212 results about "Climate change" patented technology

Carbon Capture, Utilization, and Storage (CCUS) is a relatively new technology directed to mitigating climate change by reducing greenhouse gas emissions. Current and new government requirements require proof that carbon dioxide (CO2) is either sequestered in a stable form or safely stored for long periods of time. In instances when the CO2 is sequestered through mineral formation, the need for long-term monitoring can be reduced, as the stability of the sequestered CO2 is inherent based on a chemical change in subterranean rocks. The reactions between CO2 and rock formations are influenced by numerous factors, including temperature, pressure, fluid composition, and the mineralogy of the formation. Furthermore, these reactions occur over large spatial areas and long timescales, making them difficult to monitor directly. Methods and systems of the present disclosure, therefore, may use a combination of laboratory experiments, field monitoring, and modeling to provide convincing evidence of CO2 mineral sequestration.

The invention relates to the technical field of energy consumption simulation, in particular to a simulation method and system based on BIM building energy consumption, and the method comprises the following steps: importing structural data in a BIM building model, recording the actual sizes and material characteristic information of building walls, windows and floors, and generating a building structure parameter table through parameter extraction and data integration. According to the method, the BIM technology and the energy consumption simulation process are utilized, the accuracy of building energy consumption prediction and the practicability of operation are remarkably improved, the building structure parameter table is constructed, the thermal bridge effect is deeply analyzed, so that thermal performance data are more accurate, dynamic adjustment of actual climate change parameters is provided in thermal load simulation, the simulation adaptability is improved, and the simulation efficiency is improved. According to the method, the consistency of an energy consumption prediction result and an actual climate condition is ensured, the simulation precision and practicability are ensured through continuous matching and iterative optimization of actual data and a simulation result, powerful data support is provided for energy efficiency management and optimization, an energy-saving target is helped to be achieved, and the environmental performance of a building is optimized.

The invention discloses a method for quantifying the influence of climate change and human activity on watershed vegetation, which comprises the following steps: acquiring data of a research area from a plurality of data sources, performing time-space consistency processing, and calculating saturation vapor pressure difference; performing feature extraction on the processed data, and constructing a high-dimensional feature tensor; constructing a space-time diagram based on the high-dimensional feature tensor, performing dimension reduction and dynamic modeling by applying a map embedding technology, and analyzing space-time dynamic features of watershed vegetation; separating and quantifying direct influence of climate and human activities on vegetation by adopting a causal network and a regression decomposition method; comprehensive evaluation is carried out, and uncertainty analysis is carried out. According to the method, the ability of understanding the dynamic state of a complex ecosystem is remarkably improved, powerful scientific support is provided for watershed ecosystem management, and more accurate, reliable and comprehensive decision support can be provided.

The invention discloses a reservoir group delivery flow prediction method considering climate change and artificial regulation and storage. The method comprises the following steps: collecting hydro meteorological data of a research basin; determining an upstream inlet reservoir, an interval reservoir and an outlet reservoir according to the reservoir position distribution of the research basin; determining reservoir inlet flow calculation modes of the reservoirs at different positions and reservoir outlet flow models of the reservoirs; and for the outlet reservoir, according to the outlet flow of the upstream reservoir and the hydrometeorological data, the reservoir inlet flow of the reservoir is obtained through a vector autoregression model or a multiple linear regression model, the outlet flow simulation of the reservoir is carried out, and an outlet flow residual prediction model based on deep learning is established, so that the outlet flow prediction of the outlet reservoir is realized. According to the method, the influence of the artificial regulation and storage effect of the reservoir on the downstream reservoir is considered on the basis of the inflow and outflow hydraulic relation between the reservoir groups, and the outflow process of the downstream reservoir is predicted by combining the conceptual rainfall runoff model, the reservoir regulation model and the deep learning model.

