22 results about "Nectar source" patented technology

The invention discloses a public security event emergency resource allocation method based on multi-agent collaborative optimization. The method comprises the following steps: S1, forming a three-dimensional state tensor field; s2, initializing an event agent, a resource agent and a scheduling agent based on the three-dimensional state tensor field, constructing a resource scheduling solution space, configuring an evolution hybrid perception model for each agent, and outputting a scheduling potential map; s3, path search based on target driving is carried out in the resource scheduling solution space, honey source position selection is carried out based on the scheduling potential map through a bee-bird search algorithm, and a resource matching path is generated; s4, establishing a bidirectional coupling mechanism between the hummingbird search algorithm and the evolution hybrid perception model, and realizing dynamic collaborative optimization; and S5, outputting a final resource scheduling scheme and executing the final resource scheduling scheme in real time by each agent to form a scheduling decision closed loop of the emergency response of the public security event. According to the method, the multi-agent evolution hybrid perception model and the hummingbird search algorithm are fused, and public security event resource scheduling optimization is realized.

The invention discloses a multi-rotating-speed flexible job shop scheduling method applied to double-resource constraint, and the method comprises the steps: taking the maximum completion time and minimum total energy consumption of a minimum machine as target functions, and constructing a flexible job shop scheduling model considering the rotating speed energy consumption of the machine and the production demands of a fine process; a machine speed gear constraint, a fine process constraint, a process sequence constraint, a completion time constraint, a machine processing constraint and a worker operation constraint are established as constraint conditions of the model; the flexible job shop scheduling problem is solved by adopting an improved artificial bee colony algorithm, bee colony search guided by excellent genes is adopted in bee learning operation in the improved artificial bee colony algorithm, and nectar source optimization is carried out based on the searched excellent genes; the following bee operation adopts a neighborhood structure which considers machine speed change and balances the working time of workers to carry out dynamic neighborhood search so as to optimize a nectar source. The effectiveness of the improved strategy is verified through experiments, and the superiority is verified through comparison of different algorithms on expansion standard examples.

The invention relates to the technical field of interplanting, in particular to a method for interplanting hemerocallis fulva under a nectariferous forest, which comprises the following steps: planning a forest stand light transmission plotting and planting zone; preparing soil layered improvement and base fertilizer; grading and planting hemerocallis fulva seedlings; erecting a dynamic shading system; drip irrigation belts are buried synchronously; synergistically pruning the nectariferous plants; root system isolation barrier construction; and dynamically monitoring and regulating the growth period. According to the method, the planting zone is precisely planned through canopy density surveying and mapping, and the optimal light environment is created for day lily; the layered soil improvement technology specifically meets the requirements of root systems with different depths; the adjustable sunshade net and the drip irrigation system form a light-temperature-water-fertilizer coordinated regulation and control network; the root prevention barrier is combined with the acid isolation belt to thoroughly block interspecific competition; accurate intervention in the whole growth period is achieved based on dynamic management of real-time data. The whole process forms a monitoring, execution and feedback closed loop, and the survival rate, the ornamental quality and the economic output of hemerocallis fulva are remarkably improved on the premise that the ecological function of the nectar forest is guaranteed.

The invention relates to the technical field of production scheduling, in particular to a hybrid flow shop self-learning scheduling method considering machine deterioration, and aims to solve the problem of hybrid flow shop scheduling considering machine deterioration, a self-learning artificial bee colony algorithm is adopted to optimize the maximum completion time. In the hired bee stage, populations are divided into P1, P2 and P3 groups according to fitness to realize diversified learning, in the bee observation stage, the food source selection probability is improved based on knowledge, multi-neighborhood search is combined, the investigation bee stage is improved, and a nectar source diversity enhancement strategy is added. The SLABC is superior to PPSOGA, DWSA and other algorithms under different workpiece numbers, stage numbers and deterioration rates, the influence of machine deterioration on the construction period can be effectively handled, the scheduling performance is remarkably improved, and the actual production requirements are met.

