239 results about "Extreme climate" patented technology

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention discloses an extreme climate self-adaptive flight control system for an unmanned aerial vehicle in a complex terrain, and relates to the technical field of unmanned aerial vehicle self-adaptive flight control. The extreme climate adaptive flight control system for the unmanned aerial vehicle in the complex terrain comprises a flight data acquisition and preprocessing module, a climate prediction and risk assessment module, an optimal path and task sequence adjustment module, a real-time flight control and path optimization module and a safety emergency response and recording module. Wherein the flight data acquisition and preprocessing module is used for acquiring flight data; the climate prediction and risk assessment module is used for constructing a neural network model to predict climate; the optimal path and task sequence adjusting module is used for performing path and task security evaluation; the real-time flight control and path optimization module is used for converting a path and a task sequence into an aircraft instruction; and the safety emergency response and recording module is used for executing and recording the emergency strategy. The problem that the unmanned aerial vehicle is difficult to adjust flight in real time due to continuous change of climate states in the prior art is solved.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention discloses a grain yield estimation method based on a land-gas coupling model and a machine learning algorithm, and relates to the technical field of agricultural meteorological prediction and intelligent decision making, and the method comprises the following steps: S1, carrying out the kernel-level algorithm fusion of a dynamic corn growth model and a weather research and forecast model, constructing a WRF-Crop coupling system, and carrying out the kernel-level algorithm fusion of the WRF-Crop coupling system; and the WRF-Crop coupling system realizes real-time interaction of transpiration and albedo change of the corn canopy and temperature, humidity and radiation data through a bidirectional data channel. According to the grain yield estimation method based on the land-gas coupling model and the machine learning algorithm, the limitation of isolated operation of a traditional dynamic corn growth model and a meteorological model is broken through by constructing a WRF-Crop bidirectional coupling model, dynamic mutual feedback simulation of vegetation growth and local climate is realized, and a crop stress response mechanism under an extreme climate event is effectively captured.

The invention discloses a water resource demand optimization method based on deep reinforcement learning, and relates to the technical field of water resource management, and the method comprises the steps: obtaining original data and real-time data; preprocessing the original data; extracting parameters of an extreme climate event according to the original data, generating extreme scene data of a plurality of disturbance scenes based on a pre-trained environment simulator according to the historical climate data and the parameters of the extreme climate event, and training a preset DQN-based deep reinforcement learning model according to the optimization training set, the network architecture of the DQN-based deep reinforcement learning model is CNN, optimizing the DQN-based deep reinforcement learning model based on a multi-target reinforcement learning strategy to obtain a trained DQN-based deep reinforcement learning model, inputting real-time climate data into the trained DQN-based deep reinforcement learning model, and outputting real-time optimized regional water resource demand data. According to the invention, the decision-making efficiency and accuracy of water resource demand optimization are significantly improved.

The invention discloses a frontier defense important place integrated intelligent monitoring method, and relates to the technical field of intelligent monitoring, aiming at the monitoring requirements of multiple types of sensors under extreme climate and complex terrain, the method comprises the following steps: firstly, preprocessing the working state data of the environment and the sensors, and establishing an initial working base line; when the deviation between the actual output and the ideal working condition is detected, automatically correcting the gain and the threshold value by adopting a dynamic calibration model; then potential faults are identified based on failure aggregation measurement and a historical fault library, and once high risks are confirmed, early warning is triggered, and standby monitoring equipment is switched to; finally, deviation is further checked through multi-source synchronization and feature fusion, the decision weight is dynamically adjusted, and robust and efficient monitoring of the frontier defense important place is achieved. According to the scheme, an expandable parameter adjustment domain is provided in the identification and adjustment and optimization link, edge and center cooperation is considered, high adaptability and expandability are achieved, and therefore the adaptability and operation efficiency of the system in changeable weather are improved.