The invention relates to the technical field of ecological environment monitoring, and discloses a regional ecological environment monitoring method and system based on remote sensing image processing, and the method comprises the following steps: collecting remote sensing image data and climate factor data of a target region; performing spatial gridding and standardization processing on the data; establishing a nonlinear regression model to analyze a relation between climate factors and vegetation indexes; extracting spatio-temporal topological characteristics of the climate factor data and identifying a change mode; long-term prediction of regional vegetation productivity is carried out through time sequence model training; the regional ecological environment is analyzed and evaluated based on the prediction result, and a scientific basis is provided for ecological environment monitoring; the system comprises a data acquisition module, a data processing module, a nonlinear regression modeling module, a spatio-temporal topology analysis module, a time sequence prediction module and an ecological evaluation module. According to the method, the problems of small data coverage, low prediction precision and insufficient capture of nonlinear influence of climate change in the existing method are solved.

The invention discloses a river micropollutant dynamic monitoring method and system based on artificial intelligence, and relates to the technical field of environment monitoring, and the method comprises the steps: building a micropollutant concentration prediction model according to the feature data and a particulate matter analysis module; adjusting model parameters of the micropollutant concentration prediction model according to climate change and use change of drainage basin land; and obtaining a concentration result of the micropollutants through the micropollutant concentration prediction model after the model parameters are adjusted, and performing graded early warning according to the concentration result and an early warning threshold value. Through continuous data acquisition and real-time analysis, the change of the concentration of the micropollutants can be found and responded in time, instant early warning information can be provided, and meanwhile, model parameters and early warning threshold values can be dynamically adjusted according to climate change and land use change, so that the system can adapt to the dynamic change of the environment.

The invention discloses a runoff change attribution nonlinear contribution identification method based on segmented hydrological simulation. The method comprises the following steps: dividing an annual runoff sequence into a natural period and a human activity influence period; selecting a watershed hydrological model, and respectively calibrating parameters of the watershed hydrological model in a natural period and a human activity influence period by using hydrometeorological data; analyzing the influence of climate change on runoff change; analyzing the influence of human activities on runoff changes; combining the influence of the climate change and the human activity on the runoff change, and analyzing the synergistic effect of the climate change and the human activity on the runoff change; and quantifying the contribution rate of climatic change, human activity and synergistic effect of the climatic change and the human activity to runoff change. According to the method, on the basis of runoff change attribution linear analysis, the synergistic effect of climatic changes and human activities is recognized in an emphasized manner, the recognition result can objectively reflect the nonlinear response of the hydrological process to environmental elements, and the method is effectively applied to the specific work of watershed water resource evaluation and management.

The invention discloses a vegetation-submerged embankment coupled tough coast protection method and system and a storage medium, and the method comprises the steps: firstly, determining a research region, and systematically collecting the near-shore terrain, water and sand power conditions, vegetation parameters and other data; secondly, constructing a water-sand-vegetation coupling numerical simulation model based on the collected data, and performing precision verification on the model; then selecting a suitable vegetation type in combination with environmental characteristics of the research area, carrying out artificial seeding and planting work in a vegetation potential habitat of the tidal flat, and determining a design elevation and an offshore distance of the submerged dike; and finally, evaluating the coast protection effect under the synergistic effect of the submerged embankment and the vegetation, and quantitatively analyzing the improvement degree of the coast protection effect on wave attenuation and beach stability. A sustainable tough coast protection system can be formed under the climate change background through a collaborative protection mechanism integrating natural vegetation and an artificial submerged embankment. The method has the advantages of low cost, remarkable wave reduction effect and the like, and the capability of coping with climate change challenges and comprehensive protection of coastal zones can be effectively improved.

The invention discloses an SSP scene reservoir group flood control scheduling method considering hydrological forecast uncertainty, and the method comprises the steps: obtaining SSP climate scene meteorological data to drive an SWAT model to generate daily runoff, and deducing and designing a flood hydrograph; a flow error sequence is calculated based on historical simulation and measured data, optimal distribution is optimized through fitting of a multi-probability distribution model, and a 95% confidence interval is generated through Monte Carlo simulation to correct design flood; and constructing an optimization model with the goal of minimizing the highest water level of flood regulation, bringing in various constraints, solving an optimal scheduling process by adopting a discrete differential dynamic programming algorithm, and finally refining an adaptive scheduling rule. The method solves the problems that prediction uncertainty is not considered and solving efficiency is low in the prior art, flood control safety and dispatching adaptability of the reservoir group are improved, and the method is suitable for flood control dispatching of the reservoir group under climate change.

