2235 results about "Extreme weather" patented technology

The invention provides an urban intelligent drainage management system based on big data analysis, and relates to the technical field of urban drainage management, and the system comprises a multi-source data collection module which collects historical climate changes, rainfall curves, satellite meteorology, pipe network node water level flow and topological structure data in real time; the dynamic three-dimensional modeling module constructs a pipe network three-dimensional model according to a fluid mechanics principle, and binds real-time sensor data by a spatio-temporal data fusion algorithm; the drainage limit analysis module calculates the maximum drainage threshold value and the failure probability of the node under extreme rainfall; the intelligent early warning module fuses LSTM and ARIMA models, generates a drainage pressure thermodynamic diagram in the future 72 hours in combination with satellite meteorological data and triggers early warning; the health assessment module generates a node health index and a maintenance priority order; the resource scheduling engine generates a dynamic scheduling scheme. The urban drainage problem is effectively solved, the ability of a drainage system to deal with extreme weather and pipe network aging problems is improved, and accurate early warning and efficient resource allocation are achieved.

The invention provides a power prediction precision improvement method and system for a wind and light station, and relates to the technical field of new energy power generation, and the method comprises the steps: obtaining the historical power, weather and equipment operation data of the wind and light station through a data preprocessing module, and carrying out the time sequence decomposition, spatial interpolation, abnormal value detection and correction, and obtaining the fusion feature data. And constructing a deep learning prediction model based on the fused feature data, extracting spatial features by using a convolutional neural network, processing the spatial features by using a long-short-term memory network to obtain an initial prediction result, introducing an attention mechanism to perform weight allocation to obtain weighted features, and combining to obtain power prediction data. And judging the weather type according to the real-time meteorological condition, and correcting by adopting the matched correction model when the extreme weather is identified. And finally, an error compensation model is constructed based on historical prediction errors for compensation, a final power prediction result is obtained, error correction is performed according to actual power data, and compensation model parameters are updated.

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

The embodiment of the invention discloses a method for determining wind power in different weathers. The method comprises the following steps: collecting historical weather data and carrying out clustering classification on the historical weather data to obtain wind profile index data, and carrying out data enhancement on the wind profile index data to obtain a first data set; determining a plurality of wind profile indexes according to the first data set, and determining a corresponding wind profile index under an extreme weather condition according to a nonlinear relation model of the plurality of wind profile indexes and meteorological parameters; determining a dynamic prediction model of the wind profile index according to the corresponding wind profile index under the extreme weather condition by using a bidirectional long-short-term memory network; collecting ground wind speed data in real time, and determining the wind speed of the hub height of the wind turbine generator according to the dynamic prediction model of the wind profile index; determining power output curves at different wind speeds according to the corresponding relation between the historical ground wind speed data and the historical wind turbine generator power output data; and determining the wind power corresponding to the wind speed of the hub height of the wind turbine generator according to the power output curve.

The invention relates to the technical field of electrical system evaluation, in particular to a toughness evaluation method of a novel power system in extreme weather, which comprises the steps of constructing a digital twin model, collecting data in real time, performing fusion processing on multi-source data, identifying potential risks of the power system in extreme weather, determining system vulnerability and constructing an extreme weather scene. The method comprises the following steps: simulating weather events possibly occurring in the future, predicting the occurrence probability and influence range of extreme weather, analyzing system vulnerability, identifying key nodes and vulnerable equipment, defining and calculating a toughness index, optimizing an operation strategy, executing an emergency plan when the extreme weather occurs, evaluating a plan execution effect, and continuously optimizing the plan through feedback; according to the method, the dynamic response capability and the toughness evaluation accuracy of the power system in extreme weather are remarkably improved, the actual operation state of the power system is reflected in real time, the model parameters can be dynamically adjusted through the digital twin technology, and the real response of the system under the changing condition is simulated.

The present invention relates to the technical field of power system risk assessment, and provides a power system risk assessment method in extreme weather based on deep feature mining. The risk assessment method comprises: performing regional division on the whole power system on the basis of the geographic location; on the basis of the historical wind speed data, constructing a correlation model of the strong wind extreme weather in a plurality of regions of the power system; constructing a strong wind scenario sample set of each region on the basis of the correlation model; acquiring the power transmission line fault probability of the corresponding region under each strong wind scenario in the strong wind scenario sample set; randomly allocating a power system operation condition to each strong wind scenario of each region, and acquiring an operation risk value of the power system under the corresponding operation condition; constructing and training a risk assessment model; and using the risk assessment model to perform power system operation risk assessment. The present invention can implement real-time and accurate assessment of the power system operation risk, and can provide an auxiliary decision-making suggestion for the power system.

