57 results about "Geographic distribution" patented technology

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention relates to an artificial hybridization fry breeding method for epinephelus fuscoguttatus and pink pink bass, which belongs to the technical field of distant hybridization breeding and comprises the steps of parent selection of epinephelus fuscoguttatus and pink pink bass, parent breeding, parent intensive breeding, gamete collection, artificial insemination, hatching of fertilized eggs and fry breeding. By means of reproductive regulation and distant hybridization technologies, two species belonging to two genera under the grouper family with different geographical distribution and different breeding time are hybridized, namely cross-generic hybridization under the grouper family is achieved, intergeneric reproductive isolation is broken through, and the breeding efficiency is improved. Therefore, new hybridized germplasm with excellent characteristics of high fertilization rate, high hatching rate, high survival rate, high growth speed and the like is cultivated.

The invention discloses a marine organism intrusion risk prediction method and system, and relates to the technical field of marine environment, and the method comprises the steps: calculating the environmental suitability of each sea area through employing a maximum entropy model based on a marine environment factor data layer and invasive organism geographical distribution data; human factor data including shipping density and fishery fishing degree and biological factor data including primary productivity and species number are collected, and an intrusion risk assessment system is established in combination with environmental suitability. According to the method, the weight of each index under each data is determined through an objective weighting method, an invasion organism invasion risk function is constructed, the invasion risk is calculated, the sea area invasion risk is judged accordingly, reference is provided for related departments, monitoring, protection and other measures are taken in advance for medium and high risk areas, early warning is performed in advance, and the influence of biological invasion is reduced to the minimum.

The invention discloses a wave energy array development site selection method, which belongs to the field of wave energy development and utilization, and comprises the following steps of: 1) processing geographical distribution data by utilizing a GIS (Geographic Information System) spatial analysis method to obtain a limited development area graph layer; and 2) constructing an evaluation index system of wave energy array development site selection, and carrying out standardization processing to obtain a comprehensive evaluation result. And 3) calculating an offshore distance, and generating a refined developable range layer. And 4) carrying out wave direction statistical analysis in the developable range to obtain an array development range. And 5) on the basis of determining the array development range of the target sea area, determining a potential development site. 6, quantitative analysis and trend analysis of the array development capability are carried out with the potential development site as the center point, and the key development site is determined.According to the method, multi-dimensional evaluation indexes such as environment compatibility, resource endowment, technical feasibility and economic rationality are integrated, and a scientific basis is provided for array development site selection of the wave power station.

The invention relates to the field of image recognition, in particular to an intelligent seeding operation terminal platform based on visual perception, which performs dynamic evolution analysis on a farmland geographical image according to the soil moisture content of a farmland, calculates the soil moisture content characteristic of each seeding area, and calibrates each seeding area as a first type area or a second type area; carrying out soil moisture content airspace analysis according to the landform elements of the second-class region, and determining an effective seeding track of the second-class region; comparing the geographical distribution images of the first-class region and the second-class region with a visual perception image generated by the seeder, and determining whether the seeder enters the first-class region or the second-class region; and visually identifying the deviation between the actual seeding track and the effective seeding track in the second type of region so as to adjust the seed reseeding in the second type of region. Visual perception from a global range to a local range of a farmland can be performed to guide seeding operation, the seeding operation efficiency is improved, and the overall germination rate of the farmland is ensured.

The invention discloses an event-driven architecture-oriented spatial-temporal feature extraction method and system for multi-type fault power failure events, and belongs to the field of power system fault diagnosis. According to the method, historical fault data, equipment operation data and external environment data are collected through event monitoring triggering and multi-source data fusion; an XGBoost model based on probability distribution is adopted to carry out precise cleaning and complementing on the data, and a comprehensive full-scene fault sample library is constructed; and further, a time sequence convolutional network and a graph convolutional neural network are utilized to deeply mine and fuse spatio-temporal characteristics such as periodicity, high-incidence time periods, geographical distribution rules and spatial relevance of fault events from a sample library. According to the method, spatio-temporal evolution law analysis and key influence factor identification of fault events can be efficiently and intelligently realized, the accuracy and timeliness of power grid fault research and judgment are remarkably improved, a scientific basis is provided for fault positioning and emergency disposal, and construction of a novel power system is assisted.