The invention discloses a soil improvement measure decision-making method and system based on multi-modal deep learning. According to the method, firstly, soil state parameter change data and crop growth data of a target planting site are acquired, and a preliminary soil improvement scheme is established by combining the two data; then, a multi-modal deep learning network model is constructed, climate change characteristics and soil conditioner content change characteristics after improvement measures are implemented are fused and analyzed, and data characteristics of influences of climate changes on the conditioner are extracted; and based on the influence data, further identifying the failure characteristics of the modifier, including the failure propagation path and the dose change characteristics of the modifier. And finally, performing dynamic compensation on the original soil improvement measures according to the failure characteristics to form a refined compensation scheme. According to the method, intelligent adjustment and dynamic optimization of the soil improvement strategy can be realized, and the method has relatively high agricultural application value and popularization prospect.

The invention provides a pollutant diffusion prediction method, and relates to the field of machine learning. The regional climate change data pollutant diffusion prediction process provided by the invention comprises the steps of constructing a regional climate change data set, performing feature selection and layer normalization operation in combination with a Pearson's correlation coefficient, ensuring the correlation and distribution balance of input data, then constructing a plurality of depth coding modules, and predicting the pollutant diffusion of the regional climate change data. Each module comprises an sLSTM, a full-connection neural network and a ReLU activation function, a residual connection mechanism is introduced to capture a complex mode and a potential relationship in data, then the most significant features in a time sequence are extracted by using a maximum pooling technology to generate a final feature vector, and a final prediction result is obtained through the full-connection neural network. Through combination of feature selection, layer normalization, a depth coding module and a maximum pooling technology, the anti-noise capability and prediction precision of the model are effectively enhanced.

The invention belongs to the technical field of marine environment prediction, and discloses a near-shore sea wave forecasting method and system based on deep learning. The method comprises the following steps: constructing a near-shore adjacent wave forecasting model NWNN training data set based on deep learning based on open source environment data; a near-shore adjacent wave forecasting model NWNN is constructed; using PyTorch to train a near-shore adjacent wave forecasting model NWNN (Neighbor Wave Nearest Neighbor); and performing accuracy verification on a forecast result by using a buoy measured value which is not trained by the near-shore adjacent wave forecast model NWNN. According to the method, more accurate and reliable data support can be provided for prediction of marine weather events, so that marine economy and life and property safety of the masses can be guaranteed, and the trend and influence of climate change can be known.

Methods and apparatus operative to quantify building metrics using artificial assessment of an environmental impact, such as climate change and / or carbon control practices. A design plan is represented using multiple dynamic components. Each dynamic component may include a parameter changeable via the user interactive interface. The dynamic components may be arranged in a user interactive interface to form a first set of boundaries, including a respective length and area, and defining at least a portion of a first unit. AI may determine a materials list, methods of construction, distance form a supply source to a job site, supply chain variables, and other variables that are included in an environmental impact analysis. The AI may assess whether a building described in the design plans complies with a relevant environmental impact objective, such, for example, objectives relating to carbon footprint and / or climate change.

The invention discloses a method for analyzing a climate change rule based on remote sensing data. Multi-source remote sensing data are obtained through multiple platforms of satellites, aviation and ground, and after preprocessing of geometric correction, radiation correction and the like, features of surface temperature, humidity and the like are extracted through a split window algorithm, a polarization difference index method and the like. Principal component analysis is used for dimensionality reduction, partial least squares regression is used for determining the quantitative relation between features and climate changes, and a correlation model is established by considering human activity influences. The method comprehensively obtains data, improves the processing and analysis precision, establishes the relation between human activities and climate changes, can accurately determine the climate change rule, and provides powerful support for climate change research and response.

