40 results about "Population dynamics" patented technology

The invention provides a digital twinborn monitoring, regulating and controlling method for a copepod bait biological domestication process. The method comprises the following steps: establishing a micro-ecology and population dynamics coupled twinborn model, and defining a monitoring parameter and control object unified term system; collecting multi-source sensing data at an edge end, and performing clock synchronization, signal denoising and state estimation to obtain a micro-ecology, group and water body state joint estimation result; executing parameter online identification based on state estimation, and constructing a safety constraint set of ammonia nitrogen, dissolved oxygen, death rate, environment change rate and the like; the breeding efficiency improvement and the system stability balance are realized through a multi-objective optimization function; and generating a micro-ecological pulse and environment gradual change cooperative control action set according to a solving result, and when a set condition is met, outputting a domestication prescription and terminating the process. Intelligent, low-stress and high-stability regulation and control of a copepod domestication process are realized through digital twin modeling, rolling predictive control and a self-evolution updating mechanism.

The invention provides a key species population spatial distribution quantification method and system in a complex ecological system, and the method comprises the steps: carrying out the feature extraction of biological and environmental data collected in a target ecological region, and carrying out the fusion of the extracted multi-modal features, and generating a comprehensive feature vector; based on the target large model, analyzing the comprehensive feature vector, identifying species in the target ecological region, and obtaining species identification information of each species; based on the species identification information and the comprehensive feature vector corresponding to the target ecological region, quantifying a species population space distribution condition, and generating species population quantification information at least comprising a population distribution diagram, a population density diagram and a population dynamic change diagram; and based on the species population quantitative information and the collected original data, generating and displaying a quantitative report. According to the method, the species can be intelligently, efficiently and accurately identified based on the large model, the distribution condition and dynamic change of the species can be visually displayed, a quantitative report is provided, and a powerful basis is provided for ecological protection and management.

The invention discloses a Pelteobagrus vachelli population dynamic monitoring system and method, particularly relates to the technical field of fishery resource monitoring, and aims to solve the problems of inaccurate juvenile fish survival rate evaluation and poor monitoring timeliness in a benthic hidden environment in the prior art. The method comprises the following steps: arranging a benthic penetration type sonar array to synchronously collect a water body reflection signal and a benthic environment sample, and extracting juvenile fish specific gene fragments by combining an environment DNA (Deoxyribose Nucleic Acid) enrichment technology to generate concentration distribution data; screening juvenile fish distribution data by using an acoustic reflection signal and body length correlation model, and positioning an active hot spot area through space-time overlapping analysis; deploying a directional sound wave excitation device to obtain escape trajectory data, and calculating a survival rate in combination with a density attenuation model; finally, multi-source data and topographic features are fused to generate a real-time population dynamic monitoring result, and high-precision and non-intrusive monitoring of the juvenile fish population in the hidden environment is realized.

The invention provides a three-dimensional multi-dimensional oceanic nekton monitoring method based on artificial intelligence, and relates to the technical field of oceanic nekton monitoring, and the method comprises the steps: deploying a temperature-salinity-depth section plotter and an acoustic Doppler flow velocity section plotter, collecting data, configuring an environment parameter data set, obtaining a three-dimensional environment parameter data set, and obtaining a three-dimensional environment parameter data set; and a multi-dimensional environment parameter database is generated. Various ocean fishery resource investigation means are fused, an advanced artificial intelligence method is applied, three-dimensional multi-dimensional dynamic monitoring of the ocean swimming animals is achieved, on one hand, the number composition and space-time distribution of the ocean swimming animals, especially the vertical distribution characteristics can be monitored, the population dynamics of the ocean swimming animals and the relation between the population dynamics and environmental factors can be revealed, and on the other hand, the population dynamics of the ocean swimming animals can be monitored. By monitoring the swimming animals, basic data can be provided for population resource evaluation, and the method serves formulation of fishing quota of ocean economic swimming animals, delimitation of ocean protection areas and the like.

The invention belongs to the technical field of semi-active suspension control methods, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm, which comprises the following steps of: performing adaptive grid division on an external file of the MOPSO algorithm to determine global optimum with the most diversity and global optimum with the most convergence; judging the evolution state of the population according to the distribution entropy variation of all optimal solutions in an external file, and selecting a global optimal gBest for guiding evolution; according to the variable quantity of the local crowding distance of the current population evolution, adjusting flight parameters of population evolution to balance the evolution trend of the population; and fusing the two core mechanisms, providing a multi-information fusion multi-target particle swarm optimization algorithm, and applying the multi-information fusion multi-target particle swarm optimization algorithm to determination of a semi-active suspension LQR control strategy weight coefficient. According to the novel multi-objective optimization algorithm, external archive information and current population dynamic information are cooperatively integrated, so that balance between convergence precision and population diversity is effectively realized.