The invention relates to the technical field of anti-icing, in particular to a photo-thermal phase change energy storage super-hydrophobic all-weather anti-icing composite coating and a preparation method thereof. Comprising the following steps: S1, preparing a phase change microcapsule of chitosan coated paraffin through a single coacervation method; s2, modifying a carbon nano tube, and adding the modified carbon nano tube into the suspension of the phase change microcapsule to form a composite microcapsule; and S3, uniformly mixing the composite microcapsule with organic silicon. Compared with the prior art, by combining the photo-thermal and super-hydrophobic characteristics and the energy storage effect of the phase change microcapsules, the effect of effectively delaying icing can be achieved, the ice resistance and deicing efficiency is high, the photo-thermal conversion performance is good, the super-hydrophobic performance is good, and surface ice prevention and deicing under the extreme weather condition can be achieved; and the coating can keep effective anti-icing and deicing functions under low-temperature and high-humidity conditions. 2, the nanoparticles are grafted to the surface of the chitosan phase change microcapsule to form an organic-inorganic composite phase change microcapsule, and the flexibility of organic substances and the stability of inorganic materials are combined;

The invention mainly relates to the technical field of hydrological forecasting. In order to improve the precision and real-time performance of hydrological flow prediction, the invention provides a hydrological flow prediction method based on causal inference and an extended long short-term memory network, and the method comprises the following steps: collecting historical hydrological flow and meteorological factor data, and carrying out the preprocessing; meteorological factors having Granger causality with the hydrological flow are screened out to serve as characteristic meteorological factors for hydrological flow prediction; and establishing a hydrological flow prediction model based on the extended long short-term memory network, taking the historical feature meteorological factors and the hydrological flow data as the input of the hydrological flow prediction model, and training the hydrological flow prediction model to predict the future hydrological flow. The hydrological flow prediction model is more accurate when learning the long-term trend and short-term fluctuation of the hydrological flow, the interpretability and scientificity of a hydrological flow prediction result of the hydrological flow prediction model are enhanced, and a scientific basis and technical support are provided for hydrological management, drainage basin treatment and extreme climate response.

The invention discloses a wind power plant micro-siting assessment method based on spatial analysis and climate adaptability, relates to the technical field of wind power plant siting, and solves the problems that in the prior art, spatial analysis depends on static topographic data, climate adaptability assessment uses dynamic meteorological data, and the static topographic data and the dynamic meteorological data lack a space-time synchronization mechanism; and a prediction result has a large deviation. The method comprises the following steps: firstly, acquiring multi-source data and fusing to realize time-space synchronization of the data; then, constructing and solving an objective function to obtain a candidate point set, and screening out an optimized point distribution scheme of which the prevailing wind direction coverage is greater than a set threshold value; and finally, inputting prediction data of various extreme climates into the cost-per-kilowatt-hour model, introducing extreme climate toughness parameters into calculation to carry out climate adaptability data correction, and calculating return on investment cycles under different carbon emission scenes. Through the steps, the limitation that only static wind resources are concerned on traditional site selection is broken through, and combined evaluation of the regional climate adaptability and the complex terrain adaptability is achieved.

The invention provides a method and a system for improving the self-healing capability of a highway micro-grid in extreme weather, and relates to the technical field of power system automation. The method comprises the following steps: deploying a sensor to collect real-time operation data, and combining meteorological data to generate an integrated system state data set through fusion processing; the digital twin engine generates system state prediction data through state estimation and dynamic simulation; the random multi-objective optimization model solves and generates a preventive scheduling instruction according to the above, and controls a power generation unit, an energy storage unit and a load unit to adjust operation points; fault detection and identification are carried out through residual analysis, a self-healing control instruction sequence is generated by using a sequence decision model, the switch equipment is controlled to isolate faults and restore power supply, closed-loop management from prediction and scheduling to self-healing is realized, and the power supply reliability and rapid recovery capability of the highway microgrid in extreme climate are improved.

This invention proposes a method and system for optimizing water resource allocation, which relates to the technical field of water resource optimization. The method includes first obtaining historical hydrological data for a study area, then inputting the data into a preset river runoff prediction model, which then outputs a monthly runoff sequence for a specific year within the region. Based on the monthly runoff sequence, a multi-conditional constrained water resource optimization allocation model based on user allocation is established for the study area under extreme climate conditions. Finally, the multi-conditional constrained water resource optimization allocation model is solved to obtain a solution result, and water resource optimization is performed for users in the study area based on the solution result. This invention effectively achieves accurate and reasonable optimal allocation of water resources to users under multi-objective constraints.