The invention relates to the related technical field of climate monitoring, in particular to a freezing circle climate change monitoring method and system suitable for an Asian alpine region, and the method comprises the steps: configuring a monitoring network, receiving a frozen soil multi-source monitoring data set, constructing a frozen soil and climate coupling response model, embedding a region climate assessment engine, and generating a frozen soil degradation risk grade distribution map. And judging whether risk reminding is triggered or not according to the dynamic frozen soil degradation threshold value, and if the risk reminding is triggered, marking an abnormal region in the frozen soil degradation risk level distribution diagram. The technical problems that Asian alpine region freezing circle climate change monitoring abnormity reminding mostly depends on static data, the dynamic evolution process of frozen soil cannot be accurately captured, and the frozen soil degradation risk identification precision is limited are solved, a frozen soil and climate coupling response model constructed based on multi-source data is achieved, and the frozen soil degradation risk identification accuracy is improved in combination with a dynamic degradation threshold value set according to the frozen soil type. And risk reminding is triggered in time and an abnormal region is marked, so that the frozen soil degradation risk identification precision is improved.

A method for spatial reconstruction and downscaling of all-sky surface temperature belongs to the field of remote sensing science and technology. It contains the following main steps: acquisition and preprocessing of multi-source remote sensing data and atmospheric driven data, spatial reconstruction of all-sky surface temperature data, and spatial downscaling of all-sky surface temperature data. The operability and universality of the present invention in practical applications will promote the popularization and application of surface temperature data in the fields of climate change, dynamic monitoring and assessment of hydrological and ecological environment, land surface radiation budget and energy balance, and further promote the development of thermal infrared remote sensing science.

A sunshade device and sunshade management system for mitigating the effects of climate change and direct and prolonged exposure to the sun, the sunshade device including a canopy attached to a collapsible web or frame structure. The frame structure can be collapsed or opened by operation of an electric motor. The sunshade device is positioned above the ground by electrically powered lifting devices that are powered by a battery system that is charged by solar cells. A sunshade management system controls the status of the sunshade device, and can collapse the frame and activate the lifting devices to position the sunshade in the sky, and open the frame and deactivate the lifting devices to allow the sunshade device to slowly descend while providing shade to areas below the sunshade device.

The invention belongs to the technical field of methane leakage monitoring, and discloses a method and a system for estimating methane leakage flux of an ocean station movable cold spring. The method comprises the following steps: acquiring physical states of bubbles at different depths of a station cold spring area and physicochemical properties of methane gas in the different depths of the station cold spring area, and obtaining a station cold spring plume three-dimensional static molar concentration model; calculating the molar flux of methane in a bubble state and the rising speed of bubbles to obtain the eruption rate of the seabed cold spring nozzle; and calculating the annual cold spring methane leakage flux of the station by combining the station cold spring plume three-dimensional static molar concentration model, the eruption rate and the bubble rising speed with the periodic leakage characteristics of the cold spring plume. The methane flux of the station cold spring leakage is 6.44 * 10 < 5 > mol / yr, which is greater than the previous estimation result. Previously, understanding of contribution of methane leakage of a cold spring system to climate change and carbon cycle is underestimated.

The invention relates to the technical field of civil engineering, and discloses a foundation pit simulation analysis method based on a finite element method. Comprising the following steps: S1, collecting foundation pit engineering data and environment information: collecting and storing real-time monitoring data of geological conditions, soil mechanical properties, underground water level, climate change and settlement of foundation pit engineering by using geological exploration equipment, an environment monitoring sensor, climate monitoring equipment, an underground water level sensor and a settlement monitoring sensor; and S2, establishing a multi-physics field coupling model of the foundation pit: according to the foundation pit engineering data collected in the step S1, providing a foundation pit simulation analysis system based on the finite element method, foundation pit simulation analysis equipment based on the finite element method and a computer readable storage medium. According to the method, by combining the finite element method, the multi-physics field coupling model and deep learning optimization, efficient and accurate foundation pit stability analysis is achieved, optimization suggestions are automatically generated, and the calculation efficiency, accuracy and real-time dynamic adjustment capacity are remarkably improved.