The invention relates to the technical field of geographic information systems and intelligent decisions, and discloses a public service facility site selection method and system, and the method comprises the steps: constructing a multi-modal urban population dynamic data collection and fusion module, a space-time dynamic population distribution and prediction module, a heterogeneous demand recognition and quantification module, and a multi-target intelligent site selection optimization module. According to the method and the system disclosed by the invention, through integration of multi-source heterogeneous city big data, construction of a refined space-time dynamic population distribution model, deep mining of heterogeneous service requirements of different crowds, and application of an advanced multi-target intelligent optimization algorithm, normal form transformation of public service facility site selection from static extensive to dynamic accurate is realized. According to the method and the system provided by the invention, the inherent contradiction of a traditional method in the aspects of dealing with population dynamics, demand differentiation and multi-dimensional complexity can be effectively solved, so that the scientificity, rationality, response capability and resource utilization efficiency of urban public service facility planning are remarkably improved.

The invention relates to a multi-factor collaborative analysis method based on vegetable bemisia tabaci population dynamic change, and belongs to the technical field of plant protection. The method comprises the following steps: constructing a gridding monitoring system by deploying a spectral imager and an Internet of Things sensor, and collecting the number of bemisia tabaci populations and multi-source factor data; an automatic data cleaning model is constructed based on a spectral imaging technology, abnormal data is eliminated through combination of a long-short-term memory neural network and anomaly detection, and missing data is filled based on a generative adversarial network; capturing a dynamic causal relationship through static association in combination with time sequence analysis to construct a causal graph model, and outputting a thermodynamic map; constructing a multi-factor collaborative model based on the fused spatio-temporal features, enhancing factor interaction, and quantifying factor uncertainty; and outputting a population dynamic change analysis report through visualizing population dynamic change based on the standardized total data. The intelligent and precise vegetable bemisia tabaci prevention and control work is realized.

To provide an analysis system and an analysis method capable of suitably analyzing movement in units of a plurality of persons.SOLUTION: An analysis system for analyzing population dynamics in a specific area includes an acquisition unit (controller 15) configured to acquire movement information (analysis data) on movement of each person in the specific area, and a processing unit (controller 15) configured to, when a plurality of persons who have moved are identified as a moving group belonging to the same group based on the movement information acquired by the acquisition unit, classify the identified moving group into one of a plurality of predetermined types of moving group classifications (moving group household classifications T) based on a combination pattern (relationship pattern P) of the plurality of persons.SELECTED DRAWING: Figure 2

The present application relates to a kind of village population hollowing fluctuation mode identification method and system, belong to geographic information technology field.The method includes: mobile phone location trajectory data pre-processing;Based on SMoT model, identify user daily residence, combine Chinese New Year holiday window to respectively statistics village population and Chinese New Year population;With population outflow rate, construct hollowing index, after natural breakpoint method grading coding, generate time series;Accordingly, divide annual non-hollow village, seasonal hollow village and annual hollow village;Finally, adopt Euclidean distance and K-means algorithm to cluster the last two kinds of villages, identify fluctuation mode.The present application solves the problem that existing technology is difficult to capture short cycle population dynamic change, realizes the accurate quantification and mode classification to the seasonal fluctuation characteristics of village population hollowing, provides scientific basis for the differentiated resource allocation of rural revitalization.

The invention discloses a Chikungunya thermal seasonal propagation risk assessment method and system based on a coupling model. The method comprises the following steps: acquiring basic data and preprocessing the basic data; model parameters are divided into a fixed type and a dynamic type, the dynamic parameters are combined with environmental data to calculate and assign values, historical epidemic situation data in the past three years are used as target values, key parameters are adjusted through a least square method, and the error between a model simulation epidemic situation curve and an actual observation value is minimized. Based on the influence of a standardized meteorological data quantification environment on mosquitoes, generating a day-by-day key parameter sequence and inputting the day-by-day key parameter sequence into a medium module; the age structure model is used for simulating population dynamics of mosquitoes at all stages, and the SEI model is used for simulating transmission of viruses in the mosquitoes. The number of infectious mosquitoes output by the medium module is used as a drive, crowd state conversion is simulated through an SEIR model, and the number of infected people every day and the morbidity are obtained; and risk level assessment of the assessment area is completed, and quantitative data support is provided for risk assessment.

