Abstract: A photogrammetric ground control point quality evaluation method based on Monte Carlo test relates to the field of photogrammetry technology. Firstly, aerial photographs and ground control points from a survey area are obtained, the ground control points are numbered, the aerial photographs are performed with point-placing and aerial triangulation densification. Secondly, a Monte Carlo test experiment is designed, a certain number of the ground control points are selected as control points with the rest as check points, ensuring each ground control point as the control point a certain number of times, and average errors of the ground control points are calculated. Thirdly, average values of the average errors of the ground control points are calculated, standard deviations of the average errors of the ground control points are calculated. Finally, a quality coefficient Q of each ground control point is calculated and evaluated according to quality evaluation standards.

Abstract: A sand dune extraction method based on fusion of positive-negative terrains and light shading includes: obtaining an original digital elevation model of a study region; using a neighborhood analysis method to process the original digital elevation model of the study region, thereby extracting a positive terrain region and a negative terrain region of the study region; using a lighting shading method to perform six directional lighting simulation calculations on each pixel of the original digital elevation model of the study region to obtain a lighting shading map, and extracting a lighting region and a shadow region of the study region from the lighting shading map; using an embedding method to fuse the positive terrain region and the negative terrain region with the lighting region and the shadow region. The sand dune extraction method is a method for remote sensing monitoring and governance of desertification regions.

Abstract: Disclosed is an online optimization method for a power consumption policy of a terminal of Internet of things (IoT), including: performing comprehensive scanning on a user terminal and a link, and obtaining a link behavior graph; inputting the link behavior graph into a simulation based optimization (SBO) system to obtain power consumption parameters for minimizing power consumption of the user terminal; performing centralized storage on the link behavior graph and the power consumption parameters; regularly sending, by the user terminal, operation data to a server, and evaluating, by the server, whether a power consumption policy needs to be optimized; and issuing, by the server, a parameter update instruction to complete the upgrading of the power consumption policy of the user terminal. In the present disclosure, the update of the power consumption policy of the user terminal can be conveyed more quickly and more efficiently.

Abstract: The present disclosure relates to a method for stochastic inspections on power grid lines based on unmanned aerial vehicle-assisted edge computing. According to the method, a stochastic distributed inspection unmanned aerial vehicle is adopted to acquire video images on a target power grid area, which can reduce funds and time costs of inspections. With assistance of superior unmanned aerial vehicle, a goal is to minimize energy consumption of an unmanned aerial vehicle system and extend operation time of the unmanned aerial vehicles under same payload conditions, while processing video image data collected from the inspection unmanned aerial vehicles. The near-far effect generated by communications between mobile unmanned aerial vehicles is eliminated by introducing a NOMA, and position coordinates, system resource allocations and task offload decision schemes are solved by using a method of combining a DDPG algorithm in a Deep reinforcement learning with a genetic algorithm.

Abstract: The present disclosure relates to a method for stochastic inspections on power grid lines based on unmanned aerial vehicle-assisted edge computing. According to the method, a stochastic distributed inspection unmanned aerial vehicle is adopted to acquire video images on a target power grid area, which can reduce funds and time costs of inspections. With assistance of superior unmanned aerial vehicle, a goal is to minimize energy consumption of an unmanned aerial vehicle system and extend operation time of the unmanned aerial vehicles under same payload conditions, while processing video image data collected from the inspection unmanned aerial vehicles. The near-far effect generated by communications between mobile unmanned aerial vehicles is eliminated by introducing a NOMA, and position coordinates, system resource allocations and task offload decision schemes are solved by using a method of combining a DDPG algorithm in a Deep reinforcement learning with a genetic algorithm.

Abstract: A method and system for evaluating casualty caused by landslide chain disaster induced by heavy rain are provided. The method includes: obtaining historical landslide data, candidate influence factors and personnel traffic distribution data of an observation site; dividing the historical landslide data into a model construction sample group, a model verification sample group, and a model test sample group; obtaining an occurrence probability of landslide chain disaster of each spatial position in the observation site; determining a start-up landslide source region of the landslide chain disaster according to the model verification sample group and the occurrence probability; determining an affected region of the landslide chain disaster according to the start-up landslide source region; determining a personnel number exposed to the landslide chain disaster; and setting an event division threshold, and evaluating the number of casualties according to the event division threshold and the personnel number.