The invention belongs to the technical field of climate prediction, particularly relates to a Markov chain Monte Carlo-based paleoclimate inversion method, system and equipment, and aims to solve the problems of accuracy and high efficiency of tracking climate change details in global inversion. The method comprises the following steps: determining a to-be-inverted paleoclimate index; inputting the to-be-inverted paleoclimate index into a paleoclimate inversion model to obtain a paleoclimate inversion result output by the paleoclimate inversion model; wherein the paleoclimate inversion model is obtained by performing iterative matching based on a target curve of a paleoclimate index and an initial parameter set determined based on the target curve of the paleoclimate index so as to continuously update a to-be-inverted parameter set. According to the method, in global inversion, climate change details are tracked more accurately, calculation is more efficient, and the response capability and adaptability to the time sequence are improved.

The invention provides a fusion method of ground station monitoring and satellite remote sensing inversion PM2.5 data, and relates to the technical field of PM2.5 pollution prevention and control. Ground station data and satellite remote sensing data are subjected to time matching and space matching and then are fused by using a universal Kriging method to obtain a high-precision and large-area coverage PM2.5 product. The PM2.5 data spatial correlation is considered, so that the fusion result is closer to reality, the calculation complexity is reduced, and meanwhile, the reliability of the fusion result can be judged by giving an error after data fusion. According to the method, the limitations of insufficient spatial consideration and high calculation complexity of a traditional method are made up, and the PM2.5 product can be used for pollution process analysis, climate change and other researches.

The invention discloses a future grid population distribution prediction method, which comprises the following steps of: a) acquiring fine spatial-temporal scale population data in a historical period, and processing the fine spatial-temporal scale population data in the historical period to obtain a population elasticity coefficient; b) acquiring a ratio of crop yield or water resource quantity of each age in the future to a steady-state value of a reference year, and acquiring an environment variable under the influence of climate change; and c) according to the population elasticity coefficient and the environment variable under the climate change influence, generating future urban population and rural population distribution change based on the shared social economy SSPs and the shared social economy and representative forced path SSPs-RCPs composite scene, and finally synthesizing population distribution change. According to the future grid population distribution prediction method provided by the invention, the influence of the natural environment on the population distribution change is combined on the basis that the social and economic change influences the population distribution change, and the future grid population distribution can be predicted more accurately.

The invention discloses a lake and reservoir future carbon income and expenditure scenario prediction accounting method based on climate change influence, and relates to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: S1, collecting climate data, social economic data and lake and reservoir ecological data of a target lake and reservoir and a surrounding region, and carrying out the cleaning and missing value supplementation of the data; according to the method, the social economy-climate coupling model is constructed, social economy factors such as population growth and economic development and climate change factors are cooperatively incorporated into lake and reservoir carbon income and expenditure prediction, comprehensive simulation of lake and reservoir carbon income and expenditure multi-driving factors is realized, the fitting degree of a prediction result and an actual situation is improved, and the prediction efficiency is improved. By setting multiple groups of social economy-climate scene combinations, lake and reservoir carbon income and expenditure prediction results under different development paths are output, a carbon income and expenditure accounting sub-model based on a process mechanism is embedded, and model parameters are verified and optimized in combination with historical data, so that the accuracy and reliability of lake and reservoir carbon income and expenditure prediction accounting are further improved.

The embodiment of the invention discloses a quantitative evaluation method for the influence of climate change and human activity on land water reserves, and relates to the technical field of geographic information, and the method comprises the steps: obtaining first TWSA data and climate-driven remote sensing data related to climate factors; reconstructing TWSA data in combination with a gradient lifting regression model, and outputting climate-driven second TWSA data; verifying the second TWSA data, and extracting third TWSA data related to the human activity factors through a separation method under the condition that the verification is passed; and respectively calculating composition contribution rates of the second TWSA data and the third TWSA data, and determining main factors for driving land water reserves to change. According to the embodiment of the invention, the direct driving effect of the climatic factors on the TWSA data is comprehensively considered, the dynamic relationship between the climatic factors and the TWSA data is combined, and a scientific basis can be provided for quantifying the relative contribution of the climatic factors and human activity factors.

