1454 results about "Weather data" patented technology

The invention belongs to the technical field of intelligent health monitoring, and discloses a retired old people home safety monitoring system based on Internet of Things, which comprises the following steps: deploying multiple types of sensors for the home environment of old people to carry out all-weather data acquisition and analysis to obtain behavior data with activity feature tags; performing correlation analysis to obtain a personalized security risk assessment model; performing real-time monitoring and mode recognition to obtain a behavior track analysis report; performing security level division and intelligent configuration to obtain a virtual security guard area and a self-adaptive reminding rule; performing grade classification processing and early warning signal generation to obtain graded early warning information and a response authority strategy; performing integration analysis to obtain a dynamic intervention strategy, and updating the personalized security risk assessment model according to the dynamic intervention strategy to obtain a target security monitoring model; blind spots and defects of traditional manual monitoring are effectively overcome, and a safe home environment is provided for the old.

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention discloses a multi-source multi-temporal-spatial feature fusion short and temporary rainfall prediction method (M4Cester), which realizes layered temporal-spatial feature extraction through a multi-space multi-time aggregator (MMA), and comprises a multi-scale patch embedding (MSPE) module, a cross-scale perception optimization (CPR) module and a multi-time self-attention (MTS) module. The first and second spatial features are respectively used for capturing local-global spatial features, dynamically balancing cross-scale information and mining a time sequence dependency relationship; meanwhile, a bidirectional bridging fusion module (MSFM) is designed, bidirectional alignment and enhancement of radar and satellite features are realized by using a cross attention mechanism, and modal difference is relieved through residual connection. Experiments on a weather data set in the Yangtze River Delta region show that the key success index (CSI) reaches 0.267 and the HSS reaches 0.372 in heavy rainfall (greater than or equal to 50dBZ) prediction by the method, which are obviously improved compared with the existing advanced model, particularly, the limitation of single-source data is effectively overcome in the prediction of a convection initiation (CI) event, and a high-precision solution is provided for short and temporary rainfall prediction.

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 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 a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention provides an expressway variable speed limit control method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: collecting traffic flow data, road condition data and weather data in real time; preprocessing the collected data, and predicting a traffic jam trend and a safety risk index through a machine learning algorithm; determining weight factors of traffic flow, safety risk and congestion prediction through an analytic hierarchy process; dividing a road section speed range by using a clustering algorithm, and calculating a road section unit speed; constructing a target function which aims at maximizing the unit speed of the road section and minimizing the safety risk and the congestion time; and based on the target function, solving an optimal speed limit value by adopting a particle swarm optimization algorithm, and dynamically issuing speed limit information in different lanes. The highway traffic big data is analyzed in real time, the speed limiting strategy is intelligently formulated, the speed limiting strategy is published to the driver in real time through multiple channels, and the passing efficiency and safety of the highway are effectively improved.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention provides a flexible preparation system and method for sustainable aviation fuel, and the system comprises a green power supply module which is used for outputting fluctuating green electric energy; the process execution device is connected with the green power supply module and comprises five process modules, namely an air carbon capture module, a co-electrolysis module, a Fischer-Tropsch synthesis module, a hydrogen production module and a hydrogenation refining module; the data acquisition module is used for acquiring the temperature, the pressure, the liquid level, the raw material input flow and / or the product output flow of each process module in the process execution device; and the decision control module is used for predicting the future power supply condition of the green electricity based on the historical weather data of the green electricity location, adjusting the distribution of the power in each process module according to the future power supply condition, and adjusting the temperature, pressure and / or raw material input flow in each process module. According to the invention, the cost, the reliability, the operation and maintenance performance, the product quality and the green power utilization rate can be substantially improved at the same time, and the flexible and stable operation of the system under the green power condition is realized.