Abstract: A multi-tag concurrent identification method and a system for a query tree based on feature groups are provided in this disclosure. In the disclosure, a whole data string space is divided into a plurality of disjoint subsets according to features of data strings returned by tags, where each of the subsets contains several different data strings, each of the data strings in the each of the subsets is regarded as a complete tag ID or a partial ID, and the each of the subsets corresponds to a unique query prefix, a length of the prefix is fixed and does not dynamically increase with an actual location of a collision, and when multiple data strings from a same subset return at a same time, a reader is capable of identifying them at a same time in a slot.

Abstract: A high-temperature disaster forecast method based on a directed graph neural network is provided, and the method includes the following steps: S1, performing standardization processing on meteorological elements respectively to scale the meteorological elements into a same value range; S2, taking the meteorological elements as nodes in the graph, and describing relationships among the nodes by an adjacency matrix of graph; then learning node information by a stepwise learning strategy and continuously updating a state of the adjacency matrix; S3, training the directed graph neural network model after determining a loss function, obtaining a model satisfying requirements by adjusting a learning rate, an optimizer and regularization parameters as a forecast model, and saving the forecast model; and S4, inputting historical multivariable time series into the forecast model, changing an output stride according to demands, and thereby obtaining high-temperature disaster forecast for a future period of time.

Abstract: The present disclosure provides a meteorological big data fusion method based on deep learning, including the following steps: constructing multi-source meteorological data samples; according to an original resolution of different climate variables, selecting a corresponding super-resolution multiple to obtain an optimized super-resolution module under the constraint of maximizing information retention efficiency; constructing a spatial-temporal attention module using a focused attention mechanism, and selecting a corresponding time stride according to periodic characteristics of different climate variables; constructing a meteorological data fusion model in combination with the optimized super-resolution model and the spatial-temporal attention module; taking a minimum resolution of climate variables as a loss function, and training the meteorological data fusion model with the multi-source meteorological data samples; and importing the acquired real-time meteorological data from multiple data sources into t

Abstract: An evaluation method for evaluating a precipitation-induced landslide disaster loss under climate change is provided. The evaluation method belongs to the technical field of geological disaster prevention and treatment. The evaluation method uses a physical process based model, in considering of spatial heterogeneity of land-surface features of grids in the area, to obtain precipitation thresholds corresponding to the respective grids in the area having the spatial heterogeneity. Historical data and climate model data are taken in combination to select suitable climate models, and the model then is used to simulate landslide prone zones and possible influence zones caused by landslides. An influence zones simulated by the evaluation method can better match disaster loss grid data, which can solve the problem that climate change scenarios and influence of landslide are difficult to be evaluated in landslide disaster evaluation.

Abstract: The underwater acoustic test system comprises an underwater acoustic transmitting unit, an underwater acoustic parabolic reflector, an underwater acoustic receiving unit, an orientation control system, and a computer measurement and control system. The underwater acoustic transmitting unit comprises an underwater acoustic signal generator and a transmitting transducer. The underwater acoustic parabolic reflector comprises a central main reflecting area and an edge diffraction processing area, wherein the central main reflecting area is configured for reflecting acoustic wave signals, and the edge diffraction processing area is configured for reducing the influence of the underwater acoustic parabolic reflector on a test area. The underwater acoustic receiving unit comprises a receiving transducer and an underwater acoustic signal receiver. The orientation control system comprises a traveling crane and a test turntable.

Abstract: The present application provides a method and system for identifying extreme climate events. The method acquires climate index (CI) grid data of a to-be-identified region within an extreme climate time period, and gradually expands each of event centers in the to-be-identified region, until CI values of all grids adjacent to the event center are not greater than a CI threshold. The method can obtain extreme climate impacted areas of extreme climate events in the to-be-identified region, and can further obtain CI intensities of the extreme climate events by average calculation. The method can obtain three pieces of dimension information on each of the extreme climate events in the to-be-identified region, including an extreme climate impacted area, a CI intensity and a duration. Therefore, the method can identify the extreme climate events more comprehensively.