The invention discloses a short-term power prediction method and system based on a wind power plant cluster. The method comprises the following steps: constructing a similarity distance matrix of a key meteorological element time-varying rule between different stations; calculating a Copula entropy between the historical power samples of each station, and constructing a spatial similarity matrix of station power according to each Copula entropy; performing normalization processing on the similarity distance matrix and the spatial similarity matrix, and summing the similarity distance matrix and the spatial similarity matrix after normalization processing to obtain a space-time similarity distance matrix; clustering the similarity distance matrix by adopting a hierarchical clustering algorithm, determining a partitioning scheme in combination with the geographic distribution of the new energy stations, and dividing each wind power plant in the target region into each region; and outputting a total power prediction result of the obtained region according to the LSTM time sequence prediction model. The overall precision and robustness of power prediction are improved, and a more reliable scheduling basis is provided for large-scale access of a new energy station to a power grid.

The invention provides a space and risk determination method and device for hidden danger objects of a distribution network line, and relates to the technical field of distribution network line management and control. The method comprises the steps of obtaining a distribution network line and multi-modal data of a hidden danger object which forms potential threats to the distribution network line, performing feature extraction on the multi-modal data, and determining multi-dimensional features of the hidden danger object. And then, inputting the multi-dimensional features into a multi-task fusion model, and predicting an event category to which the hidden danger object belongs based on the multi-task fusion model. And next, obtaining geographic position data of the hidden danger object, and mapping the geographic position data and the belonging event category to a target GIS platform to determine the geographic distribution condition of the hidden danger object. And finally, determining a risk score of the hidden danger object based on the event category and the geographical distribution condition, and determining a risk level of the hidden danger object based on the risk score. The geographic distribution condition and the risk level of the hidden danger object can be accurately determined.

The invention provides an application of an OsINV2 gene in regulation and control of rice cold stress resistance. A nucleotide sequence of the OsINV2 is shown as SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3 or SEQ ID NO: 4. By revealing the key regulation and control effect of the vacuole sucrose invertase gene OsINV2 in rice low-temperature stress response, the molecular mechanism that the gene affects the cold resistance of plants by regulating the sugar metabolism balance in vacuoles is illustrated for the first time. The expression of the OsINV2 is regulated in a targeted manner through molecular marker-assisted breeding or a gene editing technology, so that the low-temperature survival rate in the seedling stage and the cold resistance in the booting stage of the rice can be remarkably improved, glumous flower degeneration and pollen abortion caused by low temperature are reduced, and meanwhile, the limitation of the cold resistance of varieties on geographical distribution is broken through; a theoretical support and a technical path are provided for breeding and adaptive popularization of cold-resistant rice varieties.

The invention relates to an artificial cross breeding method for male epinephelus fuscoguttatus and female humpback perch, which belongs to the technical field of distant cross breeding and comprises the steps of parent selection of the epinephelus fuscoguttatus and the humpback perch, parent ripening cultivation, gamete collection, artificial insemination and incubation and fry cultivation. By means of reproductive regulation and distant hybridization technologies, two species of two genera of grouper family in different geographical distribution and different reproduction time are hybridized, namely cross-generic hybridization of grouper family breaks through the generic reproductive isolation, and therefore good varieties which grow fast and are high in survival rate are cultivated.

The invention discloses a carbapenemase OXA-58-like positive plasmid and a host bacterium propagation risk assessment method thereof, which are used for carrying out integrated assessment on the plasmid and the host bacterium in three dimensions of drug resistance, pathogenic potential and propagation potential, and carrying out comprehensive risk grading and classification by taking the OXA-58-like positive plasmid and the host bacterium thereof as a plasmid-host complex. The invention develops a set of OXA-58-like gene screening and systematic analysis process based on a public plasmid database aiming at the defects that the existing research of the OXA-58-like gene is mostly limited to a single species or a local sample and a standardized automatic analysis method is lacked. According to the method, all OXA-58-like gene positive plasmids in a database can be subjected to unified and efficient drug-resistant gene mining, the association rule between the drug-resistant genes and plasmid pedigree, host strains and geographical distribution is systematically disclosed, and important technical support is provided for drug resistance propagation monitoring and risk assessment.

