep hydrocarbon source rock organic matter enrichment law based on glowworm algorithm optimized GBDT, and belongs to the field of hydrocarbon source rock TOC prediction. Specifically, the method comprises the following steps: obtaining logging curve data; preprocessing the obtained logging curve data; based on the preprocessed data, establishing a machine learning model (the core lies in the hyperparameter optimization of the model, and the glowworm algorithm is particularly used in the optimization process); and using the established TOC prediction model to predict the total organic carbon content. The application combines machine learning and intelligent optimization algorithm and predicts the organic matter enrichment law of deep hydrocarbon source rock, which can greatly improve the prediction accuracy and precision, and reduce the exploration cost and risk.

The present application relates to a distributed photovoltaic MPPT method, system, device and medium, and relates to the technical field of distributed photovoltaic power generation, which comprises the following steps: preprocessing photovoltaic array data, obtaining irradiance, temperature and MPP voltage dataset; determining the optimal ElmanNN hidden layer node number by K-fold cross-validation and RMSE evaluation, and dividing the dataset into a training set, a validation set and a test set; inputting the training set into global search of the firefly algorithm and local optimization of the ant colony optimization algorithm, defining the stable solution as the FA-EAS optimal parameters after convergence; updating the ElmanNN weight threshold based on the optimal parameters, deploying the training model to perform MPP voltage online prediction; inputting the difference between the predicted voltage and the reference voltage into a PID controller, calculating the control amount through the incremental PID, generating a PWM signal to control the duty cycle of the Boost converter, and realizing maximum power point tracking. The present application can improve the tracking speed, stability and success rate of MPPT.

The invention discloses a power system frequency regulation and control method considering load aggregator demand response, and relates to the technical field of power system frequency regulation and control. Firstly, for a mode that load aggregators participate in power system frequency regulation and control, different characteristics of user loads of the load aggregators and the power unbalance amount of a power system are combined; constructing a load aggregator demand response model, and defining key parameter constraints such as an adjustment rate and response delay; the method comprises the following steps of: constructing a multi-dimensional regulation and control objective function including frequency deviation, power unbalance, load regulation rate, change rate and the like by taking realization of frequency stabilization of a power system as a core objective; and finally, solving the objective function based on a firefly algorithm, and obtaining a load aggregator demand response optimal scheduling scheme. The method can quickly respond to the frequency deviation of the power system, guarantees the smooth load adjustment process while guaranteeing the frequency stability and power balance of the system, reduces the impact on the system, and effectively improves the operation stability and reliability of the power system.

The application discloses a method for unmanned aerial vehicle path deception jamming based on an intelligent optimization algorithm, belongs to the technical field of unmanned aerial vehicle path deception jamming, and is used for unmanned aerial vehicle path deception jamming. The method comprises the following steps: establishing a dynamic equation and a kinematic equation of an unmanned aerial vehicle to obtain an expected flight trajectory of the unmanned aerial vehicle; introducing a particle filtering method based on a glowworm swarm optimization algorithm to track and estimate the state of the unmanned aerial vehicle; and introducing an improved particle swarm optimization algorithm to calculate a public time delay in a retransmission type jamming and to interfere with and guide the positioning information of the unmanned aerial vehicle. Compared with the prior art, the application simulates the real flight process and the maneuvering change of the unmanned aerial vehicle, reflects the attitude change and the dynamic response of the unmanned aerial vehicle, alleviates the particle degradation and sample impoverishment problems, improves the tracking stability and the precision under noisy observation, improves the precision of deception positioning and the system robustness, enhances the overall deception jamming effect, and provides technical support for improving the deception jamming capability of an end defense system.

This invention relates to a dual-modal flexible temperature-strain sensor and its fabrication method. The sensor achieves efficient detection of temperature and strain signals by combining direct ink writing and screen printing processes. The sensor features a unique design, comprising a rigid substrate and a flexible substrate, housing a thermal resistance temperature sensor and a piezoresistive strain sensor respectively, ensuring effective separation and accurate measurement of temperature and strain signals. The use of flexible materials and a FA-BP neural network algorithm based on an improved firefly algorithm enhances stability and measurement accuracy under varying environments. Furthermore, the sensor can still operate normally at high temperatures, outperforming common flexible thin-film materials on the market. This sensor demonstrates significant application potential in smart wearable devices, health monitoring, and industrial automation, providing a new solution for achieving high-precision and high-stability temperature and strain monitoring.

The application belongs to the field of personalized recommendation, and particularly discloses a personalized news recommendation method based on user behavior graph, which comprises the following steps: data collection, profile construction, content similarity index, neighbor user searching, news recommendation and hybrid optimization. The method can ensure that each extracted keyword can accurately reflect the core of the news theme by constructing a target corpus, performing fine preprocessing, using log-likelihood value for preliminary screening, combining revision index and clustering scatter diagram. In the user profile construction, a time decay factor is introduced, the cosine similarity is used to calculate the interest similarity, the Pearson correlation coefficient is used to eliminate the scoring benchmark difference, and the double indexes are used for accurate screening of neighbor users. The global search ability of the firefly algorithm and the local optimization advantage of the weighted crow search algorithm are combined to realize real-time tracking of user dynamic interest and accurate mining of potential interest users.

