8results about How to "Disaster loss reduction" patented technology

The invention relates to the technical field of highway slope geological disaster management, and discloses a risk zoning and early warning method for mountain highway slope geological disasters, comprising the following steps: step 1, obtaining the slope geological data of the mountain highway through a network connection to form a geological data set; step 2, obtaining the real-time meteorological data of the mountain highway through a network connection to a big data platform to form a meteorological data set; step 3, obtaining the real-time video data of the mountain highway through a network connection to a monitoring device to form a monitoring data set; step 4, analyzing and generating risk scores and average scores according to the geological data set, dividing the geological disaster risk levels of all road sections from high to low, and comprehensively evaluating the disaster risk more accurately; step 5, identifying the video data set through image recognition technology, judging whether the mountain highway slope has geological disasters, and generating a corresponding disaster area, and then generating an early warning report or an alarm report in combination with the meteorological data set, so that the prevention and control capability is strong and the early warning effect is good.

The invention discloses a risk double-control prevention and control system, which belongs to the technical field of geological disaster prevention and control, and comprises a point-surface double-control system and a point-area joint control system, and the point-surface double-control system is a double-control system of a hidden danger point and a risk prevention area; point-area joint control is a joint control system of hidden danger points and area risk prevention areas; the method comprises the following two conditions: in a non-typhoon rainstorm period, a double-control system of a hidden danger point and a risk prevention area is adopted; in a typhoon rainstorm period, on the basis of adopting a double-control system of a hidden danger point and a risk prevention area, a joint control system of the hidden danger point and a regional risk prevention area is adopted. According to the risk double-control prevention and control system, a point-surface double-control prevention and control system and a point-area joint-control prevention and control system are combined, the system is also effective for mass-occurring geological disasters occurring in a typhoon rainstorm central area, and risk management and control are conducted on a plurality of new geological disaster points.

The invention discloses a reservoir dam body-based facade structure analysis method, equipment and a medium, belongs to the technical field of dam monitoring, and aims to solve the problems of low efficiency, difficulty in comprehensive acquisition and difficulty in identification of potential safety risks in facade structure deformation observation in existing dam facade structure monitoring. And early warning information is difficult to send out in time. The method comprises the following steps: performing multi-level point cloud scanning on a steel sheet pile cofferdam of a dam through a preset three-dimensional laser scanner to obtain three-dimensional point cloud data; performing data preprocessing on the three-dimensional point cloud data to obtain uniform point density point cloud data of the steel sheet pile; performing steel sheet pile cylinder deformation analysis related to a least square circle on the uniform point density point cloud data to obtain change trend data based on the steel sheet pile cylinder; and determining the safety risk information of the dam based on the change trend data of different sections in the steel sheet pile cylinder.

A method for warning abnormal events in urban underground integrated pipe corridors, including: obtaining target attribute data through pipe corridor sensor equipment; using Kalman filtering to perform data-level fusion on homogeneous data sensor data; using the results of data-level fusion to predict future pipe corridor data through an improved and trained TS-Transformer model; using D-S evidence theory to perform decision-level fusion on the results of data-level fusion and predicted data; using the decision-level fusion results to realize real-time detection and warning analysis in parallel. The proposed secondary data fusion model can effectively reduce the redundancy and uncertainty of pipe corridor data; the proposed prediction model solves the problem that the traditional RNN-based time series prediction model cannot be trained in parallel and is difficult to handle long-term time dependence. The proposed abnormal event warning method can not only detect whether abnormal events occur in the pipe corridor in real time, but also warn of possible accidents in advance, effectively reducing the risk of major accidents in the pipe corridor and reducing economic losses.

