15 results about "Slime mold" patented technology

The invention relates to a hydroelectric generating set controller parameter optimization method based on a mucus algorithm and an orthogonal test. The method comprises the following steps: dividing working conditions based on the orthogonal test; obtaining water turbine modeling data to form a data set, and constructing a water turbine neural network model; based on an Octave tool, a water turbine adjusting system comprehensive simulation model containing the water turbine neural network model is built; constructing a comprehensive objective function combined with a myxobacteria optimization algorithm; and optimizing the PID parameters of the hydroelectric generating set by combining a myxobacteria optimization algorithm, the comprehensive simulation model of the water turbine regulating system and the comprehensive objective function. The method for optimizing the parameters of the hydroelectric generating set controller based on the myxomycete algorithm and the orthogonal test overcomes the defects of a traditional optimization algorithm, has the advantages of being high in global search capability, high in convergence speed and high in adaptability, improves the efficiency of searching an optimal solution, and improves the operation performance and stability of the hydroelectric generating set.

The application discloses a semiconductor temperature control system control method based on a slime mold algorithm. The method comprises the following steps: identifying a differential equation of a semiconductor temperature control system by using a least square method to obtain a parameterized transfer function; performing excitation test, inputting a signal into the semiconductor temperature control system, and outputting a temperature change value; inputting an error value into a PID controller to control the semiconductor temperature control system; optimizing and iterating control parameters by using the slime mold algorithm, annealing and mutating the optimization result of the slime mold algorithm, and replacing a global optimal solution with a new solution generated by annealing and mutation until the fitness value of the fitness function of the PID controller response is the global optimal fitness, and then optimizing the PID control parameters of the PID controller. The application improves the response speed, stability and control precision of the semiconductor temperature control system, reduces overshoot and shortens the regulation time of the control system, and has good engineering application value.

The present application discloses a method and system for optimizing the parameters of a flutter signal based on an improved slime mold optimization algorithm, which relates to the technical field of optimizing control signals for electro-hydraulic servo systems. It includes superimposing a flutter signal on the control signal output by the PID controller of the electro-hydraulic servo system to determine the frequency and amplitude of the optimized flutter signal; constructing a fitness function according to the parameter variables and the average hysteresis width of the system, determining the frequency and amplitude ranges of the flutter signal according to the specification attribute information of the relevant equipment of the system, constructing the overshoot constraint condition of the system and the upper and lower limit constraint conditions of the parameter variables, and based on the fitness function, the overshoot constraint condition and the upper and lower limit constraint conditions, and an improved slime mold optimization algorithm, constructing a flutter signal parameter optimization model, inputting the interval corresponding to the parameter variables into the flutter signal parameter optimization model, and obtaining the optimized values of the parameter variables. The method can effectively reduce the hysteresis area and hysteresis width of the system and improve the control accuracy of the system.

The invention discloses a raw material component content measuring method, a raw material component content measuring system and a dendrobium extraction method, and relates to the field of component content measurement. According to the raw material component content measuring method, raw material storage environment influence factor data and raw material component content data detection frequency are considered, and the raw material component content is measured by adjusting the influence factor data and the detection frequency; according to the method, the deterioration rate of the raw materials can be controlled to be optimal after the raw materials are stored, and the high quality of the raw materials supplied in the subsequent extraction process is ensured, so that the quality and the drug effect of an extract are ensured; a current storage raw material component content influence factor initial data set and a current storage component content initial detection frequency data set are subjected to multiple times of iterative adjustment by adopting a myxomycete optimization algorithm, and the deterioration rate at the end of storage of the corresponding raw materials is used as a fitness function; therefore, along with iteration, the deterioration rate of the raw materials at the end of storage becomes smaller and smaller, and the requirements of subsequent extraction are finally met.

This paper presents a maximum power point tracking (MPPT) method for photovoltaic (PV) cells based on RBFNN and slime mold optimization algorithms, encompassing the fields of PV system control and precision measurement. A multi-level intelligent optimization mechanism is constructed to adapt to complex dynamic lighting conditions. The system distinguishes between sudden and stable lighting conditions through a dynamic lighting filtering mechanism, achieving precise control across different modes. At the moment of sudden lighting changes, a radial basis function neural network is invoked to globally predict the maximum power point, shortening the optimization time. To address temperature drift deviation in sensorless conditions, SMA is introduced for local optimization, locking the maximum power point while ensuring smooth power output through small-radius random exploration. In the steady-state phase, an adaptive step-size perturbation observation method is employed to adapt to stable lighting scenarios. During sudden lighting changes, the optimization time is shortened through the RBFNN's drop-in and the slime mold algorithm's local optimization, resulting in high tracking accuracy, excellent noise resistance and robustness, making it suitable for high-performance PV operation and maintenance and precise cell performance testing.

