9 results about "Slime mold" patented technology

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.

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.

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 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 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.