63 results about "Specific population" patented technology

The invention provides a regulation and control simulation method for a population ecosystem, and the method comprises the steps: obtaining historical data of the population ecosystem, and constructing an initial space population model based on the historical data; calculating a Sobol index of each variable in the initial space population model, and screening out a key regulation variable from the initial space population model based on the Sobol index of each variable; and obtaining observation data of the population ecosystem, dynamically adjusting the key regulation and control variables based on the observation data to obtain a target space population model, and realizing simulation of the population ecosystem. By adopting the method, accurate regulation and control and simulation of the population ecosystem are realized, and the accuracy of regulation and control simulation of the population ecosystem is improved.

This invention provides a fine-grained population identification and counting method based on deep learning and population characteristics. The method includes: obtaining a density distribution map representing the location and quantity information of the target of interest from a population image to be identified using a preset density estimation algorithm; performing grayscale processing on the density distribution map to generate a density grayscale map, and performing clustering and localization processing on the density grayscale map according to population distribution characteristics using a preset population clustering and localization algorithm to obtain the population's location boundary information; using the population's location boundary information, using a trained fine-grained population identification model to identify the population image to be identified to obtain the population's category information; and using the population's category information, performing classification and counting using the population's location boundary information and the density distribution map to obtain the quantity information of different populations. This invention also provides a method, electronic device, and storage medium for training the fine-grained population identification model.

This invention discloses an intelligent analysis method and storage medium for multi-population FATA axial spondyloarthritis based on adaptive dual-strategy pool optimization, comprising the following execution steps: Step 1, data acquisition; Step 2, data preprocessing; Step 3, parameter initialization and population; Step 4, construction of FATA update strategy pool and binary conversion strategy pool; Step 5, adaptive strategy iterative optimization; Step 6, outputting the optimal feature subset and using it for classifier diagnosis. This invention effectively expands the search space of high-dimensional heterogeneous axSpA data, improves the efficiency of obtaining the globally optimal feature subset, and reduces data dimensionality and computational complexity by using dual-strategy pool collaborative adaptation and multi-population parallel exploration, combined with an adaptive strategy learning mechanism, thus providing more accurate auxiliary diagnostic support for clinical practice.

The application discloses a passive time difference positioning method based on linear elimination weight and population center, mainly solves the problems that the artificial honeybird algorithm has resource waste and a large number of repetitive behaviors when solving the time difference positioning problem, thereby reducing the population richness, easily falling into local convergence and affecting the positioning accuracy. The implementation scheme is as follows: firstly, on the basis of elimination and update of the artificial honeybird algorithm, the current elimination weight is calculated by using an elimination weight calculation formula, and then the candidate solution is updated by using an elimination update equation. Secondly, based on the population center, the distribution state of the current population is judged by calculating the historical step length of each individual and the distance from the population center, when the population is in a redundant state, the population center is used to replace the invalid individual, and the problem that the positioning efficiency is not high due to a large number of invalid behaviors in the late iteration of the prior art is overcome.

The present application belongs to the technical field of feed formula research, and more particularly relates to a sow nutrition feed formula screening method based on an optimization algorithm. By analyzing population initialization distribution, a Bernoulli mapping sequence is introduced to initialize the population, so that the population distribution is more uniform, and the search ability of the algorithm in the early stage is improved. An improved adaptive nonlinear convergence factor is introduced to balance and enhance the global search ability and local development ability of the algorithm. A neighborhood disturbance optimization mechanism is added to generate new neighborhood solutions in the iteration process to prevent the algorithm from easily falling into a local optimal solution in the iteration, especially in the later stage. Finally, by comparing the optimization of sow feed formula before and after the improvement of ChOA, the effectiveness of the BANChOA algorithm is further verified. Through the analysis of the improvement strategy of the present application, it can be seen that the improved algorithm requires a large number of parameters and the optimization performance of the algorithm is greatly affected by the neighborhood disturbance optimization. There is still room for improvement in population initialization and convergence factor.

The present disclosure discloses a method for reconstructing the working face of coal mine based on search space pruning and multi-population dynamic adjustment, which involves the technical field of working face reconstruction of coal mine under incomplete projection conditions, comprising: based on prior knowledge of the working face reconstruction model structure, an initial grid partitioning of the exploration area is performed. Subsequently, multi-scale grid partitioning is achieved through search space pruning, guided by the sum of ray intercepts within each grid unit; to meet the requirements of population diversity and rapid convergence in the multi-population genetic algorithm, a multi-scale reconstruction objective function is constructed based on the partitioned multi-scale grids, this objective function is then solved using a dynamic multi-population genetic algorithm. Therefore, the above-mentioned method for reconstructing the working face of coal mine based on search space pruning and multi-population dynamic adjustment may dynamically adjust and optimize the number of populations, to ensure that the diversity of populations may be maintained, and the search efficiency may be improved, thereby improving the stability and convergence speed of the working face reconstruction operation.