The invention discloses an acid mine wasteland ecological restoration plant dynamic optimization configuration method, and relates to the field of ecological restoration, and the method comprises the following steps: 1, collecting soil attribute data in a predetermined range of an acid mine wasteland in a dotted manner, and synchronously obtaining growth attribute data of to-be-selected plants and periodic weather data of a target area; step 2, extracting a deterioration feature set having a deterioration effect on plant growth based on the soil attribute data, and analyzing a bioremediation feature set capable of inhibiting the deterioration feature in the plant attribute data to generate a candidate plant set; 3, constructing a dynamic environment simulation model based on a deep learning algorithm, wherein input dimensions comprise weather data time sequence features, soil deterioration feature vectors and a plant bioremediation feature matrix; the method can accurately predict the restoration effects of plants under different climate and soil conditions, simulate the effects of different intervention measures in real time, output the optimal intervention scheme, and ensure the balance between the resource utilization efficiency and the restoration effects.

The invention provides an unmanned aerial vehicle group path planning method and system based on environment analysis, and relates to the technical field of unmanned aerial vehicle path planning. Unmanned aerial vehicle operation data are collected, unmanned aerial vehicle operation abnormity is analyzed based on the unmanned aerial vehicle operation data, an unmanned aerial vehicle group flight path is collected, and the flight distance in the unmanned aerial vehicle flight process is obtained; the method comprises the steps of screening out a flight risk interval based on a flight interval in a flight process of an unmanned aerial vehicle, collecting weather data, analyzing flight abnormity generated by the flight risk interval under the weather influence based on the weather data, and obtaining unmanned aerial vehicle group density in a set range when the unmanned aerial vehicle generates the flight abnormity based on an unmanned aerial vehicle group flight path. The flight influence of flight abnormity on the unmanned aerial vehicle group is analyzed based on the unmanned aerial vehicle group density, the optimal flight path of the unmanned aerial vehicle group is screened out based on the flight influence of the flight abnormity on the unmanned aerial vehicle group, potential flight risks such as out-of-control, course deviation and even crash of the unmanned aerial vehicles can be predicted, and therefore the flight safety is improved.

The invention belongs to the technical field of power system control, and discloses an intelligent adjusting system and method for a transformer. The method comprises the following steps: inputting transformer operation data and weather data collected in real time into a pre-trained load prediction model to obtain predicted load data; inputting the acquired physical parameters and operation data and preset calibration parameters into a pre-constructed digital twinborn model to obtain a cooling capacity index; calculating the total heat source power of the copper loss and the iron loss according to the predicted load data and the electrical parameters of the transformer collected in real time, and dynamically quantifying the balance state of the total heat source power and the cooling capacity index through a matching degree evaluation function in combination with the cooling capacity index to obtain a matching degree evaluation index; and the problem of dynamic unbalance between the heat dissipation capability of the transformer and the load demand in an extreme environment is systematically solved.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring method and system for intelligent water purification equipment. The system comprises a sensing execution unit, a data routing unit, an intelligent central unit and an interactive service unit. The intelligent central unit is used for comparing underground water quality data collected in real time with corresponding month threshold values over the years, water quality fluctuation caused by natural changes such as seasonal replacement can be accurately distinguished, the water quality data interpretation accuracy is improved, when the water quality data deviates from a normal range and influences of natural laws are eliminated, the system introduces real-time weather data, and the water quality data interpretation accuracy is improved. And the analysis module considers the effect of weather on water quality by means of a dynamic structure equation model and resets a threshold value. The intelligent self-adaptive mechanism can effectively distinguish water quality abnormity caused by non-natural laws, for example, normal fluctuation of water quality after rainstorm is recognized, accuracy and intelligence of water quality monitoring are enhanced through multiple times of accurate judgment, and abnormity can be quickly recognized and early warned.

Lowest cost usage scheduling of an energy system during a time interval of interest is achieved by utilizing two components of learning and optimization. First, a number of learning approaches including Linear Least Square Regression (LLSR), Auto Regressive Integrated Moving Average (ARIMA), and Multi-Layer Perceptron Deep Learning (MLPDL) are used to forecast energy production and storage of the energy producing components of a given group of energy components as a function of historical production data and weather data collected from weather and geo reports. Then, an optimization problem is formulated to create the optimal schedule of energy usage during time intervals of interests for the given group of energy components subject to scheduling and equipment constraints. Two alternative solutions, namely Simplex Branch and Bound (SBB) and Interior Point Branch and Bound (IPBB) are presented to solve the optimization problem. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the cost of energy consumption for the given group of energy components within time intervals of interest.