The invention relates to the technical field of spaceflight launching, and provides a swarm-type spaceflight launching system and method, and the system comprises a honeycomb system, a nectar source system, and a bee passage system. The honeycomb system is a technical area for spaceflight launching, is connected with the nectar source system through the bee passage system and is used for completing a satellite-rocket combination; the plurality of nectar source systems are arranged around the honeycomb system, and each nectar source system comprises a plurality of emission positions; the bee passage system is connected with the transmitting position and the honeycomb system and comprises a plurality of traffic roads. According to the scheme, a large number of satellites with different orbits can be launched in a short time, and the satellite launching efficiency is improved.

The invention provides a path optimization method for cleaning an unmanned aerial vehicle swarm and application. The method comprises the steps that position initialization is conducted on multiple unmanned aerial vehicles in a task area, a fitness function is set, and a variable Trials is recorded; the stages of employed bees, observation bees and reconnaissance bees are executed in sequence through an artificial bee colony algorithm, and path searching, probability selection, boundary processing and position updating are carried out; a reverse learning strategy, disturbance control frequency and a global factor are introduced into track initialization to improve search performance, and meanwhile, a nectar source regeneration mechanism in a scout bee stage is set. Through dynamic task allocation optimization, global collaborative path planning and a distributed elastic control architecture, the cleaning resource utilization rate, the operation efficiency and the energy consumption balance are improved, the fault-tolerant capability of the system is enhanced, and the cleaning requirement of the complex building facade is met.

The invention relates to the technical field of detection and evaluation, in particular to a method for rapidly detecting and dynamically evaluating the honey secreting amount of a nectariferous plant by utilizing near infrared spectroscopic analysis, which comprises the following steps of: determining a flowering stage of a target nectariferous plant flower; collecting spectral data of the target nectariferous plant flowers in the current flowering phase by using a near infrared spectrometer; inputting the spectral data into a stage specific honey secretion amount analysis model corresponding to the current flowering stage, and outputting a honey secretion amount predicted value of the current flowering stage; in a target flowering phase, obtaining a time sequence formed by a plurality of honey secretion amount predicted values; and generating a flowering period honey secretion dynamic curve of the target honey source plant flowers according to the time sequence. According to the method, deep coupling of the model structure and the plant physiological rhythm is achieved, the prediction result is made to better conform to the actual honey secretion mechanism, higher generalization ability is achieved in different stages, and particularly, the problem of misjudgment or sudden precision drop of a traditional model can be effectively solved in the non-peak period.

The invention relates to an unmanned aerial vehicle cluster tea picking method based on bee colony intelligence, and belongs to the field of intelligent agriculture. The method comprises the following steps: setting a reconnaissance unmanned aerial vehicle, introducing a pheromone diffusion model to identify an excellent nectar source, proposing a growth quality map construction method based on a pheromone model and Kriging interpolation, and constructing a time-space continuous tea tree growth quality map in a rasterized tea garden. The specific income is obtained based on a nectar source value function and a quadtree subdivided dynamic income function, the picking risk is calculated through a link quality confidence function, and then task allocation of the picking unmanned aerial vehicle is refined through a multi-layer game-energy coupling model. After tasks are allocated, the picking unmanned aerial vehicle performs short-time parallel simulation in a shared digital twin environment, performs flight path planning based on a hybrid cluster artificial potential field path planning method, and finally controls the picking unmanned aerial vehicle to accurately arrive at an operation point position through a double-layer control model, so that efficient and intelligent tea picking of the unmanned aerial vehicle cluster in a complex tea garden environment is realized.