The invention provides a wetland forest and grass ecosystem restoration strength evaluation method under drought stress. The method comprises the steps of data collection, daily-scale ecosystem service function index ESS construction, ecological drought identification and restoration elasticity curve construction. Wherein the time resolution of the index is accurate to the day, the capture of short-time drought below the monthly scale is enhanced, and the accurate description of the dynamic process of ecological system restoration under the drought stress is realized; combined identification is carried out on the drought events in combination with the standardized ecological water deficit, and the accuracy of identification of the drought events causing significant differences of ecological system service functions is improved; a quantile regression model is utilized to quantify the ecological system recovery capability of different vegetation types, and data characteristics of different recovery time distributions of the ecological system under the same drought intensity are considered, so that the defect that a traditional mean value regression model cannot fully describe drought complex characteristics is overcome; and a theoretical support is provided for sustainable development of an ecological system under an extreme climate condition.

The invention relates to the technical field of marine surveying and mapping, and discloses a sea level change trend accurate calculation method based on multi-source data fusion, which comprises the following steps: acquiring multi-source satellite height measurement data, performing data grouping, establishing a plurality of average sea level models, establishing a time-varying average sea level model through the average sea level models, and calculating the sea level change trend according to the time-varying average sea level model. And performing linear regression fitting on the average sea surface height time sequence at each grid point, constructing a grid average sea level change model, and calculating a change trend at each grid point. The method can improve the separation capability of signals with different time scales in sea level changes, is not affected by extreme climate events, natural environment changes and physical environment correction, and can greatly improve the prediction accuracy of the sea level trend.

The invention discloses an extreme high-temperature scene multi-energy complementary optimization scheduling method considering uncertainty, which mainly comprises the following steps of: 1, fusing bidirectional time convolution, a bidirectional long-short-term memory network, an attention mechanism and a quantile regression forest, realizing high-precision prediction and uncertainty modeling of wind speed, solar irradiation and load, and constructing a typical day scene set; 2, constructing a two-stage scheduling model fusing epsilon-constraint multi-objective optimization and opportunity constraint mixed integer programming, optimizing and adjusting margin day ahead, and rolling and correcting a scheduling path within the day; 3, three physical correction mechanisms of wind power air density correction, photovoltaic temperature response and hydroelectric evaporation-water level coupling are provided for extreme high-temperature disturbance; and 4, a prediction-optimization-feedback-correction closed-loop process is integrated and constructed, and the stability and response toughness of the system under extreme climate are improved. The method has the beneficial effect that the prediction precision, the scheduling flexibility and the operation toughness of the system under the extreme climate are remarkably improved.

The invention discloses a comprehensive evaluation method for a comprehensive energy system, and the method specifically comprises the following steps: 1, constructing a multi-dimensional index system: employing a hierarchical structure to construct four types of indexes including technology, economy, environment and reliability, and refining each type of indexes into at least three secondary indexes; and 2, dynamic weight adaptive distribution: adopting an improved entropy weight and game theory combined weighting method based on real-time data, such as electricity price fluctuation, weather change and load demand, and preset scenes, such as extreme climate and policy adjustment, and relates to the technical field of comprehensive energy system evaluation. According to the comprehensive evaluation method for the comprehensive energy system, correlation interference among indexes is effectively eliminated through an improved entropy weight and game theory combined weighting method, and the entropy value is dynamically updated by introducing multiple data information, so that weight calculation can adapt to the time sequence change characteristic of the energy system, and the comprehensive evaluation accuracy is improved. And meanwhile, the subjective and objective weight collaborative equalization is realized by constructing a Nash equilibrium game model.

The invention discloses application of a transcription factor BnaMYB52 in regulation and control of drought resistance of rape, and relates to the technical field of biology. The amino acid sequence of the transcription factor BnaMYB52 is as shown in any one of SEQ ID NO. 5 to 8. According to the invention, the negative regulation effect of the transcription factor BnaMYB52 in the drought resistance of the rape is disclosed for the first time, and a brand new molecular target and a genetic improvement strategy are provided for improving the drought resistance of the rape. Through in-depth study, it is found that knockout of the BnaMYB52 gene can significantly enhance the tolerance of rape plants to the drought environment. In addition, a new drought-resistant rape germplasm obtained after genetic modification of the gene shows excellent adaptive capacity in a drought stress environment. The technical breakthrough not only helps to solve practical problems faced by rape planting in arid regions, but also lays a solid theoretical foundation and technical support for cultivating high-yield and high-quality rape varieties adapting to extreme climate conditions.