Data integration and distribution systems. A system includes a graphical user interface (GUI). Weather and market data are collected. A weather symbology including symbol elements linked to segments of the collected weather data and rules for generating weather symbology instructions are stored. The GUI is generated for display on a user device. A weather symbology instruction is determined based on at least one requested symbol element indicated in a weather data request and the rules. A weather forecast dataset is created from among the collected weather data based on the weather symbology instruction. A presentation package including the weather forecast dataset and the collected market data is generated such that the weather forecast dataset is integrated with the collected market data. The presentation package is presented on the GUI and updated concurrent with changes to at least one of the weather data, the market data and user input.

The invention relates to the technical field of scheduling management, in particular to a source network load optimization scheduling analysis method and system for an active power distribution network of an electric vehicle. The method comprises the following steps: acquiring power supply side data, power grid side data and load side data in real time, and performing sliding filtering and standardization processing to obtain source network load standard data of the electric vehicle; historical charging data, traffic flow data, weather data and user appearing habit data are obtained, and load and power output prediction calculation is carried out to predict and output electric vehicle charging power distribution and distributed power generation power distribution; a corresponding multi-objective optimization function and a power distribution network operation constraint condition are constructed through an electric vehicle active power distribution network, a source network load optimization scheduling model is constructed, and a source network load optimization scheduling scheme is solved to adjust corresponding electric vehicle charging power and distributed power generation power in real time. The energy utilization efficiency and the power grid operation stability can be improved.

A control method for an energy management and control system, an apparatus, a device and a storage medium. The method comprises: acquiring weather data of a target date (110); on the basis of the target date, the weather data and a prediction model, generating first predicted electric power data and second predicted electric power data, the first predicted electric power data being data of electric power which can be converted by a renewable energy module on the target date, and the second predicted electric power data being data of electric power required by a load device to work on the target date (120); and, on the basis of the first predicted electric power data and the second predicted electric power data, generating a charging policy of the target date, the charging policy comprising: a first charging policy for charging an energy storage device on the basis of the renewable energy module, or a second charging policy for charging the energy storage device on the basis of the renewable energy module and another power supply module (130). The method achieves accurate control on power storage of energy storage devices, thereby improving the utilization efficiency of photovoltaic storage systems.

The invention relates to the technical field of port operation state sensing and monitoring, and particularly discloses a port operation state sensing and monitoring system based on a data center, which monitors a wind speed value and a visibility value at each current time point in real time through a micro weather station array, and synchronously obtains tidal phase data of an observatory. Comprehensively generating multi-dimensional meteorological data in a set time period; dynamically calculating the berthing safety coefficient of the target ship through a multi-dimensional constraint condition in combination with the load tonnage of the target ship; according to the target ship berthing safety coefficient, a dynamic ship berthing strategy of the target ship is generated; through comprehensive application of multi-dimensional meteorological data acquisition, berthing safety coefficient calculation, dynamic ship berthing strategy generation and a feedback terminal, a port manager can monitor and optimize the ship berthing process in real time. Therefore, the operation safety and efficiency of the port are improved, a more scientific and flexible berthing scheme is provided for the ship, and the sustainability and economic benefits of port operation are ensured.

The invention discloses a photovoltaic centralized charging control method and device for a light commercial electric vehicle and a medium, and belongs to the technical field of new energy charging. The method comprises the following steps: acquiring weather data, a vehicle battery charge state and task scheduling data; photovoltaic module parameters and environment data are processed through an LSTM neural network model, and a photovoltaic power generation power prediction curve is generated; constructing a multi-dimensional evaluation index system including task emergency degree, charging efficiency and power grid interaction factors, and generating a charging priority sequence in combination with an entropy weight TOPSIS algorithm; solving a real-time power distribution scheme based on a mixed integer programming algorithm, and dynamically adjusting the output power of the charging pile; monitoring the operation state of the system, and triggering a multi-stage power compensation mechanism when the photovoltaic power is insufficient or the electric quantity is low; and the battery state and the photovoltaic power are updated in real time, and a charging strategy is corrected through a rolling optimization algorithm. By means of the method, centralized charging control over the light commercial electric vehicle is achieved, and the technical effect of meeting the requirements of a motorcade is achieved.