The application discloses a kind of population pre-screening and space reduction proxy model aided optimization method, it is related to model optimization field, first, the extremely low computing cost advantage of proxy model is used to pre-screen population individual, then based on the individual characteristics after pre-screening, optimization space is reduced.Subsequently, subsequent operation is based on the individual selected in advance and reduced optimization space, to realize the goal of fast optimization convergence.On the other hand, in the optimization process, the method makes full use of the acceleration auxiliary role of proxy model in engine overall performance simulation, reduces the evaluation cost of objective function, further reduces optimization calculation amount.This not only improves optimization efficiency, but also guarantees the accuracy of original overall performance simulation model.

This invention provides a method, device, storage medium, and program product for issuing early warnings to specific populations affected by meteorological disasters. The method includes: acquiring regional meteorological data and communication device information of a region to be processed; generating meteorological disaster information based on the regional meteorological data, and determining an early warning method for the region to be processed based on the communication device information; determining a meteorological disaster early warning based on the meteorological disaster information and the early warning method; and sending the meteorological disaster early warning to target devices corresponding to the region to be processed. The target devices include both located and unlocated communication devices in the region to be processed. This invention achieves precise delivery of regionally related meteorological disaster information to different populations in different regions by sending corresponding meteorological disaster early warnings to communication devices corresponding to the region to be processed.

The application discloses a social network key node identification method based on double population interaction, which comprises the following steps: 1, double population construction, based on the original network space and the reduced search space, the main population and the auxiliary population are generated respectively to complete initialization; 2, double population evolution, the main population and the auxiliary population obtained in step 1 are respectively subjected to genetic operation, offspring is generated through crossover and mutation, and a new population is formed according to the corresponding population updating method; 3, double population interaction, when the double population evolution reaches a certain iteration number and meets the interaction condition, the influence strategy and the expansion strategy are alternately executed to realize the information interaction of the main population and the auxiliary population; 4, individual selection, the individuals in the main population and the auxiliary population are sorted based on the target function value, and the optimal individual is selected as the key node combination output. The application can quickly and effectively identify the key node combination in a large-scale social network, and provide a high-quality selection scheme for decision makers within a reasonable time.

The invention provides a specific population urban park satisfaction evaluation and influence factor identification method and system based on social media big data. According to the method, through social media data collection, specific crowd accurate screening, multi-dimensional text analysis, emotion value calculation and interpretable machine learning analysis, scientific evaluation of satisfaction and quantitative presentation of influence factors are realized, and reliable technical support is provided for fine design and quality improvement of urban parks.

The invention discloses a cascade reservoir scheduling method based on multi-population self-adaption, which comprises the following steps: establishing a cascade reservoir scheduling (CRS) model, taking maximized power generation, maximized desilting and maximized ecological rate as optimization targets of the CRS model, and determining constraint conditions according to the optimization targets of the CRS model and operation requirements of a cascade reservoir system; and then performing operation on the optimization target by using a cascade reservoir scheduling method based on multi-population self-adaption. According to the invention, by designing the single-target population and the double-target population, the conflict difficulty between target optimization and constraint satisfaction is reduced; waste of computing resources caused by low-efficiency populations is reduced through a population self-adaptive activation mechanism; by designing an environment selection mechanism based on bidirectional information sharing, the effectiveness of knowledge migration is improved, and the calculation complexity is reduced; the method is high in robustness and can be suitable for reservoir data in different years.

The application relates to the technical field of robot autonomous navigation, in particular to a cellular genetic path planning method based on multi-population cooperation adaptability, which comprises that one cell corresponds to one grid in a grid map; the number of sub-populations is set according to a path planning task; a plurality of initial path individuals of the sub-populations are generated through cell evolution; in each sub-population, a path individual with the maximum gene coding fitness is extracted, and the rest path individuals are randomly paired, the cell state matrix is adjusted based on a crossover and mutation rule, and iterative evolution is carried out; the individual with the maximum fitness and the individual obtained through evolution are used as sub-population representative individuals; the cooperation fitness is calculated according to the sub-population representative individuals of each sub-population; the population is updated according to the cooperation fitness ranking; an optimal solution is output; the application considers the population cooperation relationship as an optimization condition, and evolution of a cellular automaton is utilized, so that the best path in a multi-task complex scene can be quickly generated.

