37 results about "Specific population" patented technology

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.

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.

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 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.

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 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.

The embodiment of the invention provides a data optimization method and device, equipment and a storage medium. The method comprises the steps that an initial population data set is obtained, the initial population data set comprises at least one piece of individual data, and the individual data comprises at least one design parameter; performing reverse learning processing on the initial population data set to obtain a first population data set; performing differential evolution processing and cross processing on the first population data set to obtain a second population data set; and performing non-dominated sorting processing and clustering processing on the second population data set to obtain a target population data set which is used for performing multi-target optimization. Through multi-working-condition constraint processing and multi-mode solution set maintenance, the output target population data set can meet design requirements under different working conditions, diversified candidate schemes are provided for engineers, the design period is shortened, and the comprehensive performance of the suspension system is improved.

The invention discloses a two-stage three-dimensional boxing method based on a double-population strategy, and provides a two-stage boxing optimization strategy, a hybrid heuristic algorithm is used as a first stage, and a splitting and replacing strategy with smaller calculated amount is used as a second stage of the algorithm. Meanwhile, an improved angle occupying method is used as a heuristic boxing algorithm, and a hybrid heuristic algorithm is improved by adopting a double-population strategy, a goods pre-combination strategy and an improved simulated annealing algorithm dynamic temperature rise strategy; the strategies of the second stage further comprise a four-corner tightening strategy, a recombination strategy and a large object sinking strategy, the calculation amount can be remarkably reduced under the condition that the quality of solutions is improved, and the diversity of populations is guaranteed. The boxing method provided by the invention has a wide prospect in the field of logistics.

The application discloses a population probability construction method for an arbitrary still mode shared car relocation problem, and belongs to the technical field of computer application. Each generation of population is generated by using a probability construction algorithm PCA under the premise of a given virtual employee combination and a virtual cost matrix. The PCA algorithm mainly utilizes an adaptive probability selection function APF, a re-pickup strategy and three neighborhood search operators to construct employee paths one by one, thereby generating a feasible solution (i.e. a population individual) of the problem. The virtual employee combination and the virtual cost matrix are continuously updated in the population iteration process, and the evolution direction of the population is controlled by means of a linear function and relevant parameters. The population probability construction algorithm can effectively solve the arbitrary still mode shared car relocation problem, and can solve a solution with a smaller total cost, with an average saving of 5.84%.

To provide a device and method capable of constructing a model that has high prediction accuracy and is suitable for a specific group from a small number of DNA methylation data items obtained from the specific group while reducing the number of samples required for model construction.SOLUTION: A biological age measurement device 1 according to the present invention measures the biological age on the basis of the DNA methylation level. By using, as an explanatory variable matrix (n×p matrix (p≤q)), first explanatory variable information based on DNA methylation information in CpG sites at q points included in DNA of each sample of a first population composed of n samples, and using, as an objective variable vector (n-dimensional), first objective variable information based on biological age information of the n samples, the biological age measurement device 1 performs transfer learning on the basis of a biological age measurement model estimated by a second population different from the first population, and outputs the learning result as a trained biological age measurement model.SELECTED DRAWING: Figure 2

The present application relates to the technical field of detection experiment, and particularly relates to a pipeline experiment automatic scheduling method and system and related equipment, the method comprising: randomly generating a full permutation array as an initial population; taking the initial population as the parent generation, performing a crossover operation to determine a first offspring; pairing the individuals in the initial population two by two to determine paired individuals, and taking the paired individuals as the parent generation, performing a mutation operation to determine a second offspring; determining a target population based on the first offspring, the second offspring and the parent generation; detecting according to the detection order corresponding to each individual in the target population to determine a first total detection time corresponding to each individual; taking the individuals in each first total detection time that meet a first preset requirement as target individuals, and detecting each detection experiment according to the detection order corresponding to the target individuals. The present application uses a genetic algorithm to schedule and arrange tasks, thereby optimizing detection efficiency.