The invention belongs to the technical field of disaster remote sensing monitoring, and relates to an AI technology-based composite high-temperature drought index construction method, which comprises the following steps of: collecting multi-source data, preprocessing the data, and constructing a high-temperature drought factor and an evaluation index; then, a drought monitoring model and a high-temperature monitoring model are respectively constructed by using multiple AI algorithms including machine learning and deep learning, finally, a composite high-temperature drought index is constructed, the constructed composite high-temperature drought index is used for monitoring and evaluating a composite high-temperature drought event, multi-source data are fused, and the accuracy of the composite high-temperature drought event is improved. According to the method, factors influencing high temperature and drought disasters are comprehensively considered, so that the recognition precision of the composite high temperature and drought disasters is remarkably improved, the AI technology is applied, internal association between data is fully explored, and the method has higher generalization ability, can be suitable for different climate regions and time scales, has good expansibility and is suitable for popularization and application. The method can be popularized and applied to construction of other composite disaster indexes, and provides support for monitoring and prediction of various extreme climate events.

The invention discloses a single-phase immersion battery intelligent thermal management control method and device for an energy storage power station, and relates to the technical field of battery thermal control. The intelligent thermal management control method and device for the single-phase immersed battery for the energy storage power station comprises the steps that S1, battery state data and external environment data in the operation process of the energy storage power station are collected and preprocessed, and a standardized thermal state data set is constructed; s2, performing working condition evaluation on the current working condition state by combining the battery heat flow density, and dynamically adjusting the flow direction of the cooling liquid; s3, the current heat exchange load and the phase state safety boundary are comprehensively analyzed, and the valve opening degree is driven to change; and S4, carrying out stability evaluation on the local thermal stability, and adjusting a power supply loop safety response strategy in real time. The problems that a traditional heat management system lacks the bidirectional regulation and control capacity for coping with wide-temperature-range extreme climate and cannot give consideration to the collaborative requirements of high-temperature heat dissipation and low-temperature heat supply, and consequently system structure redundancy, energy consumption increase and operation and maintenance complexity are caused are solved.

The invention provides an early warning system and early warning method for extreme climate in the orange orchard soil cultivation process, and relates to the technical field of agricultural meteorological monitoring, and the early warning method specifically comprises the steps: collecting a plurality of climate-related key parameters of an orange orchard soil cultivation region, and calculating a temperature stability index, a precipitation fluctuation index and a temperature and humidity adaptability index; generating an environmental influence index and an ecological adaptability index according to the soil humidity, the illumination intensity, the soil pH value, the soil organic matter content, the temperature stability index and the precipitation fluctuation index after the dimensionless treatment; and combining the environmental influence index, the ecological adaptability index and the temperature and humidity adaptability index to generate a comprehensive evaluation value of the soil cultivation area of the orange garden, comparing the comprehensive evaluation value with a preset climate evaluation threshold value, and formulating corresponding management measures according to a comparison result. The scientific assessment enables farmers to timely identify climate risk levels, and provides a basis for formulating targeted management measures.

The invention provides a flood control dam surface crack intelligent detection and early warning method based on AI image recognition, and the method comprises the following steps: obtaining multi-dimensional data of a flood control dam, and constructing a basic data set according to the obtained multi-dimensional data; constructing a preprocessing model according to the basic data set, and preprocessing the multi-dimensional data according to the constructed preprocessing model; constructing an intelligent detection and early warning model according to the preprocessed data, and carrying out detection and early warning decision multi-scene adaptive detection on the surface cracks of the flood control dam according to the constructed intelligent detection and early warning model; constructing a region-material-climate adaptive model cluster through federal learning and meta learning; the detection bottleneck of a traditional algorithm on special regions, extreme climates and special material dams is broken through, the cross-scene detection precision is improved by more than 30%, and the risk of missing detection and false detection is reduced.