This invention specifically relates to a dynamic multi-objective optimization method, apparatus, device, and medium based on population co-evolution, comprising: acquiring the objective function and constraints of a target radio network; generating three populations based on the objective function and constraints, including a primary population, a first auxiliary population, and a second auxiliary population; detecting the environment of the target radio network and obtaining the detection results; when the detection results indicate that the environment is static, optimizing the three populations using multi-population co-evolution (MPCE); when the detection results indicate that the environment is changing, updating the three populations using an adaptive combined response mechanism (AIRS), and then optimizing the three populations using MPCE; and obtaining the optimization result after optimizing the three populations for a preset number of rounds. This invention improves the communication efficiency of radio networks and reduces communication overhead.

The application belongs to the technical field of medical informatics, and discloses a population automatic subdivision method based on a population pharmacokinetic model, which extracts individual core pharmacokinetic parameter full posterior distribution and statistical characteristics, combines clinical data to construct a fusion feature space and complete clinical-oriented feature screening, and then builds a Bayesian non-parametric pre-clustering algorithm incorporating clinical stratification constraints to guide the clustering process to fit the clinical diagnosis and treatment logic, while eliminating ambiguous samples and verifying clinical rationality, so that the population subdivision is supported by data and pharmacokinetic theory, potential pharmacokinetic difference subgroups are found, and the clustering results have clear biological and clinical significance, improving the subdivision objectivity and rigor; a specific population pharmacokinetic model is constructed for each pre-clustering subgroup, subgroup merging and splitting are completed through likelihood ratio test, and the subgroup boundary and model parameters are corrected in reverse in combination with clinical feedback.

The invention provides a self-adaptive knowledge migration multi-task optimization method based on a multi-population evolution framework. The method comprises the following steps: 1) initializing a population of each task; 2) selecting self evolution or migration evolution according to the migration probability to generate a filial generation, and using an enhanced migration strategy to improve the migration effect when an individual selects migration evolution; 3) generating a next-generation population through environment selection, combining a filial generation generated by migration evolution and a filial generation generated by self evolution, comparing the combined filial generation with a parent individual, selecting individuals with higher fitness to form the next-generation population, eliminating disadvantageous individuals through continuous iteration, and retaining a solution with better performance; 4) adaptively updating the migration probability; 5) reducing the size of the linear population, performing judgment after evolution of each generation of population is finished, and performing population reduction when conditions are met; and 6) when the maximum number of evaluation times is reached, outputting an optimal solution by the population. According to the method, the problem of continuous single-target multi-task optimization can be solved, and the core problem of a single-target multi-task optimization algorithm is improved.

This invention discloses an intelligent optimization method for energy management based on a swarm algorithm, comprising the following steps: S1, collecting multi-source time-series data and adjustable equipment parameters from the energy management system to generate a prediction sequence, constituting a scheduling space; S2, calculating a risk index sequence to form a risk map; S3, constructing a mapping structure between scheduling schemes and gap fields; S4, constructing a coupled coding structure between population individuals and gap fields to form an initial population cluster; S5, forming an updated population cluster using an improved HHO algorithm; S6, performing selection, replication, and elimination operations to form a new generation population cluster; S7, performing convergence determination, updating the risk map and prediction sequence, and completing the optimization closed loop. This invention enables risk perception, dynamic adjustment, and global optimization of complex energy systems across multiple time scales, improving the economy, energy efficiency, and operational safety of the energy management process.

The invention relates to the technical field of optimal scheduling, and discloses a material transfer optimal scheduling method and system based on a large language model. The method comprises the steps of initializing a population, constructing cue words, calling a large language model to generate a filial generation, detecting validity, evaluating and selecting fitness, carrying out linkage adjustment based on a temperature parameter and a population structure, and outputting a current optimal solution. The system corresponds to the method. According to the invention, through deep fusion of the large language model and the material transfer optimization scheduling, the production workshop material transfer efficiency is significantly improved.

The invention provides an intelligent scheduling method and device for coal feeder verification, electronic equipment and a medium, and the method comprises the steps: obtaining coal feeder parameters, worker parameters and constraint conditions, and carrying out the coding to obtain a scheduling sequence; calculating deviation values of the chromosomes based on a preset fitness function, sorting the deviation values, and dividing the chromosomes into sub-populations according to a sorting result; respectively selecting one chromosome from the two sub-populations for pairing; performing gene crossover on a pair of paired chromosomes to form a third sub-population, and performing mutation operation on part of chromosomes in the third sub-population; mixing the three sub-populations to construct a new population; calculating the deviation value of each chromosome in the new population, and selecting the first N chromosomes with the minimum deviation value in the new population to construct a new population; and repeating the steps until a preset number of iterations is reached, and obtaining a final scheduling result. According to the invention, the problems that manual scheduling cannot be flexibly changed and the scheduling efficiency is low can be solved.