Climate change poses a significant threat to human well-being, the planet's sustainability, and the global economy, mainly caused by excessive greenhouse gas emissions, particularly CO2. To combat this phenomenon, various tools have been adopted, with the most effective being the carbon emission trading scheme established by the United Nations which created a system that monetizes and reduces CO2 emissions. However, the current system faces multiple challenges, such as manipulation, complexity, and a lack of integrity, preventing it from achieving its ultimate objectives. To tackle these issues, blockchain technology has been proposed due to its distinguishing characteristics of security, traceability, decentralization, and trust. However, blockchain frameworks existing in the literature have been inefficient and inadequate. This research aims to design and develop a practical blockchain system that employs smart contracts and Internet of Things (IoTs) with the use of oracles to solve the problems facing the present carbon emission trading scheme and elevate its operations. The developed smart contracts automate the participating entities' registration and suspension processes and emitters released CO2 measurements as well as carbon credit assignment and trading operations. Our smart contracts were simulated, tested and analyzed where results proved the efficiency, integrity, traceability and security of all processes under the carbon trading market at a reduced cost.

The invention relates to the technical field of water and soil loss treatment, and discloses a water and soil loss treatment method and system based on a wind erosion area, and the system comprises a multi-dimensional monitoring module and an intelligent management module. According to the system, environment data and climate data of all areas are obtained through a multi-dimensional monitoring module and are classified to form a data set, an intelligent management module analyzes the water and soil loss degree of each area, corresponding earth surface exposure difference and erosion coefficient are generated, dynamic quantitative indexes are synthesized, and the water and soil loss degree is calculated. And the intelligent management module analyzes the climate change trend of each region, generates a corresponding wind power fluctuation index and a monitoring data set, quantifies the wind power fluctuation intensity, pays attention to individual differentiation, and obtains the corresponding wind power fluctuation index and monitoring data set. And the intelligent management module evaluates the water and soil loss degree, the wind power change degree and the climate change degree, outputs corresponding treatment decisions, realizes partitioned policy application, establishes an emergency protection mechanism, and is high in resource allocation efficiency and good in dynamic treatment effect.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention discloses an elastic city rainwater garden layout optimization method and system self-adaptive to climate change, and relates to the technical field of sponge city drainage optimization. Multi-dimensional data are collected in real time through an ultrasonic liquid level sensor, an electromagnetic flowmeter and a meteorological radar, a physical model is constructed, and a weighted flow value is generated in combination with LSTM; intelligent prediction under the constraint of the physical law is achieved, threshold matching and deviation correction are conducted based on the weighted flow value, and the pipe diameter adjusting amount is calculated; and constructing a three-color matrix execution feedback strategy containing prediction precision, drainage efficiency and equipment loss. The problems that a traditional scheme is poor in dynamic adaptability, single in model and free of closed-loop feedback are solved, and the elasticity of a drainage system to cope with climate changes is improved.

The invention discloses a tough city evaluation method based on climate and biodiversity coupling and application. The evaluation method comprises the following steps: S1, determining a biodiversity index of a city to be analyzed; s2, based on a weight prediction model, determining a climate partition weight coefficient of the biodiversity index in a future time period; s3, an index evaluation value BRI is obtained through calculation by means of the biodiversity index and the climate partition weight coefficient, and the larger the index evaluation value BRI is, the higher the city toughness is; according to the method, the climatic factors and the biodiversity are subjected to coupling analysis, the weight prediction model is established to predict the biodiversity index in the future time period, the toughness of the urban biodiversity can be quantified, the limitation of static evaluation of an original evaluation method is solved, accurate evaluation of climatic partition adaptation is realized, and the evaluation efficiency is improved. A tough city evaluation system with better ecological adaptability is constructed, and a scientific tool is provided for global cities to cope with climate changes.

The present invention pertains to optical wave guides (ie structures that guide waves by restricting the transmission of energy to one direction) buoys, anchors, and climate change. The Algal Wave Guide directs energy to Algae in maritime and lake environments in order to expand the viable range of highly productive carbon sequestering environments such as the Brown Algae among others. Sequestering carbon at sea has several advantages over terrestrial methods such as lower cost, surface availability compared to land based real estate, reduced infrastructure, increased fishery quotas, commercial chemical and lubricant production, tourism, fertilizer inputs, etc.