The present invention provides a method and system for encrypted storage and inference of DNA barcodes of rare and endangered species, which relates to the field of encrypted inference technology, including: obtaining a rare and endangered species monitoring data set and a corresponding biological sample set; constructing an encrypted database based on the monitoring data set to obtain an anti-parsing database framework; encrypting the DNA barcodes based on the biological sample set to obtain an encrypted barcode set; generating a single valid sequence based on a verification sequence generated by the DNA sample to be identified; performing collaborative inference verification based on the single valid sequence and the encrypted barcode set to generate a species identification code; performing a comprehensive judgment based on the species identification code and the monitoring data set, and outputting an identification result by correlating morphological feature changes with geographic distribution density. By constructing an anti-parsing encrypted database framework and a functionally limited DNA barcode set, the present invention achieves active protection of rare species genetic data and solves the key hidden danger of sensitive biological information being used for poaching tracing.

The invention discloses a water injection wellhead injection pressure monitoring and evaluating method, which comprises the following steps of: realizing cross-well pressure data coupling by utilizing geographical distribution labels based on multi-point pressure real-time acquisition of a target and an adjacent water injection well, extracting pressure fluctuation characteristics after data missing correction, multi-stage noise reduction and normalization, and calculating the injection pressure of a water injection wellhead. Inputting an automatic induction dominant pressure mode of the self-evolution multi-label clustering model, and generating a reference template dynamically updated along with working conditions; the pressure fluctuation consistency is judged by comparing the real-time pressure trend with a reference template distribution label, collaborative anomaly traceability analysis is implemented for significant deviation, the anomaly cause type is recognized by combining historical drift and noise characteristics, a detection threshold value is automatically optimized, a machine-readable anomaly traceability report is generated, and an operation and maintenance decision is assisted; an online sample continuous learning mechanism ensures the adaptability and diagnosis accuracy of the model under different working conditions. According to the method, the intelligence, robustness and abnormal response efficiency of water injection well area pressure monitoring are improved.

The invention discloses a basin and peripheral area digital modeling method and system based on big data analysis, and relates to the technical field of digital modeling, and the method comprises the steps: connecting a GIS system, and obtaining the geographic distribution information of a target basin area; building a digital space, carrying out grid division on the digital space, and determining a digital grid; acquiring detection data of each exploration zone in the target basin area, performing data interpretation and preprocessing respectively, and determining geological feature information; based on the digital grids, in combination with the geological feature information, performing three-dimensional modeling on the digital grids in parallel, and respectively determining grid models; and splicing the grid models, determining a digital basin periphery model, performing macroscopic quantization, and generating a macroscopic digital basin periphery model. According to the method, the technical problem of low model building efficiency in linkage modeling of the basin combined peripheral mountain-building belt in the prior art is solved, and the technical effect of improving the efficiency and the accuracy of exploration and analysis of the basin and the peripheral area is achieved.

A method for estimating glacial meltwater runoff is disclosed, comprising the following steps: S1, obtaining glacier geographical distribution information and calculating driving data; S2, simulating glacier ablation; S3, calculating glacial meltwater runoff. A relatively accurate glacial meltwater runoff estimation model is established, solving the problem of significant uncertainty in existing meltwater runoff estimation methods, and is able to simulate the changing characteristics of each glacier.

The present invention relates to the technical field of geographic information systems, specifically a geographic information acquisition system and method based on remote sensing images, including a remote sensing image analysis module, a feature data extraction module, a classification and matching module, and a geographic data analysis module; by performing spectral reflectance analysis on all pixels, extracting color values and feature analysis within different band ranges, and extracting texture feature values containing geological information, the present invention can more accurately identify different surface cover types; by calculating the weighted Euclidean distance between the feature vectors of each image unit and each label classification vector, effective matching between the image unit and the geographic classification label is achieved; by extracting the statistical results of the geographic classification label matching to count various types of geographic information data, it helps to understand the geographic distribution characteristics and provides data support for subsequent geographic planning and resource utilization.

The application discloses a wave energy array development site selection method, and belongs to the field of wave energy development and utilization, comprising the following steps: 1) processing geographic distribution data by using a spatial analysis method of GIS to obtain a restricted development area layer; 2) constructing an evaluation index system for wave energy array development site selection, and performing standardization processing to obtain a comprehensive evaluation result; 3) calculating an offshore distance to generate a refined developable range layer; 4) carrying out statistical analysis on a wave direction in the developable range to obtain an array development range; 5) determining a potential development site on the basis of the array development range of a target sea area; and 6) taking the potential development site as a center point to perform quantitative analysis and trend analysis on array development capacity, and determining a key development site. The application integrates multidimensional evaluation indexes such as environmental compatibility, resource endowment, technical feasibility and economic rationality, and provides a scientific basis for wave energy power station array development site selection.