The application discloses a rice borer peak period detection method based on intelligent forecasting lamp data, comprising the following steps: step 1, pest monitoring time series data acquisition and pretreatment; step 2, abnormal trapping data identification and repair based on the working condition characteristics of the insect trapping device; step 3, multi-peak Gaussian model construction; step 4, model adaptive optimal parameter selection and peak period detection; step 5, peak value merging and ecological generation division; step 6, peak period information extraction and prevention window output. The application utilizes the overall trend and peak value characteristics of the time series curve, can effectively reduce local interference, and improve the accuracy and stability of moth peak identification; and can simultaneously extract multiple moth peak characteristics of multiple sites and multiple years in a region, does not need to separately set a measurement standard for different scenes, and can be applied to large-scale Chilo suppressalis population dynamic monitoring.

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 present application relates to the analysis method of the population dynamics of wetland water birds and habitat quality. The method analyzes the population dynamics of birds based on the historical cumulative data of the target wetland and the bird data obtained by field investigation, and analyzes the habitat quality of key ecological environment indicator bird species based on the land use data of the location of the target wetland and the main ecological threat factors, so as to provide a scientific basis for the habitat protection and management of wetland birds.

The present invention discloses a system and method for monitoring the dynamics of yellow catfish populations in yellow catfish (Psoralea vachelli), which specifically relate to the technical field of fishery resource monitoring. The system and method are used to solve the problems of inaccurate juvenile survival rate assessment and poor monitoring timeliness in benthic hidden environments in the prior art. The system deploys a benthic penetrating sonar array to synchronously collect water body reflection signals and benthic environment samples, and uses environmental DNA enrichment technology to extract juvenile-specific gene fragments to generate concentration distribution data. The system uses an acoustic reflection signal and body length correlation model to screen juvenile distribution data, and locates activity hotspots through spatiotemporal overlap analysis. A directional acoustic wave excitation device is deployed to obtain escape trajectory data, and the survival rate is calculated using a density attenuation model. Finally, multi-source data and terrain features are integrated to generate real-time population dynamics monitoring results, thereby achieving high-precision, non-invasive monitoring of juvenile fish populations in hidden environments.

The application belongs to the technical field of semi-active suspension control method, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization algorithm, which comprises self-adaptive grid division of an external archive of the MOPSO algorithm to determine a global optimum with the most diversity and a global optimum with the most convergence; evolution state of a population is judged according to a distribution entropy change quantity of all optimal solutions in the external archive, and a global optimum gBest for guiding evolution is selected; flight parameters of population evolution are adjusted according to a change quantity of local crowding distance of current population evolution to balance evolution trend of the population; the above two types of core mechanisms are fused, a multi-information fusion multi-objective particle swarm optimization algorithm is proposed, and the algorithm is used for determination of weight coefficients of a semi-active suspension LQR control strategy. The novel multi-objective optimization algorithm effectively realizes balance between convergence precision and population diversity by synergistically integrating external archive information and current population dynamic information.

The invention provides a population dynamic monitoring method and system based on a real-time public information platform. The method comprises the following steps: acquiring people flow sequence data of a preset area through a real-time public information platform, analyzing and processing the people flow sequence data in different time periods to obtain population scale data and population flow data of the preset area, and performing data correction and accuracy evaluation to obtain population flow data of the preset area; analyzing and processing the people flow sequence data, the population scale data and the population flow data through a preset population dynamic monitoring model to obtain a population change index, and comparing and evaluating a population dynamic monitoring state through a threshold value; according to the method, the human traffic sequence data, the population scale data and the population flow data of the preset region in different scenes and different time dimensions are obtained by relying on the real-time public information platform, and data optimization and accuracy verification are performed, so that comprehensive, accurate and multi-dimensional monitoring of regional population dynamics is realized.

The application discloses a method and system for predicting casualties caused by earthquakes and landslides, taking into account population dynamic characteristics and environmental heterogeneity, which comprises the following steps: step one, collecting all environmental factor data and multi-source population distribution data in the target earthquake area according to a preset spatial unit; step two, inputting the collected environmental factor data into a preset logistic regression model to obtain an earthquake landslide probability; step three, processing the multi-source population distribution data by using a data fusion method to obtain a dynamic population distribution during an earthquake occurrence period; step four, processing the environmental factor data based on a random forest algorithm model and a SHAP method to obtain environmental heterogeneity parameters and environmental factor contribution degrees; and step five, obtaining a probability distribution of casualties caused by earthquake landslides according to a probability prediction model of casualties caused by earthquake landslides. The application improves the accuracy of the evaluation of casualties caused by earthquake landslides and provides a reliable basis for formulating an action plan for earthquake rescue.