The invention discloses a bridge scouring risk assessment method considering extreme climate influence. The method comprises the following steps: constructing a simulation model for simulating local scouring depth of a pier under different extreme working conditions; acquiring actual construction parameters of the current pier and local extreme climate types, and acquiring local scouring depth data of the pier in the acquisition time period; the pier local scour depth simulation model is adjusted according to the actual construction parameters, and pier local scour depth data and the extreme climate type serve as input and are used for conducting dynamic simulation according to the adjusted pier local scour depth simulation model; according to the dynamic simulation result, judging whether the current bridge pier has a bridge scouring risk under the influence of different extreme climate types, and generating an evaluation report; according to the method, the risk assessment report with high integrating degree with the target region pier and the extreme climate type can be generated, and the assessment reliability can be improved.

The invention relates to the technical field of medical cold chain transportation, in particular to a portable intelligent medical refrigerated transport box suitable for an extreme temperature environment, which comprises a box body, a heat insulation layer is arranged on the inner wall of the box body, and the box body is provided with a storage cabin and an electrical element mounting cabin. An energy supply module, an integrated information processing module and a temperature control system are mounted in the electrical element mounting cabin; an information display and control panel is arranged on the side wall of the box body; the integrated information processing module is used for collecting temperature data in the storage cabin and position information of the medical refrigerated transport box; the temperature control system is used for regulating and controlling the temperature in the storage cabin; according to the invention, the working temperature range is effectively expanded, the extreme temperature adaptability is effectively improved, and 99% of extreme climate regions in the world can be covered; the semiconductor refrigeration / heating cooperative control is combined with the precise regulation and control of the data processing module, so that the quick response to the temperature change in the box is realized, the response time is greatly shortened, the temperature fluctuation is reduced, and the activity and quality of medical supplies can be better guaranteed.

The invention provides a lightning fire occurrence probability prediction method fusing extreme climate factors, and relates to the technical field of forest fire prediction and risk assessment. The method comprises the following steps: collecting multi-source data such as thunder and lightning, extreme climate, weather, vegetation, soil and terrain, and constructing a balanced sample set; training an ignition probability model by adopting a random forest, XGBoost, a convolutional neural network and other models, and evaluating the performance difference before and after the extreme climate index is included; explaining variable contribution by using an SHAP method, and identifying a key driving factor; a risk grading grid map is generated according to the prediction probability, and the fire danger grade is automatically improved under the composite condition of heat waves and drought. The method has high precision and strong interpretability, and is suitable for fine prediction and prevention and control decision support of the occurrence probability of the lightning fire under the extreme climate background.

The invention relates to the technical field of cables, and provides a high-weather-resistance power cable. The high-weather-resistant power cable comprises a cable core and an outer sheath, the outer sheath is prepared from the following raw materials in parts by weight: 40 to 50 parts of low-density polyethylene, 25 to 35 parts of ethylene-vinyl acetate copolymer, 20 to 30 parts of ethylene propylene diene monomer, 10 to 12 parts of maleic anhydride grafted ethylene-vinyl acetate copolymer, 16 to 20 parts of ZrO2 coated modified hydrotalcite, 15 to 20 parts of magnesium hydroxide, 4 to 6 parts of silane coupling agent, 2 to 3 parts of dicumyl peroxide and 3 to 5 parts of zinc oxide; 6 to 10 parts of conductive carbon black, 0.8 to 1 part of antioxidant and 1 to 1.5 parts of zinc stearate. According to the cable prepared in the invention, the protection performance, the service life and the safety of an existing high-weather-resistance power cable under extreme weather conditions are improved, and the mechanical property of the cable is also improved.

The invention relates to the technical field of photovoltaic cells, in particular to a photovoltaic cell with an efficient sunlight utilization function. The photovoltaic cell comprises a photovoltaic unit; and the plurality of photovoltaic units are mutually spliced to form a spherical structure. According to the spherical photovoltaic cell provided by the invention, photoelectric conversion efficiency fluctuation caused by angle change of a traditional photovoltaic cell can be avoided while the spherical photovoltaic cell can receive sunlight irradiation in all directions. Due to the spherical design, the photovoltaic cell can effectively capture sunlight at any angle, so that the photovoltaic cell has remarkable advantages in a dynamic illumination environment. In addition, the spherical photovoltaic cell is flexible in structure, can obtain better adaptability in extreme environments such as seaborne and desert, and does not need to depend on an expensive sun tracking system. By adopting the spherical structure, the combination of a plurality of flexible photovoltaic cells can effectively disperse external pressure and vibration and improve the capability of resisting extreme climates such as wind and sand, so that the service life of the photovoltaic cells is prolonged.