The application discloses a bird chirp sound recognition method based on a combination of voiceprints and spatial distribution, and belongs to the technical field of intelligent sound classification and recognition. In view of the problems of ignoring geographical distribution prior knowledge and sample imbalance in the prior art, the application firstly constructs a voiceprint recognition model: a training data set is constructed by audio preprocessing, logarithmic mel spectrum and dynamic difference feature extraction, a model is trained based on DenseNet-121 by adopting a two-stage training strategy, and recognition confidence of each species is obtained; meanwhile, a spatial distribution model is constructed: based on public observation data, an average observer ability index is used to correct an original encounter rate, and spatial distribution probability of the species in a specific city is obtained; finally, a Sigmoid function is used to perform nonlinear fusion on the two, a joint recognition probability is calculated, and a classification result is output. The application introduces ecological spatial constraints into the recognition decision, effectively reduces false positive misjudgment, improves rare species monitoring capability, and makes the recognition result have ecological interpretability.

The invention discloses a plant identification method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: identifying a plant in a plant image to obtain plant identification result information; wherein the plant identification result information is generated according to plant multi-modal feature information of the plant; displaying the plant identification result information; wherein the plant identification result information comprises at least one of the following items: attribute information of the plant, geographical distribution information of the plant, functional value information of the plant, and morphological description information and images of the plant in different growth periods.

The present invention relates to "A process for the production of whitefish from Pátzcuaro (Chirostoma estor) and balanced diets for its growth and fattening." These diets have specific characteristics for this species, with a content of easily digestible protein ingredients that efficiently contribute to larval survival and the size and weight of the juveniles fed in aquaculture farms with the appropriate diet for each stage. These diets can enter the national and international markets due to their unique characteristics and the results obtained with stomachless fish. Additionally, these feeds can be used for the production of species at a lower level of the food chain, which do not require the use of fishmeal and fish oil, making the aquaculture of these species a more sustainable activity.These diets are the first developed specifically for the successful growth and fattening of the species C. estor, "Pátzcuaro whitefish", having the potential to impact its conservation and marketing in the region of geographic distribution.

An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.

The application discloses a mountain and hilly biomass collection, storage and transportation collaborative scheduling method and system based on multi-agent game, relates to the technical field of wisdom agriculture and supply chain collaboration, and aims to solve the problems of unbalanced benefit distribution, ignored terrain constraints and rigid collaborative mechanism in the prior art. The application first acquires static attribute data, dynamic operation data of the collection and storage nodes, and geographical distribution and resource quantity of all resource points of the biomass to be collected and transported; determines the dispersion degree of the geographical distribution of all resource points, the balance degree of the resource quantity, and the difference degree of the appeals of the benefit related parties; takes the dispersion degree, the balance degree, the static attribute data of each collection and storage node and the difference degree of the appeals as judgment indexes, determines the collaborative mode of the benefit related parties according to a preset judgment mechanism, executes collaborative planning, and completes dynamic planning of the collection path and the corresponding resources; and finally realizes dynamic allocation of resources.

The invention discloses a method and system for analyzing extreme lightning distribution characteristics, and the method comprises the steps: obtaining multi-year lightning data monitoring result data, carrying out the preprocessing of the multi-year lightning data monitoring result data, and obtaining the preprocessed data; carrying out binning processing on the preprocessed data according to a preset time dimension and a space dimension, and calculating a key statistical magnitude to obtain lightning activity data; performing spatial matching on the lightning activity data and geographic information data to obtain a geographic distribution condition of the lightning activity data; and carrying out visualization processing on the geographical distribution condition of the lightning activity data. According to the method, the extreme value spatial distribution characteristics of the lightning frequency and the current intensity can be accurately identified, and invalid or abnormal data can be effectively eliminated, so that the reliability and the accuracy of an analysis result are improved.