This application provides a method and system for regional population dynamic monitoring and prediction based on spatiotemporal big data. The method includes: acquiring historical population characteristic data for a preset region, including monthly and annual historical population characteristic data; acquiring a preset prediction model set, filtering and processing it to obtain preset prediction model sets that meet prediction requirements, and marking them as a preset short-term prediction model set and a preset medium-to-long-term prediction model set, respectively; collecting real-time population characteristic data for the preset region; and combining it with the monthly and annual historical population characteristic data, processing it separately using the preset short-term and medium-to-long-term prediction model sets to obtain corresponding monthly and annual population prediction value sets; further processing to obtain the final monthly and annual population prediction values, thereby realizing the technology for regional population dynamic monitoring and prediction based on spatiotemporal big data.

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 present invention discloses a system and method for monitoring and warning the scale of phytoplankton biomass in water bodies. The system is an algae biomass scale measurement scheme based on an ecological mathematical model, including a sample collection module 100, a water quality type identification module 200, an initial model matching module 300, a target model calibration module 400, an algae biomass scale prediction module 500, and an algal bloom information processing module 600. Through the hierarchical classification technology logic of water quality type trigger response and matching model, the system has a phased leap response to high organic matter water quality, which has emergency management significance. By constructing a prediction model around population dynamics, placing the importance of organic matter content before nitrogen and phosphorus content, constructing a mathematical function for environmental factors classification, and using surface runoff variables to characterize the hydrodynamic characteristics of water bodies, the present invention saves data costs and computing overhead, and reduces the adaptability of the technical solution for promotion. The present invention also provides a system-based method for monitoring and warning the scale of phytoplankton biomass in water bodies.

The invention discloses an OpenClaw-based Chinese white dolphin intelligent supervision method and system. The method comprises the following steps: constructing a three-layer bidirectional semantic linkage protection knowledge system through an OpenClaw intelligent agent; performing space-time dimension alignment on the multi-source heterogeneous data, and generating cross-modal unified semantic representation based on double-constraint joint fusion; on the basis of cross-modal unified semantic representation, a federal multi-modal joint model is constructed through an OpenClaw agent, a cloud large model is obtained through joint training, and migration from the cloud large model to an edge small model is realized; on the basis of cross-modal unified semantic representation, fitting a noise intensity-behavior response reaction curve through an OpenClaw agent, determining a critical disturbance threshold value, and constructing a causal inference model of ship activity-white dolphin population dynamics in combination with a causal graph model; and constructing a multi-source heterogeneous data conflict resolution mechanism, performing two-dimensional consistency verification on a decision result output by the causal inference model, and completing semantic conflict resolution of the multi-source heterogeneous data.

An object of a system according to an embodiment is to efficiently formulate, evaluate, and review a location optimization plan for realizing sustainable development of a local government.SOLUTION: A system includes a GIS data collection part, a population dynamics analysis part, a location optimization plan generation part, and an evaluation review part. The GIS data collection unit collects GIS data. The demographic analysis unit analyzes the GIS data collected by the GIS data collection unit. A location optimization plan generation part generates a custom-made location optimization plan on the basis of the population dynamics and human flow information analyzed by the population dynamics analysis part. The evaluation review unit performs continuous evaluation of the location optimization plan and review of the plan as necessary.SELECTED DRAWING: Figure 1

The application provides a kind of three-dimensional multi-dimensional ocean swimming animal monitoring method based on artificial intelligence, relating to the technical field of ocean swimming animal monitoring, comprising: deploying a temperature-salinity-depth profiler and an acoustic Doppler current profiler, collecting data and configuring environmental parameter data sets, obtaining three-dimensional environmental parameter data sets, and generating a multi-dimensional environmental parameter database. By integrating various ocean fishery resource survey methods and using advanced artificial intelligence methods, three-dimensional multi-dimensional dynamic monitoring of ocean swimming animals is achieved. On the one hand, the number composition and spatio-temporal distribution of ocean swimming animals, especially the vertical distribution characteristics, can be monitored, revealing the relationship between population dynamics and environmental factors. On the other hand, through monitoring of swimming animals, basic data for population resource assessment can be provided, serving the formulation of ocean economic swimming animal catch quotas and the delineation of marine protected areas.