The invention relates to the technical field of electric power energy storage system planning analysis, and discloses an electric power system planning operation optimization method, system and device and a storage medium. The method comprises the following steps: collecting electrical and equipment states and environmental parameters for feature extraction, and evaluating the health state of equipment; grouping the distributed energy sources based on health state evaluation and weather early warning information to form a virtual power plant resource pool; performing task decomposition according to the characteristics of the resource pool, and formulating a collaborative scheduling strategy; arranging a maintenance plan according to the equipment state and the scheduling strategy; and optimizing system operation parameters based on the scheduling strategy and the maintenance plan to obtain a balance control scheme. According to the invention, under the condition of considering the health state of the equipment and the influence of extreme weather, the dynamic balance of the minimization of the operation cost of the power distribution network and the maximization of renewable energy consumption is realized through the intelligent arrangement technology of the virtual power plant group.

The present invention discloses an intelligent measurement and control system for a withdrawable switch cabinet and a control method thereof, which relate to the technical field of automatic control of electric power systems, and solve the problems of slow response speed, inflexible control strategy and difficulty in coping with extreme weather conditions in existing intelligent measurement and control systems when responding to changes in power grids; real-time data acquisition and analysis are realized by integrating an edge processing module, and state prediction and anomaly detection are performed in combination with a long short-term memory network; an adaptive control module is used to optimize the control strategy according to dynamic monitoring results; at the same time, the emergency response module monitors environmental variables through a SCADA system, and an intelligent scheduling algorithm adjusts the switch cabinet operation mode and switches standby equipment; the present invention greatly improves the safety and reliability of the switch cabinet, significantly enhances the rapid response capability and adaptive control capability of the power system to changes in the power grid, and ensures the stability and safety of the power supply.

The invention discloses an urban life body full-cycle monitoring system based on a digital twinborn technology, relates to the technical field of urban governance, integrates multi-source data such as GIS data, remote sensing information, surface permeability data and an underground drainage pipe network structure, quantifies a flood high-risk area, and provides an urban life body full-cycle monitoring system based on the digital twinborn technology. The AI flood prediction module deduces the ponding range and the drainage capacity under different rainfall situations by adopting extreme weather analogue simulation based on a historical rainfall trend, a current meteorological condition and a drainage network state, and dynamically adjusts a flood diffusion trend prediction model, so that the flood risk identification is more accurate; the intelligent emergency scheduling module automatically matches flood control resources, combines flood high-risk areas, ponding point locations and traffic flow analysis, optimizes a drainage pump station start-stop strategy, remotely regulates and controls an underground drainage gate, dynamically adjusts the overflow adjustment capability of a sewage treatment plant, and optimizes an emergency risk avoiding route in combination with an intelligent traffic management system. And the accuracy and the execution efficiency of the flood control scheduling scheme are improved.

The invention relates to the field of power grids, and particularly discloses a multi-type extreme weather oriented power system risk assessment method and device. Comprising the steps of defining risk scene elements, determining a risk scene judgment confidence value based on a threshold value under a power grid normal operation condition, constructing a full-scene comprehensive evaluation framework, establishing an extreme weather occurrence probability model, performing generation and screening by using an approximate daily search method and an approximate distance method, and obtaining a source load power probability model. A fault probability model under the influence of different factors is constructed, a series model is utilized, a comprehensive time-varying fault probability model is determined, a power grid fault state occurrence probability model is obtained, comprehensive analysis is carried out, a risk scene probability model is obtained, and a risk consequence index system is established according to a power grid system operation potential risk. And inputting the target power grid data into the risk scene probability model, and performing evaluation by using the risk consequence index system to obtain a risk evaluation result. And the disaster response capability of various aspects of a future power system can be improved.