The invention provides a computer-implemented method for decision support in selecting crossings for population development, the method comprising: receiving a set of candidate parents, a set of traits, and one or more constraints comprising a resource constraint representative of a maximum number of crossings and / or a maximum progeny population size; carrying out a multi-objective optimization; evaluating a pareto front of the set of solutions and selecting a subset of one or more solutions within the set of solutions based on a result of evaluating the pareto front; selecting a solution of the subset of solutions; and providing the list of crossings of the selected solution and a predicted required number of progenies for each of the crossings comprised in the list of crossings.

The invention discloses a multi-modal multi-objective problem optimization method based on a balanced environment selection component. The method comprises the following steps: 1, constructing a multi-modal multi-objective problem optimization system based on the balanced environment selection component; 2, a population initialization module carries out population initialization to obtain a main initial population and an auxiliary initial population; 3, a population updating module combines the main initial population and the auxiliary initial population, and then generates a main filial generation population and an auxiliary filial generation population; combining to obtain a main updating population and an auxiliary updating population; 4, a primary selection module performs screening from the main update population and the auxiliary update population to obtain a main elite population and an auxiliary elite population; 5, a reselection module selects an optimal main population and an optimal auxiliary population from the main elite population and the auxiliary elite population; and 6, repeating the steps 3-5, and when the maximum number T of iterations is reached, outputting the optimal main population obtained by the last iteration as a final main population. The method has the advantages that diversity, convergence and feasibility of understanding are effectively balanced.

Embodiments of the present application disclose a kind of test paper generation method, device, computer readable storage medium and equipment, method includes: obtaining first population and determining fitness function and optimization parameter based on the first constraint condition related to group paper;According to fitness function, obtain the fitness value corresponding to each of multiple individuals in first population, determine the first individual, the second individual and the third individual in the top three of fitness value order;Based on optimization parameter, update the rest of individuals in first population using the first individual, the second individual and the third individual, determine second population;Determine the fitness variance value of second population, based on fitness variance value, the second population is processed by variation, determine new first population;In the case where new first population meets first preset condition, generate target test paper based on target individual in new first population.The test paper of the present application is good in universality, strong in expandability, high in efficiency, and avoids local optimum.

The application relates to a multi-agent robust control method and device driven by population iteration optimization, and belongs to the technical field of multi-agent control. The method comprises the following steps: initializing existing disturbance strategies in a disturbance strategy population pool and a target strategy to be trained; sampling a parent disturbance strategy set from the disturbance strategy population pool; performing sequential state disturbance to obtain a current round of disturbance state and collect strategy optimization sample data; updating the target strategy based on the current round of disturbance state; generating a new state disturbance strategy based on the strategy optimization sample data, taking the minimization of the cumulative income of the target multi-agent as an optimization target, and combining a population diversity regular constraint optimization; calculating a strategy distance and updating the disturbance strategy population pool; and repeating iteration until a training termination condition is met, so that a trained robust multi-agent strategy is obtained. The application effectively improves the robustness of the target strategy and ensures stable operation of the target strategy in a disturbance environment.

The application discloses a material supply method and system, wherein the method comprises the following steps: acquiring scene parameters and generating an initial population; calculating a first fitness value of the initial population; performing pretreatment on the initial population to obtain a first population and calculating a second fitness value of the first population; combining the initial population and the first population to obtain a second population, performing screening on the second population to obtain a third population; performing neighborhood search on feasible solutions of the third population to obtain a fourth population and calculating a third fitness value of the fourth population; combining the third population and the fourth population to obtain a fifth population, performing screening on the fifth population to obtain a sixth population, and calculating a fourth fitness value of the sixth population; acquiring a current iteration number, repeating the above steps until the current iteration number is equal to a preset iteration number if the current iteration number is less than the preset iteration number; selecting a minimum value from all the fitness values, acquiring pareto front data corresponding to the minimum value, and obtaining a target supply scheme.