The invention relates to the technical field of intelligent optimization algorithms, in particular to an optimization method based on an improved artificial bee colony algorithm. According to the technical scheme, the method comprises the following steps that a bee colony is initialized, positions containing nectar sources are generated randomly, each nectar source represents a solution, and the fitness value of each nectar source is evaluated; elite nectar source screening: selecting the first 10% nectar source with the optimal fitness value from the bee colony to form an elite group; and global optimal guide initialization: randomly selecting a nectar source from the elite group as a current global optimal solution. According to the optimization method based on the improved artificial bee colony algorithm, the three core performance dimensions of convergence speed, solving precision and robustness are comprehensively improved, and the technical problems of premature convergence, low convergence speed, poor parameter adaptability and the like of a traditional artificial bee colony algorithm are effectively solved.

This invention discloses an improved near-infrared spectral wavelength selection and modeling method for artificial bee colony algorithms, comprising the following steps: data preparation, chaotic initialization, fitness evaluation, establishing a PLS model based on wavelength subsets, using the regularized fitness function of RMSECV as the fitness Fitnessxi of nectar source xi; searching for foraging bees; searching for observation bees; searching for scout bees; outputting the optimal wavelength subset Sbest and establishing the final PLS model. The beneficial effects of this invention are: the method effectively improves convergence speed and accuracy; chaotic initialization improves the quality of the initial population, avoiding the algorithm from prematurely falling into local extrema; the adaptive perturbation factor balances the algorithm's global search and local exploitation, improving the ability to find the optimal wavelength combination in high-dimensional discrete space, and the selected wavelength subset has stronger representativeness, resulting in a final PLS model with lower RMSEP (root mean square error of prediction) and better robustness.

The invention discloses a composite operation method for breeding bees under a eucalyptus forest, which specifically comprises the following steps: step 1, carrying out habitat evaluation and zoning planning under the eucalyptus forest, and carrying out ecological environment evaluation and zoning planning on the basis of site conditions, forest stand structures, vegetation composition under the forest and microclimate characteristics of the eucalyptus forest; the under-forest space is divided into a core culture area, a transition buffer area and an ecological conservation area, and corresponding culture units are configured according to the biological characteristics of different bees; the invention relates to the technical field of agriculture and forestry. According to the composite operation method for breeding the bees under the eucalyptus forest, through systematic habitat evaluation and zoning planning, multi-layer nectar source plant construction, multi-bee-species collaborative breeding, dynamic management and resource recycling, the problem that a traditional under-forest bee breeding mode is not matched with the special habitat of the eucalyptus forest is solved; the health level of a bee colony and the utilization efficiency of a natural nectar source are remarkably improved, and multi-element synergy of pollination, biological control and bee product production is realized.

The invention discloses an intelligent prediction method and system for the arrival time of a river ship, and belongs to the field of water transportation traffic, and the method comprises the steps: building an initial prediction information data set, and carrying out the initial feature selection; generating a new feature by multiplying any two features, calculating the information gain rate of the new feature, screening, and adding the screened new feature to a data set; normalizing the data set; constructing a ship arrival time prediction model; initializing parameters of an artificial bee colony method for optimization and determining a honey content function; obtaining a new nectar source through a self-adaptive neighborhood nectar source searching method; the method comprises the following steps: selecting a nectar source, mapping the generated nectar source into atoms in an atom search method, iteratively updating the positions of the atoms by calculating accelerations of different atoms, comparing the honey content of the atoms in the atom search method with the honey content of the nectar source in a self-adaptive neighborhood method, selecting a new nectar source, and outputting optimized parameters after round iteration is completed; and the whole-process real-time dynamic prediction of the arrival time of the river ship is realized.

The application discloses a kind of based on dynamic probability self-adapting butterfly algorithm's integer ambiguity search method, this method according to the data received by ground receiver obtains the float solution of ambiguity and covariance matrix and carries out decorrelation processing, then determines search space by GPS interferometer measurement attitude principle.It is placed into search space by a certain number of butterflies randomly, according to the position update of the difference of individual between scent concentration, obtain the integer array corresponding to butterfly position and target function value, the individual of minimum target function value is regarded as nectar source position, records nectar source position and target function value.After reaching the maximum iteration number, output optimal position, namely the integer array of minimum target function value, as the integer solution of integer ambiguity search, add decimal part, and the fixed solution of integer ambiguity can be obtained.This method improves search accuracy, while solving the convergence speed slow in the integer ambiguity search of prior art, and the shortcoming of being easy to fall into local minimum.