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 a power load prediction method and device based on multi-factor influence, and particularly relates to the technical field of power load prediction. Historical weather data and power load data are collected, preprocessing and feature extraction are carried out, temperature change deviation features and power load lag features are extracted and input into a prediction model for training, the accuracy of a prediction result is evaluated, and when the prediction accuracy is low, the prediction result is subjected to prediction. The abnormal degree is further analyzed and predicted, and the power dispatching strategy is adjusted in time, so that the violent fluctuation of the load demand is dealt with, the stable operation of the power grid is ensured, the accuracy of power load prediction is effectively improved, and the risk of overload or collapse of the power grid is reduced especially in the case of extreme weather.

The invention relates to the technical field of power distribution network recovery, in particular to an urban power distribution network toughness recovery management system under extreme weather, which comprises the steps of acquiring voltage, current, temperature, humidity and wind speed in real time, cleaning, formatting and standardizing data, identifying abnormal conditions influencing the stability of a power grid, evaluating the performance of the power grid under the extreme weather, and managing the toughness of the power distribution network under the extreme weather. An optimization suggestion is provided, various extreme weather scenes are simulated, feedback data are generated, comprehensive risk assessment is carried out, a coping strategy is generated, a fault point needing emergency processing is identified, recovery resources are dispatched, and a result is analyzed in real time to dynamically optimize an operation strategy of a power grid; according to the invention, by introducing the scene simulation and virtual training module, complex dynamic scene simulation is carried out, and coping strategies are verified and optimized. Feedback data generated by the system in simulation is used for further optimizing strategies in the toughness evaluation and decision support module, and adaptability and effectiveness of the strategies in actual extreme weather events are ensured.

The invention provides a multi-mode tailing pond dam break digital twinning emergency deduction and decision optimization method, which comprises the following steps of: firstly, constructing a high-precision three-dimensional scene model, performing simulation calculation on the high-precision three-dimensional tailing pond model based on a finite element model to obtain a dam body stress-strain field, and performing simulation calculation on the dam body stress-strain field based on ANSYS + reinforcement learning. And predicting a dam break probability distribution diagram by using the dam body stress-strain field and the current environment state, generating a rescue scheduling scheme based on the dam break probability distribution diagram and the resource distribution data, building a dynamic virtual deduction scene for exercise, and verifying and optimizing the rescue scheduling scheme according to exercise record data. And a final rescue scheduling scheme is obtained. According to the method, the ANSYS + reinforcement learning hybrid engine is utilized to reinforce the deduction precision, dam break path prediction, optimal resource scheduling and plan self-optimization closed loop are realized, the problems of data fusion distortion, deduction stiffness, decision lag and the like in the traditional technology are solved, and the method is particularly suitable for variable working conditions such as tailing pond seepage field sudden change and extreme weather.

The invention provides a cross-domain target detection method and system based on semi-supervised learning in extreme weather, and the method comprises the steps: enabling a student model to learn bounding box regression knowledge of a trained teacher model through distillation, so as to achieve the regression capability in extreme weather; performing instance-level feature adversarial training on a pre-training detection model which is obtained in a distillation stage and has a regression capability in extreme weather by using an intermediate-state image which is generated by a diffusion model and is closer to a target domain and has smaller noise, and determining a new teacher model and a new student model based on the detection model after the adversarial training; in the fine tuning stage, a dynamic category pseudo label screening strategy is utilized to screen pseudo labels generated by a new teacher model, and a screened target domain pseudo label is utilized to train a new student model to obtain a target detection model used for detecting a to-be-detected image in extreme weather. The phenomena of leak detection and error detection in extreme weather scenes in the prior art can be reduced, and the original reasoning speed is kept.

The invention discloses a multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method and system. The multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method comprises the steps of obtaining freight basic elements, constraint conditions and real-time environment information, and assigning a matched number of unmanned transfer vehicles and unmanned aerial vehicle sets; generating an initial optimal path; and the local path of the initial optimal path is adjusted in real time, the unmanned transfer vehicle and the unmanned aerial vehicle set advance to the unloading destination according to the global optimal path, and multi-vehicle cooperative cargo unloading is completed according to the conflict-free motion trail. A multi-machine cooperation technology is adopted, advanced technologies such as unmanned driving, the Internet of Things and an artificial intelligence algorithm are fused, the unmanned transfer trolley, the unmanned aerial vehicle and the intelligent gantry crane can work efficiently and cooperatively, various operation scenes such as indoor and outdoor switching, place layout difference and extreme weather can be dealt with, cargoes can be rapidly and accurately received, unloaded and transferred, and the working efficiency is improved. And the efficiency of multimodal transport is greatly improved.