The application discloses a kind of based on snake hawk hybrid algorithm's self-disturbance rejection fault-tolerant control method and system, and relates to mechanical control technical field.The position of firefly is calculated formula is integrated into the attack prey formula of snake hawk algorithm;The formula of fitness function including the dynamic decision domain radius of two random candidate solutions of snake hawk hybrid algorithm is improved in firefly algorithm;The dynamic decision domain radius in improved firefly algorithm is integrated into the escape strategy of snake hawk algorithm, the global search capability of firefly algorithm in later period is strengthened, and the algorithm is avoided to fall into local optimum quickly.Snake hawk hybrid algorithm is used to optimize self-disturbance rejection fault-tolerant control method and Elman neuron network, and the best neuron network parameter is obtained, which is converted based on real-time operation parameter for calculation, to ensure that it can reliably and stably work.

The application discloses a torque signal denoising method based on an MFO-WPT algorithm, and belongs to the technical field of signal processing. The method comprises the following steps: obtaining a noisy torque signal; decomposing the noisy torque signal at different decomposition scales to obtain wavelet packet coefficients of each node of the noisy torque signal at different scales; obtaining a signal signal-to-noise ratio (SNR) after wavelet packet coefficient denoising, and taking the signal SNR as a fitness function of a firefly algorithm; optimizing the wavelet packet threshold value by using the fitness function and the firefly algorithm to find an optimal wavelet packet threshold value; constructing an optimal wavelet packet threshold function according to the optimal wavelet packet threshold value, wherein the optimal wavelet packet threshold function is an optimal wavelet packet hard threshold function or an optimal wavelet packet soft threshold function; obtaining estimated wavelet packet coefficients by using the wavelet packet coefficients and the optimal wavelet packet threshold function; and performing wavelet packet inverse transformation on the estimated wavelet packet coefficients to reconstruct the estimated wavelet packet coefficients into a denoised torque signal.

The application discloses a kind of based on multi-sensor data's side slope deformation monitoring and dynamic early warning method and system, it is related to side slope monitoring technical field, including: using pre-deployed multi-class sensor acquisition multi-source sensor data, according to sensor distribution and pre-processed multi-source sensor data constructs graph structure data, using graph convolution network is modeled, constructs side slope deformation monitoring model;Introduce clustering federal learning strategy to carry out joint training to the side slope deformation monitoring model of multiple sites, using the side slope deformation monitoring model trained carries out risk grade division;Extract the key influence factor of landslide disaster and introduce improved firefly algorithm dynamically optimizes early warning threshold, using the early warning threshold optimized and current risk level are distinguished, generate early warning information.The application improves the reliability of monitoring and the timeliness of early warning by multi-source data fusion and intelligent analysis, realizes the leap of side slope deformation monitoring from single point static to networked intelligence.

The invention discloses a frequency division wave impedance inversion method and device, electronic equipment and a medium. The method comprises the following steps: converting seismic data and logging data into the same domain, and acquiring a plurality of frequency division data volumes according to the seismic data; obtaining a wave impedance curve according to the logging data; establishing a sample data set according to the frequency division data volume and the wave impedance curve; obtaining a wave impedance inversion result of each frequency division data volume based on the sample data set; and obtaining a full-band seismic wave impedance inversion result based on a firefly algorithm according to the wave impedance inversion result. According to the method, on the basis of fully utilizing high, medium and low frequency information in an effective frequency band of seismic data, the relation between amplitude and frequency is introduced into a wave impedance inversion process, and full-band seismic wave impedance inversion is realized by taking a GRU neural network and a firefly algorithm as technical means.

The invention discloses a mobile robot positioning and mapping method based on UWB radar, and belongs to the technical field of mobile robot SLAM. According to the method, two UWB radars carried by an ROS system mobile robot platform are used for collecting environment original time domain data, and the target distance is solved through timestamp alignment, noise reduction, normalization, Savitzky-Golay filtering and self-adaptive peak detection in combination with odometer information; then visual range verification and polar coordinate conversion are completed through a triangulation positioning method, and polar coordinate parameters of the target relative to the robot are obtained; and finally, an RBPF-SLAM algorithm optimized by a firefly algorithm is adopted, pose estimation, scanning matching, particle weight updating and partition optimization, resampling and map updating are sequentially executed, and self-localization and environment map construction are achieved. A base station does not need to be arranged in advance, the limitation of a traditional odometer method is overcome, and the method is high in positioning precision, good in mapping effect, high in robustness and suitable for the autonomous navigation scene of the mobile robot in the unknown environment in the indoor environment and the like.

The application discloses a kind of based on voice recognition's broadcasting host auxiliary system, it is related to voice recognition technical field, including acquisition module, feature extraction module, adjusting module and optimization module, the present application extracts program series single semantic feature in broadcasting preparation stage, and constructs virtual potential energy field, program is mapped as gravitational centroid, and potential energy guide groove and timing repulsive field are constructed based on timing, when live, the acoustic characteristics of host's speech are extracted in real time, construct target function based on sparse representation to solve activation vector, dynamically adjust the gravitational depth and effective action radius of potential energy field, optimization is carried out in potential energy field using firefly algorithm, real-time monitoring group barycenter trajectory, combined with physical operation signal executes material preloading or play control, the present application converts intention recognition into physical field optimization, solves the static text and dynamic voice dimension mismatch problem, realizes continuous tracking to non-standard expression using swarm intelligence, ensures the coherence of live process.