The invention relates to the technical field of motor optimization design, in particular to a motor performance and efficiency optimization method and system based on a bee colony algorithm, and the method comprises the following steps: determining an optimization target and a design variable; an improved bee colony algorithm module is constructed, and the following steps are executed: a nectar source is initialized; calculating the fitness; performing screening; a global optimal nectar source and an individual historical optimal nectar source; the employed bees calculate random disturbance of honey source position updating based on the global optimal honey source and the individual historical optimal honey source; constructing a motor optimization model, and calculating a nectar source optimization target value; and the improved bee colony algorithm module and the motor optimization model are subjected to collaborative iteration solution, and an optimal nectar source design variable combination is output. According to the method, disturbance memory and a global extreme value guiding mechanism are fused in an employed bee stage, so that historical search direction information can be reserved while the search process of a nectar source is accelerated, and the optimization capacity and the stability and convergence efficiency of the search process are improved.

1. The name of the design product: city common nectar source plant identification card. 2. The use of the design product: the design product is used for popular science, learning and entertainment. 3. The design points of the design product: the combination of shape, pattern and color. 4. The picture or photo that best indicates the design points: component 1 front view. 5. The design claimed contains color. 6. Omit component 1-27 left view, right view, top view, bottom view.

The present invention provides an artificial bee colony intelligent optimization method and system based on a biological regulation mechanism; inspired by the mechanism of regulating physiological indexes in biological systems, on the basis of the leading bees, observing bees, and scout bees in the traditional artificial bee colony algorithm, the optimization method designs a blue light guiding unit, a nectar source regulation unit, and an antenna orientation unit according to the biological regulation law. Among them, the blue light guiding unit acts in the observing bee stage and can enhance the ability of the algorithm to jump out of local optima; the antenna orientation unit acts in the scout bee stage and can, to a certain extent, accelerate the convergence speed of the algorithm; the nectar source regulation unit acts in the leading bee iteration stage and can reduce the execution time of the algorithm. Simulation experiments show that the algorithm of the present invention has higher optimization performance compared with other traditional algorithms, especially in improving the global optimization performance and the algorithm convergence speed, and has improved the operation effect.

The invention discloses an indigenous plant combination and matching method for ecological restoration of an exposed high and steep slope, and relates to the technical field of ecological restoration of high and steep slopes, and the indigenous plant combination and matching method comprises the following steps: step 1, investigating a target area; step 2, carrying out primary screening treatment on the indigenous plants, wherein the primary screening treatment comprises formulating an exclusion standard and a retention standard; 3, performing multi-dimensional index quantitative evaluation; 4, building a plant combination according to five rules according to an evaluation result; 5, verification and adjustment are carried out; 6, determining a final combination; and 7, constructing a succession model of the pioneer plant and the apical pole plant. The method has long-term stability: the screened indigenous plants are drought-resistant and barren-resistant, the survival rate of the restored indigenous plants in 20 years can still reach 70% or above, and the limitation that foreign plants are degraded in 3-5 years is broken through; ecological integrity: functional complementation (nitrogen fixation, nectar source and soil fixation) promotes soil improvement and ecological chain construction, and soil organic matters can be increased by 0.3%-0.5% after one year.

The invention relates to the technical field of wine brewing, in particular to acanthopanax honey fermented wine and a preparation method thereof. The fermented wine is prepared from the following raw materials in parts by weight: 20-40 parts of acanthopanax honey, 0.03-0.07 part of yeast, 0.03-0.07 part of yeast nutritive salt and 0.1-0.2 part of citric acid. The acanthopanax honey is applied to fermentation of the mead, the limitation of traditional raw materials is broken through, and the blank of a deep processing technology of the acanthopanax honey is filled; a single nectar source is used for retaining and strengthening natural fragrance, and quality deterioration and fragrance covering caused by addition of substances such as fruits are avoided; the preparation process is simple and efficient, original nutritional ingredients are reserved after fermentation, the health-care effect is remarkable, and the health-care requirement is met.