The invention relates to the technical field of genomics and microbial science, in particular to a myxobacteria T2T genome assembling method. The assembling method mainly comprises the four steps of global assembling, telomere sequence screening, local sequence clustering screening and second-generation data multi-round correction. According to the method provided by the invention, pollution removal strategies of telomere sequence screening and local sequence clustering screening are newly added, so that the problem that tandem repeat sequences in a myxobacteria genome are complex and short is effectively solved, the analysis of a complex region becomes possible, the integrity and continuity of assembly are improved, and the assembly efficiency is improved. The method does not need fingerprint spectrum, Hi-C technology or optical spectrum sequencing technology, reduces the experiment cost, ensures that all the sequences belonging to myxobacteria are extracted, effectively removes the interference of repetitive sequences and deep pronucleus pollution, enables highly similar repetitive sequence fragments to be reduced and positioned to an accurate genome position, and improves the accuracy of the detection result. The integrity and continuity of the genome are obviously improved.

The application discloses a waiting time constraint batch processing pipeline crane scheduling method based on an improved slime algorithm and belongs to the field of intelligent scheduling of industrial production. The method comprises the following steps: a scheduling model is constructed, the waiting time fuzzy interval is defined by a trapezoidal fuzzy number, and the maximum completion time is minimized as the target; the waiting time uncertainty is accurately characterized by the fuzzy interval, and the fluctuation range of the waiting time in actual production is completely covered; based on the model, a hybrid improved slime algorithm is used for solving; a matrix coding and One-Hot mapping are adopted to adapt to the discrete scheduling scene; a good point set initialization method is introduced to construct an initial population, and multiple scheduling schemes are obtained; and a double tabu table strategy is combined to optimize the local search efficiency and convergence stability. The application is superior to traditional genetic algorithms, discrete slime algorithms and other comparative algorithms in terms of solution quality, convergence speed and robustness, can efficiently adapt to the crane scheduling demand of process-type industries, and provides a stable and reliable solution for multi-crane collaborative scheduling in a complex uncertain environment.

The present invention relates to the technical field of magnetic flux sensors, and discloses an automated design method for an excitation coil of a sleeve-type magnetic flux sensor. The excitation coil in the sleeve-type magnetic flux sensor is designed using an improved slime mold algorithm. The iterative process of the improved slime mold algorithm comprises the following steps: S1 sorting; S2 updating weights; S3 updating positions. In step S3, after each update of the second-dimensional data of the slime mold, that is, after updating the number of excitation coil layers m, the number of excitation coil layers is immediately rounded off; after each update of the third-dimensional data of the slime mold, that is, after updating the diameter Φ of the excitation coil copper wire, the upper and lower limits of the current I are immediately modified to [0, 1.5πΦ 2 *10 6 The present invention rapidly generates excitation coil design parameters for steel cables of different diameters. The excitation coil designed according to this method ensures that the cable is saturated and uniformly magnetized while using as little copper wire material as possible, thereby reducing sensor costs.

Embodiments of the present application relate to the cross field of computer vision, bionic computing and industrial automation detection, and disclose a railway wagon wheel set front cover bolt coordinate extraction method and system. The present application constructs a nutrition potential field by fusing image multi-scale gradient and texture features, uses the chemotactic migration characteristics and "deposition-evaporation" competition mechanism of digital slime molds to guide the self-organization evolution of digital slime mold particles in the nutrition potential field, and generates pheromone pipe network that inhibits noise. Then, the sub-pixel coordinates are extracted by biomass weighted centroid. Finally, the topological verification is performed by using the rigid geometric prior of the three bolts of the wheel set front cover. In addition, the automatic completion of the occluded target is realized through a collaborative inference mechanism. Therefore, the present application has the advantages of strong noise resistance, high computing efficiency, good robustness and the like, and is suitable for industrial precision detection under complex working conditions.

This invention discloses a method for energy community electricity-carbon differential matching based on an improved slime mold algorithm, belonging to the field of smart grids. First, considering the coupling characteristics of energy hubs at the distribution network level, a data-driven method is used to quantitatively analyze the peak-valley coupling potential between different types of energy hubs. Initial differential matching is then performed on energy hubs with different external characteristics. Combining the load characteristics and carbon quota balance of energy hubs, a comprehensive index system that considers both regional structural and functional needs is constructed. Finally, aiming for optimal overall energy community characteristics, a matching strategy based on an improved slime mold optimization algorithm is proposed, introducing an adaptive search mechanism and information sharing strategy to optimize the structure and boundaries of the energy community. This invention simulates the dynamic matching process of energy communities, achieving differential matching that considers both electricity and carbon quotas, efficiently and accurately determining the boundaries and optimal matching schemes of energy communities, providing a new approach to improving the comprehensive benefits of electricity and carbon emission management.