The invention provides an environment-considered photovoltaic panel cleaning scheme determination method, and belongs to the technical field of photovoltaic panel cleaning.The method comprises the steps that firstly, real-time weather data and prediction data are obtained, and an environment stain correlation model is established; a multispectral camera is adopted to identify the types of stains on the surface of the photovoltaic panel and calculate a distribution map; calculating a cleaning index by combining the generating capacity data and the solar irradiation intensity, and determining a cleaning priority; a weather adaptability scoring system is constructed, and a robot operation environment threshold value is set; dividing power generation time periods according to the sunlight intensity and calculating an optimal cleaning time window; cleaning schemes are matched according to different stain types, and parameters are adjusted according to environmental conditions; calling a stain cleaning efficiency optimization function to carry out multi-objective optimization; and finally, generating an optimal cleaning path and parameter configuration by utilizing the photovoltaic cleaning planning network model. The technical problem that a photovoltaic panel cleaning scheme cannot be adaptively optimized according to complex and changeable environmental factors is solved.

The embodiments of the present invention relate to the technical field of logistics management. Specifically, disclosed are a logistics order dispatching method and system based on a transport capacity calculation. The method in the embodiments of the present invention comprises: acquiring logistics order data, and determining a basic required transport capacity; planning a plurality of order delivery routes, and performing a status impact assessment, so as to acquire a status impact score; acquiring dispatching weather data, and performing a weather impact assessment on the dispatching weather data, so as to acquire a weather impact score; acquiring service feedback data, and performing a service impact assessment on the service feedback data, so as to acquire a service impact score; and calculating an optimized required transport capacity, and performing logistics order dispatching on the basis of the optimized required transport capacity. Therefore, a basic required transport capacity is determined on the basis of the number of logistics orders, a route status impact assessment, a weather impact assessment and a service impact assessment are then performed, an optimized required transport capacity is calculated, and the logistics order dispatching is performed on the basis of the optimized required transport capacity, such that logistics order dispatching based on a transport capacity calculation is rationally realized, and the on-time delivery of orders is ensured.

The invention relates to the technical field of navigation, in particular to a high-positioning-precision vehicle self-adaptive navigation method and a vehicle-mounted navigator, and the method comprises the following steps: obtaining real-time vehicle positioning data, real-time event information and weather data, carrying out association matching with geographical road sections, and establishing a data record for each road section. According to the invention, quantitative evaluation is carried out on each road segment factor based on the preset reference, and a road segment navigation risk score reflecting a real-time risk level is generated in combination with a dynamic weight distribution mechanism of events and weather, so that the response sensitivity to emergencies is enhanced. By analyzing the vehicle track speed deviation value and correcting the comprehensive traffic cost of the road section, the historical traffic flow density mean value is introduced to be compared with real-time data, and it is ensured that cost calculation gives consideration to both stability and timeliness. In the path planning stage, traffic cost and risk scores are synchronously fused, a screening model of a product of geometric average risk indexes and total cost is adopted, a dual-objective optimization function is constructed, and efficiency and safety requirements are effectively balanced.