The invention discloses a bee-keeping intelligent optimization method and system with adaptive bee species and nectar sources, and aims to solve the problems of nectar source information lag, blindness in bee species selection, low efficiency in transition decision, extensive bee colony management and the like in traditional bee-keeping. According to the method, a honey source space-time database is constructed through fusion of remote sensing and ground survey, and the honey secretion peak period is predicted in combination with meteorological and phenological models; establishing a bee species characteristic knowledge base covering dimensions such as collection radius, flowering phase adaptability, nectar source preference and the like; a bee species-nectar source adaptation degree evaluation model is constructed based on machine learning, and multi-target intelligent recommendation is achieved; the integrated Internet of Things sensor monitors the swarm state in real time, and dynamically optimizes a transition path and a time window; and services such as a nectar source map, yield prediction and abnormity early warning are provided through a user visual platform. The system forms a'sensing-analysis-decision-execution-feedback 'closed loop, so that bee colony loss is reduced, invalid transition is reduced, and ecological sustainable bee keeping is promoted. The method is suitable for smart bee industry, digital agriculture and rural revitalizing application scenes.

This invention relates to the field of power transmission line technology, specifically to a distributed fault diagnosis method and system for power transmission lines. The method includes: first, acquiring voltage and current data and the actual arrival time of the fault traveling wave from multiple monitoring terminals, and calculating the reliability of information from each terminal; then, initializing an artificial bee colony, employing bees to search directionally according to the traveling wave propagation direction, and observing bees selecting nectar sources based on information reliability; next, calculating a stagnation index, dynamically combining the strategies of employed bees and scout bees to generate candidate locations; finally, calculating a fitness value by comprehensively considering the electrical quantity fitting degree and the traveling wave timing matching degree, and outputting the optimal candidate fault parameters. This invention improves the convergence speed through directional searching in the traveling wave propagation direction and significantly improves the accuracy and robustness of fault location by adaptively balancing local development and global exploration through the stagnation index.

The present invention discloses an intelligent prediction method and system for the arrival time of river transport ships, which belongs to the field of water transport. The method comprises: establishing an initial prediction information data set and performing initial feature selection; generating a new feature by multiplying any two features, calculating its information gain rate, screening it, and then adding it to the data set; normalizing the data set; constructing a ship arrival time prediction model; initializing parameters of an artificial bee colony method for optimization and determining a nectar content function; obtaining a new nectar source by an adaptive neighborhood search nectar source method; and mapping the generated nectar source to atoms in an atomic search method, iteratively updating the atomic position by calculating the acceleration of different atoms, and then comparing the nectar content of the atoms in the atomic search method with the nectar content of the nectar source in the adaptive neighborhood method, selecting a new nectar source until the optimized parameters are output after the round of iteration is completed; thus, real-time dynamic prediction of the entire process of the arrival time of river transport ships is realized.

The invention discloses a bee product traceability information generation method and system, and the method comprises the steps: firstly obtaining nectar source region image data, environment data, bee health data and bee colony behavior data, and then extracting the multi-dimensional feature vectors of the nectar source region image data, the environment data, the bee health data and the bee colony behavior data; coding each multi-dimensional feature vector into a coding feature, carrying out feature fusion to obtain a multi-dimensional fusion feature vector, and carrying out a multi-prediction task based on the multi-dimensional fusion feature vector to obtain bee product traceability information consisting of bee colony health state category information, nectar source quality grade information, abnormal condition category information and environmental suitability information; the system comprises a nectar source image acquisition module, an environment monitoring module, a bee health monitoring module, a swarm behavior monitoring module and a processor module, and is used for acquiring data and realizing the method process. The process of generating the bee product traceability information does not need manual participation, so that tampering is avoided, and the method has the advantage of high reliability.