The invention discloses a cross-platform power transaction data interaction optimization method, and particularly relates to the technical field of power transaction data digital twinning. Simulation transaction data of market participants is generated based on an antagonistic neural network model; identifying a causal relationship among market variables from the simulation transaction data of the digital twin through a causal discovery algorithm, and constructing a causal graph; modeling spatial dependence and causal conduction paths among market participants by using a graph neural network model, and performing anti-factual reasoning in a single external impact scene in a digital twinborn body by intervening key variables in a causal graph; simulating dynamic game behaviors of market participants in an extreme weather parameter distribution scene; the power market failure assessment is carried out by combining the causal conduction path to obtain the power market failure assessment index, and early warning is carried out based on the power market failure assessment index, so that the adaptability and stability of the power market in extreme weather scenes are improved.

The invention relates to the field of power system safety monitoring, and discloses a power transmission line vibration monitoring and early warning method for dealing with extreme weather, which comprises the following steps: arranging a multi-level sensor network to monitor vibration and environmental conditions of a power transmission line; monitoring data are processed and transmitted in real time through edge calculation and a 5G network; performing trend prediction on the vibration data by using a multi-modal deep learning model; an early warning threshold value is adjusted through a distributed self-adaptive dynamic threshold value control method; and when abnormal vibration or a high-risk trend is detected, generating and issuing early warning information. Through a multi-level sensor network, an intelligent early warning algorithm and dynamic threshold control, efficient monitoring of power transmission line vibration and accurate early warning in extreme weather are realized, the real-time performance of data transmission and processing is effectively improved, the accuracy of vibration trend prediction is enhanced, the false alarm rate and the missing report rate are reduced, and the method is suitable for popularization and application. Therefore, the method has higher adaptability and stability in a complex environment.

The invention provides a new energy output prediction method and system based on Monte Carlo Dropout, and the method comprises the steps: carrying out the probabilistic prediction of new energy output through a Monte Carlo Dropout technology, generating a dynamic prediction result containing a confidence interval, and quantifying the uncertainty of meteorological sudden change and equipment state; secondly, constructing a multi-stage random dynamic programming model, discretizing a prediction interval into multi-scene input, designing a non-linear objective function based on the discharge depth, and synchronously optimizing the electricity purchase cost and the energy storage aging cost; and finally, realizing rolling optimization of the system in combination with a model prediction control framework, and dynamically adjusting an energy storage aging cost weight by updating prediction data and a scheduling instruction on line and embedding an energy storage health state real-time feedback mechanism. The photovoltaic and wind power consumption rate can be remarkably improved, the full life cycle cost of an energy storage system is reduced, and meanwhile, the robustness of a scheduling strategy in extreme weather is ensured.

The invention discloses an AI-NWP three-dimensional closed-loop bidirectional dynamic feedback coupling method for extreme rainfall event area simulation and a program product, and belongs to the technical field of meteorological numerical simulation and artificial intelligence fusion. According to the method, a mid-term forecast field is generated based on an AI global weather prediction model, an analysis field is constructed by assimilating multi-source observation data, and a high-resolution NWP region mode is driven to perform rolling simulation. Furthermore, a space-time residual field is constructed by using the difference between an NWP region simulation result and live data, a residual learning model is trained, an AI model prediction structure is fed back and corrected, and dynamic weight adjustment updating of the AI prediction model is realized. A closed-loop two-way feedback system among AI output, NWP simulation and residual evaluation is integrally formed, the space structure reduction capability and the area positioning precision of a medium-term heavy rainfall event are effectively improved, the continuous predictability and the simulation credibility of an extreme weather process are remarkably enhanced, and the method has good stability, universality and engineering expansion value.