According to the permanent basic farmland quality intelligent monitoring system and method provided by the invention, accurate monitoring and dynamic management are realized through multi-module cooperation, the terrain gradient and canopy height are calculated based on LiDAR point cloud, the complexity index is generated in combination with meteorological data, and the A * algorithm is improved to optimize the unmanned aerial vehicle inspection path. The method comprises the following steps: extracting hyperspectral data to analyze soil and crop states, calibrating an electrode response curve, calculating a bio-availability index in combination with temperature and humidity, dynamically adjusting an early warning threshold value, identifying abnormal data by adopting an isolated forest algorithm and carrying out grading processing, and constructing a space-time cube by adopting space-time Kriging interpolation and CNMF algorithm and fusing multi-source data. According to the method, indexes such as ecological protection grades and economic values are integrated to generate a graded soil quality index model, a disease thermodynamic diagram is generated, key data are stored in a chaining mode, and farmland quality real-time monitoring, risk early warning and ecological economic balance decision making are achieved through multi-source heterogeneous data fusion, dynamic weight optimization and block chain technologies.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention provides a short-term load prediction method, which comprises the following steps: collecting multivariable data of a power system, and preprocessing the collected multivariable data; wherein the multivariable data at least comprises power load data, weather data, holiday and festival information and date data; inputting the preprocessed multivariable data into a pre-trained prediction model to obtain a corresponding prediction result; wherein the prediction model is a model combining a time convolution network and a long short-term memory network, hyper-parameter optimization is carried out through a preset search algorithm, training is carried out through historical multivariable data, and a plurality of intrinsic mode component prediction results are combined to be output as the model; and determining the short-term load corresponding to the power system according to the prediction result. According to the method, the nonlinear features in the power load data are effectively extracted, more accurate load prediction is realized through advantage complementation of multiple models, and a solid guarantee is provided for stable operation of a power system.

An expressway holiday travel traffic volume prediction method based on the influence of a weather factor, said method belonging to the technical field of road traffic flow prediction. In order to solve the problem of accurate holiday expressway travel traffic volume prediction based on the influence of a weather factor, the method of the present application comprises: establishing a relative influence coefficient between historical weather and travel traffic volume; constructing a travel traffic volume transfer model based on weather data; selecting basic travel traffic volume features, constructing derived travel traffic volume features by means of a cross operation between the basic travel traffic volume features, and then constructing a multiple linear regression model based on historical travel traffic volume features; correcting a predicted travel traffic volume obtained by the multiple linear regression model; correcting a predicted travel traffic volume of the remaining days of a holiday; constructing a layered flow road network travel traffic volume model; and constructing an objective function, and using a reverse gradient propagation method to optimize and solve the layered flow road network travel traffic volume model, so as to obtain an expressway holiday travel traffic volume prediction result based on the influence of a weather factor.

Disclosed are twin-based systems and methods for predicting solar power generation and optimizing power generation and distribution processes. Employed are a digital twin model of a solar power plant, which includes detailed representations of various components, such as solar panels, inverters, and transformers, as well as real-time weather data and historical data. This advantageously allows for accurate simulations of plant performance under various weather conditions and operational scenarios. Our systems and methods Incorporate novel machine learning algorithms that are trained on historical and real-time data from the digital twin model, weather data, solar power generation data, and other relevant factors. These algorithms utilize an advanced ensemble learning approach, which combines multiple predictive models, such as deep learning, support vector machines, and decision trees, to achieve higher accuracy and robustness in predicting solar power generation

The invention relates to a peak clipping and valley filling processing method and device and electronic equipment, and relates to the technical field of power system automation control, and the method comprises the steps: obtaining a historical load data sequence and a corresponding historical weather data sequence; extracting data features of the historical load data sequence and the historical weather data sequence, and inputting the extracted data feature sequence into a load prediction model for short-time load prediction processing to obtain a predicted load curve; the predicted load curve is input into a light-storage combined peak clipping and valley filling model, the light-storage combined peak clipping and valley filling model is iteratively optimized by adopting a particle swarm algorithm, optimal key parameters of the model are determined, a photovoltaic power generation output form and an energy storage output form are planned on the basis of the optimal key parameters, and the output forms are corrected and optimized in real time, so that the photovoltaic power generation output form and the energy storage output form are obtained. And carrying out peak clipping and valley filling processing on the predicted load curve. By applying the scheme, the load curve can be effectively stabilized, and stable operation and supply-demand balance of the power system can be maintained.