The invention discloses a forklift attachment structural member stress analysis method based on ASA combined with PNN. The forklift attachment structural member stress analysis method comprises the steps that original stress data in a forklift attachment period are collected; decomposing the original stress data into a plurality of independent modes and residual modes; performing signal reconstruction to obtain a reconstructed signal; fusing the time domain features and the frequency domain features in the signals to obtain time domain and frequency domain combined features of the stress of the typical structural member of the attachment of the forklift truck; carrying out feature screening on the obtained time-frequency domain joint features of the stress based on an adaptive myxobacteria optimization algorithm ASA to obtain stress screening features; and taking the screened features as the input of a probabilistic neural network PNN model, constructing a stress analysis model of the typical structural member of the attachment of the forklift truck to analyze the stress data acquired in real time, and outputting an analysis result. According to the method, the model is constructed by collecting data, the stress characteristics of the forklift attachment structural member are effectively screened, and the requirement for online accurate analysis of the stress of the structural member can be met.

The invention relates to the technical field of motor optimization design, in particular to a motor optimization design method and system based on a multi-objective myxomycete algorithm, and the method comprises the steps: determining an optimization objective and a design variable; constructing a target function vector; iterative optimization is carried out based on an improved multi-target myxomycete algorithm, and the position of a myxomycete individual is updated based on a guiding individual and a self-adaptive exploration mode. According to the method, the elitist strategy is introduced and the elitist pool is updated on the basis of the traditional multi-target myxomycete algorithm, so that directional retention and utilization of the historical high-quality solution are realized, the loss of the high-quality solution in random search is avoided, a stable reference direction is provided for algorithm evolution, the optimization efficiency is improved, and the method is suitable for large-scale popularization and application. And the robustness of the optimization result and the engineering practicability are enhanced.

This invention provides a deep learning-based method for dynamically optimizing the metabolic feedback immune activity of fermented feed, comprising the following steps: S1: Collection of basic data and immune activity indicators of fermented feed; S2: Data preprocessing and feature engineering; S3: Construction of a deep learning-based immune activity prediction model; S4: Construction of a fusion framework driven by chaotic mapping—slime mold algorithm, symbiotic organism search, and fruit fly optimization algorithm; S5: Dynamic optimization of fermentation parameters based on the fusion algorithm; S6: Construction of a metabolic feedback mechanism and verification of optimization results. This invention overcomes the technical problems of inaccurate parameter control, limited optimization algorithms, lack of metabolic feedback, and insufficient immune activity prediction in existing fermented feed production.

The invention discloses a model modeling method and device, equipment, a storage medium and a program product, and the method comprises the steps: updating the position of a myxomycete individual through employing a preset myxomycete algorithm, and obtaining an updated myxomycete individual corresponding to the myxomycete individual; determining updated model parameters according to a mapping relation between the positions of the myxus individuals and the model parameters; under the condition that the model parameters of the model are updated model parameters, calculating an error value corresponding to the updated myxomycete individual by utilizing a prediction result of the model and the label data; according to the error value, adjusting a control parameter in a preset colistification algorithm to obtain an updated colistification algorithm; and taking the updated myxomycete algorithm as a preset myxomycete algorithm, returning to the step of updating the position of the myxomycete individual by adopting the preset myxomycete algorithm to obtain an updated myxomycete individual corresponding to the myxomycete individual until the returning frequency reaches a preset threshold value, and constructing the target model according to the model parameter corresponding to the minimum error value. The accuracy of a model prediction result can be improved.

The application provides a kind of electroplating production line intelligent scheduling method based on slime mold algorithm, comprising the following steps: S1: obtaining the product processing demand data of multiple products;S2: using slime mold algorithm to calculate the product processing demand data, obtain the scheduling sequence;S3: according to the request time of each station calculated based on the scheduling sequence, send the carrying request until the product carrying is completed and returns to step S1.The application uses slime mold algorithm to optimize the processing sequence of different process products, which can improve the production efficiency of electroplating production line;The travel of products and carrying vehicles is considered in advance, accurate scheduling can be realized, and in theory, the production products can be immediately transported after processing at each station, and the actual processing time deviation from the set processing time is zero;Only the processing sequence of products is scheduled, so orders that need to be processed urgently can be inserted in the normal production process.