The invention provides a sunlight greenhouse intelligent environment prediction and regulation method comprising the following steps: collecting greenhouse data and meteorological data outside a greenhouse, combining to form a greenhouse environment data set, and carrying out data noise processing, screening, correlation analysis and normalization processing to obtain a prediction data set; constructing and training a greenhouse temperature and humidity prediction model comprising an input layer, a convolutional neural network layer and a gating circulation unit neural network layer; inputting the prediction data set into a greenhouse temperature and humidity prediction model, and training, verifying and adjusting hyper-parameters of the greenhouse temperature and humidity prediction model to obtain a greenhouse temperature and humidity prediction result; and constructing a greenhouse environment control module, and adjusting the greenhouse environment based on the optimal temperature and humidity information of crop growth and the greenhouse temperature and humidity prediction result. According to the intelligent prediction and control method, the greenhouse control precision and management efficiency can be remarkably improved, modernized and intelligent development of agricultural production is achieved, crop growth is promoted, and losses caused by extreme weather are reduced.

The invention relates to a data and model fused tower crane displacement response prediction method under the typhoon action, which comprises the following steps: constructing a monitoring system, acquiring actual measurement data of a construction site and a tower crane, and constructing a tower crane finite element model by utilizing a design parameter set of the tower crane; the extreme wind speed under the most unfavorable wind direction of a construction site is obtained based on typhoon full-path simulation and wind field simulation, and the tower crane finite element model is calibrated by analyzing a parameter object needing to be calibrated in the tower crane finite element model through sensitivity, so that the calibrated tower crane finite element model can accurately reflect the response condition of a tower crane structure; a calibrated tower crane finite element model is used for simulating a data set formed by displacement data under the typhoon working condition to serve as a training set, the defect of an extreme weather sample in an actually-measured training sample is overcome, then response prediction of tower crane displacement under the typhoon working condition is achieved by training a CNN-BiLSTM-Adaboost displacement response prediction model, and the method is simple and convenient to implement. The precision and robustness of tower crane displacement prediction under the extreme typhoon working condition are remarkably improved, and sufficient emergency processing time is reserved for a construction site.

The invention discloses a land-sea-air-space holographic sensing and collaborative decision-making system based on multi-mode edge intelligence. The system comprises a sensing layer which is used for carrying out three-dimensional monitoring on a ship driving process, the sea, the sky, a shore base and a space formed by the ship driving process, the sea, the sky, the shore base and the sea, the sky and the shore base; the network layer is used for constructing a global communication basis, preprocessing monitoring data, judging whether the ship has an abnormal condition or not, and realizing data transmission of the ocean, the sky, a shore base and a space formed by the ocean, the sky and the shore base; and the application layer is used for judging whether an abnormal condition exists in the ship driving process or not based on the data of the ocean, the sky, the shore base and the space formed by the ocean, the sky and the shore base transmitted by the network layer, performing collision early warning decision based on the abnormal condition, and performing dynamic channel capacity prediction. And navigation guidance and route tracking in extreme weather are carried out based on environmental risk early warning, underwater obstacle collision is avoided, and early warning information and decision information are fed back to a sensing layer.

The invention provides a solar power generation power real-time prediction method, system and device based on multi-mode deep learning and a storage medium. Wherein the prediction precision is improved by fusing multi-dimensional features of earth surface radiation, equipment temperature and environmental data, mutation fluctuation decomposition is performed on long and short wave components of earth surface solar radiation, and a frequency domain sub-modal sequence is extracted; constructing a correlation model of a photovoltaic module temperature field and hot spot infrared data, and generating a dynamic feature map reflecting hot spot interference space distribution; establishing a multi-mode time sequence prediction model, and synchronously processing frequency domain features of radiation sub-modes, spatial correlation of hot spot feature maps and time sequence dependence of temperature and humidity data; and integrating the prediction results of the sub-modals through a frequency domain superposition reconstruction technology. According to the technical scheme provided by the invention, through multi-modal spatial-temporal feature joint modeling and dynamic interference decoupling, the power prediction precision in extreme weather and hot spot abnormal scenes is remarkably improved, and meanwhile, the dynamic adaptive capacity of a prediction system to complex environment disturbance is enhanced.