The invention relates to a multi-population co-evolution multi-target energy scheduling decision algorithm, and relates to the technical field of power system automation, and the multi-population co-evolution multi-target energy scheduling decision algorithm comprises the following steps: S1, dividing an energy system structure into a plurality of sub-populations; s2, obtaining chromosome codes of a plurality of sub-populations, and establishing an objective function to evaluate the fitness of the corresponding sub-populations; s3, iterating the sub-populations periodically to form a new generation of sub-populations, obtaining excellent individuals with high fitness in the new generation of sub-populations, and performing excellent individual exchange among different sub-populations; s4, a multi-target time-varying weight dynamic function is constructed, multi-target balance optimization is achieved by dynamically adjusting the weight, and the multi-target time-varying weight dynamic function is F = lambda (t) * C + mu (t) * R + gamma (t) * E; the problem that a traditional single population evolutionary algorithm is prone to falling into local optimum is solved, the probability of finding a global optimal solution is improved, and therefore comprehensive optimization of multiple energy scheduling targets is achieved.

The application discloses a coal mine working face reconstruction method based on search space pruning and multi-population dynamic adjustment, relates to the technical field of coal mine working face reconstruction under incomplete projection conditions, and comprises prior knowledge based on a working face reconstruction model structure, preliminary grid division of a working face exploration area, and multi-scale grid division through search space pruning according to the sum of ray intercepts in each network; on the basis of considering the population diversity and fast convergence requirements of a multi-population genetic algorithm, a multi-scale reconstruction objective function is constructed based on the divided multi-scale grid, and is solved through a dynamic multi-population genetic algorithm. Therefore, the coal mine working face reconstruction method based on search space pruning and multi-population dynamic adjustment can dynamically adjust and optimize the number of populations, so that the diversity of the population can be maintained, the search efficiency is improved, and the stability and convergence speed of working face reconstruction operation are improved.

The invention discloses an intelligent ore blending method based on a CEALA algorithm. According to the invention, the co-evolution ALA algorithm is applied to an intelligent ore blending scene. The population scale is dynamically adjusted according to the population evolution condition in the optimization process through a cyclic population attenuation mechanism, the calculation cost is reduced, and convergence is accelerated while the population diversity is guaranteed. Meanwhile, the algorithm provides a co-evolution mechanism, based on a self-adaptive exploration mechanism and a self-adaptive development mechanism, individuals are dynamically allocated to different evolution mechanisms, the influence of parameter setting on the optimization performance of the algorithm is reduced, exploration and development are balanced, population diversity is increased, and local optimum is avoided. A parameter adaptive mechanism is introduced into the algorithm to capture beneficial information in a population iteration process, so that the influence of parameter setting on the optimization performance of the algorithm is reduced, and the robustness of the algorithm is enhanced. And finally, the algorithm is based on external archiving, beneficial information of abandoned solutions is mined, and the optimization performance of the algorithm is improved.

The invention relates to a supply chain configuration optimization method and system based on a multi-source driven population genetic algorithm, and the method comprises the steps: dividing a production cycle formed by a plurality of orders into a plurality of scheduling units, and each scheduling unit comprises a plurality of orders; three populations are initialized, each population comprises a plurality of individuals, and each individual is obtained by randomly selecting and configuring each order of one scheduling unit; carrying out fitness evaluation on each individual of the three populations according to respective fitness functions; respectively carrying out selection operation, repairable crossover operation and mutation operation on the three populations to obtain a new main population; performing fitness evaluation on each individual in the new main population again; and according to the new fitness evaluation result, judging whether an iteration termination condition is reached or not, if so, optimizing all scheduling units, and if not, circularly executing the improved genetic algorithm to reach the iteration termination condition. According to the invention, the supply chain can be effectively configured and optimized.

This invention provides a multi-objective optimization device based on dynamic elite learning and dynamic environment selection, comprising: an input module for user input of m objective functions and n decision variable bounding range functions constructed according to a specified multi-objective optimization task; an initialization module for constructing and initializing a population P consisting of N individuals, and setting the iteration count G to 1; a population storage module for storing the population P; a counting storage module for storing the iteration count G and a pre-set maximum number of iterations; and a population iteration module for iteratively updating the population P based on the iteration count G, the maximum number of iterations, the decision variable bounding range functions, and the objective functions. F The output module is used to display the population P to the user. F As a preferred solution for multi-objective optimization tasks, this method can more quickly and accurately obtain the preferred solution for a given multi-objective task.