The invention relates to the crossing field of intelligent agricultural insurance and intelligent agriculture, in particular to a wheat planting complete cost insurance double-precision double-reduction technology integrated intelligent system based on space-air-ground multi-source data, which comprises a data acquisition module for acquiring satellite remote sensing, unmanned aerial vehicle aerial survey and internet-of-things perception covering a wheat planting complete period, space-air-ground multi-source data such as cross-department government affairs and market sharing can be realized; and a data preprocessing, outputting and active pushing module. According to the method, wheat exclusive monitoring equipment is adopted, air-space-ground multi-source data and standardized technical parameters are integrated, through planting area grading verification and marking, a growing period dynamic compensation model is fused, data accuracy and consistency are ensured, single data dimension limitation is broken through, full-period all-dimensional accurate monitoring from sowing to harvesting is achieved, and the method is suitable for large-scale popularization and application. Causal association and forecasting of risks and yield, quality and price are achieved, the planting area underwriting error is smaller than or equal to 3%, and the loss assessment error is smaller than or equal to 5%; the claim settlement period is shortened to be within 3 days from traditional 15-30 days, the farmer experience and insurance claim settlement efficiency and precision are greatly improved, and high-quality data support is provided for double-precision business and double-reduction intervention.

The invention belongs to the field of corn waterlogging early warning, relates to remote sensing monitoring technology, and is used to solve the problem that the accuracy of corn waterlogging early warning cannot be guaranteed and the early warning process cannot be optimized. Specifically, it is a remote sensing monitoring system for corn waterlogging early warning, comprising a waterlogging early warning module, a waterlogging alarm module and a waterlogging early warning optimization module, wherein the waterlogging early warning module comprises a planting site monitoring unit and a precipitation monitoring unit; the waterlogging early warning module, the waterlogging alarm module and the waterlogging early warning optimization module are sequentially connected for communication; the water body and corn state of the corn planting site are monitored through a remote sensing platform, and waterlogging early warning and waterlogging alarm are performed on the corn planting site in combination with precipitation information, and the waterlogging early warning process is optimized according to the early warning level and the alarm level; the invention can combine the water body state, precipitation information and the growth coefficient of corn to ensure the timeliness of corn waterlogging early warning and improve the accuracy of corn waterlogging early warning.

This invention discloses a method for predicting urban flood disasters based on satellite infrared brightness temperature and precipitation data. The method includes: combining satellite infrared brightness temperature and precipitation data to identify relevant data of complete lifecycle MCSs (Multi-Category System Components); extracting and filtering CCS area evolution data; determining the optimal value of the sensitivity coefficient; fitting the relationship function between CCS area and time, determining the most vigorous development time as the center point, determining the slope critical threshold based on the sensitivity coefficient, and finding boundary points to divide the development, maturity, and dissipation stages; finally, integrating urban underlying surface characteristics and a waterlogging model to quantitatively predict the level of urban flood disasters and push emergency response measures, optimizing the prediction results and response plans through a dynamic feedback mechanism. This invention can accurately capture the essential characteristics of each stage of MCSs, and based on the capture results, achieve full-process automation from meteorological monitoring to flood prediction and emergency response.

The invention provides a seismic wave and sound wave multichannel real-time fusion processing method and device, and the method comprises the steps: obtaining a seismic data set collected by at least two collection points in multiple earthquakes, and dividing the seismic data set into a first sub-seismic data set and a second sub-seismic data set; inputting the first sub-seismic data set into a preset model for training to obtain a trained seismic information acquisition model; and acquiring a real-time seismic data set, and inputting the real-time seismic data set into the seismic information acquisition model to obtain real-time seismic information. The method has the beneficial effects that the accuracy and comprehensiveness of seismic information extraction are improved by fusing seismic wave and sound wave data, seismic data can be acquired and processed in real time, timely information support is provided for seismic early warning and disaster assessment, a confrontation training mechanism of the GAN model is adopted, model parameters are continuously optimized, the performance and robustness of the model are improved, and the method is suitable for large-scale popularization and application. A new technical means is provided, and the earthquake disaster coping capacity can be improved, and disaster loss can be reduced.