The invention discloses a time-space characteristic analysis method for meteorological new energy load big data, and belongs to the technical field of new energy power management, and the method comprises the steps of data fusion alignment, physical causal modeling, extreme weather attribution and time-space characteristic analysis. According to the method, time-space characteristic analysis is performed by combining physical causal modeling and extreme weather attribution, the prediction precision of new energy load fluctuation under daily weather is improved, and load abnormity caused by extreme weather can be effectively identified and coped with; a double-flow space-time causal graph network model is adopted to perform physical causal modeling, and through physical constraint expression of wind power and photovoltaic characteristics, causal modeling of load change association between power stations and a dynamic feature fusion prediction mechanism of the wind power and photovoltaic characteristics, the load prediction accuracy, stability and scheduling practicability are improved; extreme weather attribution is carried out by adopting an attribution method combined with an improved disturbance response ratio, the actual influence of the extreme weather on the new energy load is accurately identified, and the interference degree of different types of weather events on the load is quantified.

The invention discloses a risk level prediction method and device for a power distribution network, and electronic equipment, and relates to the technical field of power distribution network operation and maintenance or other related fields, the method comprises the following steps: obtaining current operation data of a target power distribution network, the current operation data being acquired by a sensor network in real time, comprising line equipment operation data of the target power distribution network, geographical environment data of the position where the target power distribution network is located and disaster weather data; all the current operation data are input to a target model, the risk prediction level and early warning information are output, the target model is a decision tree model pre-established based on a random forest algorithm and is used for extracting key features from the line equipment operation data, the geographical environment data and the disaster weather data, and therefore risk level study and judgment are carried out; and early warning information is generated according to different risk prediction levels. According to the invention, the technical problem of low power distribution network risk prediction accuracy in extreme weather in the prior art is solved.

The invention discloses an intelligent power grid new energy consumption management system, and relates to the technical field of intelligent power grids, and the system comprises the following components: bidirectional interaction mechanism construction, an intelligent decision engine, a collaborative optimization scheduling strategy and user participation and incentive mechanism, and visual monitoring and operation and maintenance management. According to the invention, a two-way interaction mechanism between the micro-grid and the main network is constructed, rapid transmission and sharing of data are realized, a scheduling strategy can be rapidly adjusted under emergency and extreme weather conditions through collaborative optimization of the scheduling strategy, continuity and stability of power supply are guaranteed, and meanwhile, the system is convenient to use. The visual monitoring and operation and maintenance management module provides visual system operation state display and intelligent operation and maintenance management, and improves the operation and maintenance efficiency and safety, thereby remarkably enhancing the safety and stability of the power grid and the capability of coping with emergencies.

The invention discloses an operation state fault monitoring method, system and device for a heavy-load overhead line, and relates to the technical field of electrical fault detection of the heavy-load overhead line. The method comprises the following steps: injecting a high-frequency test signal into a heavy-load overhead line to carry out predefined fault detection so as to obtain a total harmonic distortion rate; performing first harmonic optimization adjustment according to comparative analysis of the total harmonic distortion rate and a threshold value; performing harmonic second optimization adjustment according to the monitoring analysis result of the external load fluctuation of the heavy-load overhead line; and performing harmonic third optimization adjustment according to the monitoring analysis result of the external environment fluctuation of the heavy-load overhead line. According to the invention, through multi-stage distribution adjustment, the effect of improving the dynamic regulation and control accuracy of the high-frequency test signal injection fault monitoring method of the heavy-load overhead line in extreme weather is achieved; the problem of insufficient dynamic regulation and control accuracy of a high-frequency test signal injection fault monitoring method of a heavy-load overhead line in extreme weather exists.

The invention relates to the field of weather forecasting, and discloses a marine meteorological coupling refined forecasting method and system, which are used for improving the precision and reliability of marine meteorological coupling forecasting. Comprising the following steps: based on a non-exchangeable geometric vortex equation and an external differential form, constructing a conservation manifold dynamic field in combination with submarine topography data, and realizing strict conservation of mass flux under a complex terrain; an additional pressure item caused by vacuum fluctuation is quantified, and an interface energy field of quantum correction is generated; the recognition precision of the mesoscale vortex and the frontal surface structure is improved; a spectral element method and lattice Boltzmann method mixed solver is combined, and high-precision coupling of ocean vertical layering and the atmospheric process is achieved. According to the method, the defects of conservation, interface energy transmission, initial field non-physics and grid adaptive capacity of a traditional method are overcome, refined forecasting of 72-hour typhoon paths, ocean frontal surfaces and energy flux is supported, and key technical support is provided for extreme weather early warning and climate change research.