The invention discloses a regional wind speed meteorological reference station representativeness evaluation method based on a correlation function field, a medium and a program product. The regional wind speed meteorological reference station representativeness evaluation method comprises the following steps: collecting multi-year day-by-day wind speed data of a high-density meteorological observation station in a target region and environment information of the observation point; selecting an independent sample of day-by-day wind speed data in each same time period in the region; establishing a correlation function field between every two stations, and fitting a relation model of correlation functions and distances; estimating a standardized observation random mean square error; calculating the relative standard deviation generated by replacing the target point wind speed truth value data with the reference station wind speed observation data; and evaluating the excellent grade of the spatial distance range represented by the wind speed of the reference station. The method can achieve the good representative evaluation of the reference station in the distance range, and has important practical values for the selection of a reliable meteorological reference station in the climate feasibility demonstration of wind power development site selection, urban and rural construction planning and the like, the evaluation of extreme climate risks and the calculation of the accuracy of engineering meteorological parameters.

The invention discloses a method for recycling and reusing road concrete, and relates to the technical field of concrete recycling, the method integrates service environment perception and an intelligent dynamic regulation and control mechanism, and effectively breaks the adaptive bottleneck of a traditional process to climatic difference demolitions; through deep coupling modeling of environmental parameters and aggregate characteristics, crushing process parameters can be dynamically optimized along with the service history of raw materials, and the performance stability of the recycled aggregate in a freeze-thaw / salt-wet alternating scene is remarkably improved; and an attention mechanism is introduced to strengthen correlation mapping of extreme climate characteristics and process parameters, so that grading adjustment better conforms to microscopic damage characteristics of the material.

The invention discloses an air-ground cooperative photovoltaic panel intelligent snow removal system, and relates to the technical field of intelligent control. A central intelligent scheduling management subsystem is used for receiving operation and maintenance requirements of a photovoltaic power station, planning a routing inspection path, receiving a routing inspection result, analyzing the routing inspection result, and generating an optimal snow removal scheme; the parking station unmanned aerial vehicle subsystem is used for collecting surface data of the photovoltaic panel according to the inspection path and analyzing the surface data of the photovoltaic panel by adopting an accumulated snow automatic identification model to obtain an inspection result; and the ground operation vehicle subsystem is used for cleaning the snow accumulation area according to the optimal snow removal scheme and uploading a cleaning result to the central intelligent scheduling management subsystem. The problems that an existing photovoltaic panel snow removal technology is low in efficiency, poor in adaptability and lack of air-ground cooperative linkage are solved, whole-process intelligentization of routing inspection, diagnosis, sweeping and dispatching is achieved, and the operation and maintenance efficiency and power generation stability of a photovoltaic power station under extreme climate and complex terrain are improved.

The invention is suitable for the crossing field of hydraulic engineering and climate science, and provides a risk evolution-based flood control benefit analysis method for a reservoir group to cope with extreme climate, and the method comprises the following steps: screening extreme climate elements through multi-source data fusion, and constructing a spatial-temporal feature model; extracting extreme climatic elements, inputting the extreme climatic elements into the pre-constructed hydrological model, simulating to obtain an initial flood process, constructing a flood database, training and adjusting hydrological model parameters, and inputting the extreme climatic elements again to obtain an ultimate flood process; flood risk data are collected, a risk assessment index system and a dynamic risk simulation model are constructed, an ultimate flood process is input to calculate flood control risks, and a multi-objective optimization algorithm is adopted to generate an optimal scheduling strategy; according to the method, a reliable basis is provided for scientific scheduling and flood control decision making of the reservoir group through innovative data acquisition and analysis means, advanced model construction and a multi-dimensional evaluation system, and the flood control benefit of the reservoir group under the extreme climate condition is improved to the maximum extent.