This invention discloses a multi-objective recommendation method based on multi-task evolutionary optimization, comprising the following steps: auxiliary task generation, which simplifies the original task at two different levels of users and items to obtain auxiliary tasks; population initialization of the original and auxiliary tasks; population evolution and interaction, wherein a novel knowledge transfer mechanism is proposed for the auxiliary and original tasks to effectively realize knowledge transfer between tasks; and environment selection. This invention not only overcomes the limitation of traditional recommendation algorithms that only focus on accuracy, achieving a balance between accuracy and inaccuracy metrics and providing higher-quality recommendation list combinations for users to choose from, but also accelerates the convergence speed of the original task through simple auxiliary tasks, thus optimizing the time aspect.

The application discloses a kind of based on double population multi-constraint evolution UAV group path planning method, including determining first population and second population according to UAV path;First combination population and second combination population are obtained after generating offspring based on first population and second population;The number of feasible solutions in first combination population and second combination population is determined in turn, and is updated in combination with contribution value threshold, the path of UAV group is planned, and whether to reach termination condition is judged, if no, continue iteration is returned, if yes, non-dominated solution is exported according to updated first combination population and second combination population.This application is based on population contribution degree, and feasible solution mode is obtained by adaptively selecting suitable constraint calculation mode, can significantly improve solving efficiency and solution quality, and simultaneously enhance the stability and adaptability of algorithm.

An exemplary embodiment of the present disclosure provides a method of identifying one or more components in a biological material, the method comprising: providing a biological specimen, the biological specimen comprising one or more components; imaging at least a portion of the biological specimen to generate data indicative of a topography of the at least a portion of the biological specimen; and determining, using a machine learning algorithm, based at least in part on the data indicative of a topography of the at least a portion of the biological specimen, an identity of the one or more components of the biological specimen.

An exemplary embodiment of the present disclosure provides a method of identifying one or more components in a biological material, the method comprising: providing a biological specimen, the biological specimen comprising one or more components; imaging at least a portion of the biological specimen to generate data indicative of a topography of the at least a portion of the biological specimen; and determining, using a machine learning algorithm, based at least in part on the data indicative of a topography of the at least a portion of the biological specimen, an identity of the one or more components of the biological specimen.

The invention discloses a hydraulic transition process optimization method based on tracking population dominant double-population two stages, which comprises the following steps: S1, constructing a hydraulic transition process calculation model, and accessing the hydraulic transition process calculation model into a double-population optimization algorithm; s2, initializing DPTPEA parameters, and randomly generating a development population and a tracking population; s3, performing first-stage double-population differential screening and stability verification; s4, in the second stage, through a dynamic cooperation strategy, a boundary point direction sampling strategy and dynamic environment selection cooperation, double populations are circularly updated; and S5, generating an optimization scheme of the hydraulic transition process. According to the method, a dynamic cooperation strategy, boundary point direction sampling and a dynamic environment selection mechanism are introduced, infeasible solution information is fused, population diversity is enhanced, search intensity is adjusted in a self-adaptive mode, multi-target constraint optimization efficiency is improved, local optimum and computing resource waste are avoided, and optimized valve pump operation parameters and system schemes are clarified; the collaborative efficient scheme is high in feasibility, effectively avoids safety risks, and reduces energy consumption and operation and maintenance cost.

The application discloses a hydraulic transient process optimization method based on a tracking population dominant two-stage double population, comprising the following steps: S1, constructing a hydraulic transient process calculation model and connecting the double population optimization algorithm; S2, initializing DPTPEA parameters and randomly generating a development population and a tracking population; S3, first-stage double population differentiation screening and stability checking; S4, in the second stage, through dynamic cooperation strategy, boundary point direction sampling strategy and dynamic environment selection cooperation, the double population is cyclically updated; and S5, generating an optimization scheme of the hydraulic transient process. Through the introduction of the dynamic cooperation strategy, the boundary point direction sampling and the dynamic environment selection mechanism, the application fuses the infeasible solution information, enhances the population diversity and adaptively adjusts the search intensity, improves the multi-objective constraint optimization efficiency, avoids the local optimum and the waste of computing resources, and clearly shows the optimized valve pump operation parameters and the system scheme; the synergistic efficient scheme has high feasibility, effectively avoids the safety risk, reduces the energy consumption and the operation and maintenance cost.