The invention discloses a rice stem borer peak period detection method based on intelligent forecasting lamp data. The method comprises the following steps: step 1, acquiring and preprocessing insect pest monitoring time sequence data; 2, identifying and repairing abnormal trapping data based on the working condition characteristics of the insect trapping device; step 3, constructing a multi-peak Gaussian model; step 4, model adaptive optimal parameter selection and peak period detection; step 5, peak value merging and ecological generation division; and step 6, peak period information extraction and prevention and control window output. According to the method, the overall trend and peak value characteristics of the time sequence curve are utilized, local interference can be effectively reduced, and the accuracy and stability of moth peak recognition are improved; and multiple moth peak characteristics of multiple sites and multiple years in the region can be synchronously extracted, measurement standards do not need to be independently set for different scenes, and the method can be applied to large-range chilo suppressalis population dynamic monitoring.

The present application provides a kind of digital twin monitoring and control method for domestication process of copepods bait organism, the method comprises: establishing micro-ecological, population dynamics coupled twin model, define monitoring parameter and control object uniform terminology system;Multi-source sensing data is collected in edge, clock synchronization, signal denoising and state estimation are carried out, and the joint estimation result of micro-ecological, population and water body state is obtained;Parameter online identification is executed based on state estimation, and safety constraint set such as ammonia nitrogen, dissolved oxygen, mortality and environmental change rate is constructed;The balance of reproduction efficiency improvement and system stability is realized by multi-objective optimization function;According to the solution result, the micro-ecological pulse, environmental gradual change cooperative control action set is generated, and when the set condition is satisfied, the domestication prescription is output and the process is terminated.Through digital twin modeling, rolling prediction control and self-evolution updating mechanism, intelligent, low stress and high stability control of copepod domestication process is realized.

The present application provides a kind of northward mangrove population dynamics, primary productivity and carbon flux evaluation system, the system includes: data acquisition unit, for the ecosystem of the mangrove planting area to be evaluated Data acquisition obtains original data set;Data analysis unit is used to analyze and identify original data set;Evaluation unit, with mangrove community and carbon sink dynamic evaluation model evaluates the community development and carbon sink dynamics after the northward mangrove and future change trend;Data storage unit is used to store original data and evaluation results.Model helps to understand and predict the health status and carbon sink function of coastal wetland mangrove system, can realize the comprehensive evaluation of community development and carbon sink capacity after the northward mangrove and the prediction of future trend, provide scientific basis and data support for accurate accounting of coastal wetland protection and repair after the northward mangrove and effective management of blue carbon resources.

Provide a system. 【Solution means】 As data collection means, a device that collects urban-related data from the existing systems of local governments, As data storage means, a device that stores the collected data in a database and performs data cleaning and shaping, As data analysis means, a device that provides analysis results of population dynamics and economic trends by using machine learning algorithms, As resident participation means, a device that accepts opinions and questionnaires from residents, As policy proposal means, a device that integrates analysis results and residents' opinions to generate policy proposals, As simulation means, a device that generates urban planning simulations by using VR or AR technology, Means for residents to evaluate policy proposals via VR or AR technology, Means for providing a user interface for residents to participate through smart devices, A system including the above.

The invention provides a flood avoidance path planning method based on population dynamic distribution and flood inundation risk, and relates to the technical field of flood control engineering. The population gathering area is determined by acquiring the position data of the mobile terminal in real time, the population short-time migration rule is analyzed, and the dominant direction of population migration is determined; historical road blocking events and real-time rainfall monitoring data are utilized, and an urban road drainage pipe network structure is combined to construct a road inundation evolution network for limiting a ponding propagation boundary; identifying path nodes with traffic conflict risks through spatial topology analysis, and determining a node avoidance sequence according to node connectivity stability characteristics; an ant colony search model fused with a local path direction guide mechanism is adopted to realize accurate planning of a flood avoidance evacuation path for avoiding a road flooding risk area, so that the instantaneity and the reliability of population evacuation path planning under a flood disaster scene are effectively improved.

The invention discloses an urban population dynamic distribution prediction method and system based on a built environment. The method comprises the following steps: firstly, constructing an initial index system containing built-up environment and social economic data; performing feature selection by using an XGBoost model subjected to hyper-parameter optimization, and screening out key influence factors; the core lies in that an improved MLP model is constructed and trained, and the model effectively models the spatial overflow effect of building environmental factors in adjacent areas by introducing a spatial receptive field and a feature fusion layer. And finally, inputting the key influence factors of the year to be predicted into the trained improved MLP model to realize dynamic and accurate prediction of the population scale of each space unit in the city. The method overcomes the defects that a traditional method neglects a multivariable interaction effect, spatial correlation and nonlinearity, significantly improves the precision and reliability of population distribution prediction, and provides a scientific basis for smart city planning and management.