The invention provides a comprehensive pipe gallery underground water level risk early warning method and system, and relates to the technical field of risk early warning, and the method comprises the steps: collecting the pipe gallery geographical position distribution of a target comprehensive pipe gallery, and constructing a comprehensive pipe gallery distribution network; subarea risk analysis of the underground water level is executed, and each pipe gallery subarea and an underground water level risk list of each subarea are generated; monitoring underground water level data of the located area in real time; and inputting an underground water level risk list of each subarea, executing early warning triggering of each pipe gallery subarea, and generating underground water level risk early warning information. The technical problem that in the prior art, due to the fact that underground water level risk early warning mostly adopts a simple static model and a unified water level threshold value to judge the safety of the pipe gallery, consideration of geographical distribution of the pipe gallery is lacked, and risk early warning is not accurate is solved, the geographical distribution network of the comprehensive pipe gallery is established, and the risk early warning accuracy is improved. Regional analysis of the underground water level risk is realized, and the accuracy of underground water level risk early warning is improved.

The present application discloses an intelligent control system and method for a music fountain based on smart tourism, which captures and learns the geographical distribution characteristics of tourists' locations by adopting a deep learning algorithm based on artificial intelligence and computer vision technology, and introduces a saliency enhancement module of a global context to enhance the expression of the geographical distribution characteristics, and then uses a reverse masking mechanism to cluster and fuse the enhanced geographical distribution characteristics of tourists, thereby forming a comprehensive representation of the geographical distribution characteristics of tourists, and finally uses a classifier to construct a performance mode determiner to intelligently determine the performance mode of the fountain according to the real-time geographical location information of tourists.

The invention discloses a model for exploring the influence of an environment on plant distribution and application. The plant refers to gentiana dahurica fisch. Based on geographical distribution of gentiana dahurica fisch and ecological factors influencing growth of gentiana dahurica fisch, which are determined by literatures and field investigation, an ecological suitable area of gentiana dahurica fisch is modeled by adopting an enhanced regression tree (BRT). And establishing a relationship between the content of each active component in the Inner Mongolia plant and an ecological factor by adopting a linear regression method. The invention discloses a model for exploring the influence of the environment on the distribution of gentiana dahurica fisch, and discusses the influence of the environment on the growth of gentiana dahurica fisch and the content of active ingredients. The model provides guidance for coordinated development of medicinal plant science.

An example method comprises receiving first historical meso-scale numerical weather predictions (NWP) and power flow information for a geographic distribution area, correcting for overfitting of the historical NWP predictions, reducing parameters in the first historical NWP predictions, training first power flow models using the first reduced, corrected historical NWP predictions and the historical power flow information for all or parts of the first geographic distribution area, receiving current NWP predictions for the first geographic distribution area, applying any number of first power flow models to the current NWP predictions to generate any number of power flow predictions, comparing one or more of the any number of power flow predictions to one or more first thresholds to determine significance of reverse power flows, and generating a first report including at least one prediction of the reverse power flow and identifying the first geographic distribution area.

The invention discloses an earthquake fortification management and disaster analysis system based on unmanned aerial vehicle brain-like real-time calculation, which relates to the field of disaster analysis and comprises an image set acquisition unit used for acquiring a time sequence image set, a feature map extraction unit used for outputting corresponding N convolution feature maps, a group classification unit used for acquiring feature groups and a feature map fusion unit. The fusion feature map obtaining unit is used for obtaining a fusion feature map, the feature arrangement unit is used for generating a feature geographic distribution map, and the disaster detection unit is used for obtaining a disaster feature geographic distribution map; wherein the disaster feature geographic distribution map represents multi-view fusion disaster features of all geographic grids in the time window. The evolution graph construction unit is used for constructing a disaster feature space-time evolution graph; the disaster feature geographic distribution map finally formed by the method can accurately reflect the spatial distribution and evolution process of the damage state of the disaster area, and provides structured disaster data for earthquake fortification management and disaster analysis.

The invention discloses a biomass collection, storage and transportation intelligent networking method and system based on geographic information, and relates to the technical field of intelligent agriculture, and the method comprises the steps: obtaining the static attribute data and dynamic state data of each collection and storage station in a network, and the geographic distribution and resource quantity information of resource points to be collected and transported; based on the static attribute data of the storage station, the topology centrality is analyzed, and the real-time function state level is evaluated; based on the geographic dispersion, the resource quantity balance degree and the road network connectivity of the resource point cluster to be collected and transported, task decoupling performance judgment is carried out; if the task is determined to be a highly decoupled task, decoupling the resource point cluster into a plurality of geographically adjacent sub-clusters, and planning an independent parallel collection path and an integration point for each sub-cluster; if the task is determined to be a low decoupling task, a centralized cyclic collection path is planned; and according to the real-time function state level of the storage station, dynamically allocating resources corresponding to the planned collection path to the storage station matched with the function state.