1158 results about "Weather data" patented technology

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 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 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 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.

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.

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 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.

The invention discloses an air-railway combined transportation path optimization method based on XGBoost and a space-time attention network, and particularly relates to the field of intelligent transportation systems.According to the method, a traffic network basis is constructed by integrating flight and train historical data, topological information and weather data, delay time and consumed time of a critical path are predicted by means of an XGBoost model, and the time consumption of the critical path is predicted by means of the XGBoost model; a space-time dependency relationship is modeled through a space-time attention network, and complex space-time association is captured in combination with a space and time attention module and a multi-head mechanism; an optimal path is generated based on a weighted multi-objective function (covering time, economy, reliability and comfort), and users are supported to dynamically adjust weights to adapt to personalized requirements; and meanwhile, real-time adjustment of model parameters is realized by adopting an exponential weighted moving average and self-adaptive updating strategy, so that the prediction precision is remarkably improved, the reliability of the path and the user satisfaction are optimized, the real-time performance is ensured through a lightweight closed-loop updating mechanism, and a high-precision, personalized and real-time response intelligent solution is provided for air-railway combined transportation.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention relates to the technical field of intelligent supply chain management and multi-modal artificial intelligence crossing, and discloses a multi-modal large model-based supply chain toughness evaluation system, which comprises a data acquisition module, a multi-modal processing module, a toughness evaluation module, a cloud edge collaboration module and a decision support module, the data acquisition module is used for acquiring supply chain texts, images and time sequence heterogeneous data; the multi-modal processing module is used for realizing dynamic feature fusion through term matrix enhanced Transform; the toughness evaluation module quantifies toughness scores based on a three-level index system and a fuzzy matter element calculation engine; the cloud edge collaboration module realizes edge end real-time reasoning through a lightweight model; the decision support module optimizes node reinforcement and path planning based on network topology and real-time weather data. According to the method, the problems of insufficient data isomerism processing, single evaluation dimension and lagging real-time response in the prior art are solved.

The invention discloses an urban rail vehicle simulation driving training method and system based on scene simulation, and belongs to the technical field of driving simulation, and the method specifically comprises the steps: collecting rail line parameters, weather data, passenger flow density, vehicle equipment state and driving data in real time, carrying out the preprocessing, constructing a dynamic personalized simulation scene, and simulating driving operation. According to operation of a driver and a preset simulation scene, micro features of eye-brow areas of the driver are recognized, the psychological stress of the driver is analyzed in combination with historical data, the simulation scene is dynamically adjusted according to a psychological stress analysis result, the performance of the driver in the training is scored according to a preset evaluation standard, and a detailed training report is generated. According to the method, pressure analysis is carried out on the eyebrow area, pressure evaluation is carried out in combination with the physiological dimension, the pressure of the driver in a simulation scene can be judged in real time, different simulation scenes are provided for different drivers, and the training effect is greatly improved.

The invention discloses a marine meteorological observation data millisecond query system based on time sequence block indexing. The marine meteorological observation data millisecond query system comprises a data input module, a data preprocessing module, a data hierarchical modeling module, a data storage cluster, a data analysis engine, a data query engine and a data operation interface module. Based on data modeling, data thinning and data partitioning technologies, data preprocessing is carried out for different meteorological ocean space weather data, and distributed storage and access of mass data are achieved; the method comprises the following steps: inputting ocean actual observation data, forecast data or historical data, carrying out model construction and index design according to meteorological ocean space weather data characteristics, completing distributed storage, and applying a data query engine and a data analysis engine to realize rapid aggregation query of the meteorological ocean space weather data; storage and query of lattice point data are realized by using a distributed database, and the problems of scale and query performance of meteorological ocean space weather data are effectively solved.

The invention discloses a meteorological data assimilation method fusing topographic features, and relates to the technical field of meteorological data assimilation, and the method comprises the steps: combining a meteorological variable-topographic factor correlation map with an atmospheric state numerical field, and obtaining a multivariable initial meteorological field model fusing topographic factors; identifying space-time dynamic deviation distribution characteristics of the multivariable initial meteorological field model by using real-time multi-source environment observation data, and generating a terrain disturbance response matrix; carrying out dynamic correction and space-time error reconstruction based on a terrain disturbance sensitive weight on the terrain disturbance response matrix and the multivariable initial meteorological field model through an assimilation optimization algorithm to obtain a meteorological assimilation result; according to the method, dynamic correction and space-time error reconstruction are carried out on the initial meteorological field through the terrain disturbance sensitive weight, directional perception and intelligent correction of observation deviation are realized, and the response capability and the error control level of the model in a dynamic change scene are effectively enhanced.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention discloses a medium-and-long-term runoff forecasting method coupled with a weather forecasting product and a related device, and relates to the technical field of runoff forecasting, the method comprises the following steps: firstly, acquiring runoff data, weather data and climate index data of a plurality of time periods before a to-be-predicted time period and rainfall correction forecasting data of the to-be-predicted time period; taking as an input data set; after preprocessing and redundant variable screening are carried out, an obtained input data set is input into an optimal runoff forecasting model which is determined through training and comparison, and runoff forecasting data of a to-be-predicted time period are obtained; compared with an existing physical hydrological model, runoff prediction is carried out by coupling rainfall correction forecast data, complex hydrological physical calculation is avoided, and the adaptability and generalization ability of the model are improved; compared with an existing data driving method, the method has the advantages that medium-and-long-term runoff forecasting is carried out by coupling rainfall correction forecasting data, and the defect that the influence of future weather on medium-and-long-term runoff is not fully considered in an existing method is overcome.

The invention discloses a GIS-based artificial influence weather operation command method and device, and the method comprises the steps: obtaining meteorological data and geographic information data, and carrying out the spatial registration of the meteorological data and the geographic information data according to latitude and longitude coordinates, and obtaining the initial data after registration; determining a first target area according to the geographic information data; analyzing the weather data of the first target area based on the initial data, determining an operation mode according to an analysis result, and formulating an operation sowing amount based on the operation mode; and carrying out weather operation in the first target area by using the operation equipment according to the operation mode and the operation sowing amount. According to the invention, through integration of the GIS and the meteorological data, accurate command and control of artificial influence weather operation are realized, and the operation efficiency and effect are improved.

A prediction method of solar-generated power includes: storing past data obtained by associating weather data and power generation data of a solar cell output via a PCS for at least one year before a prediction target day; calculating a sunny power generation curve from the past data; obtaining an annual transition curved line indicating an annual transition of a total power generation amount; obtaining a sunny power generation model from the sunny power generation curve so as to match the annual transition curved line; obtaining a the sunny power generation model for the entire year; obtaining a weather coefficient; and acquiring a weather forecast and obtaining prediction generated power from the sunny power generation model and the weather coefficient.

The invention relates to the technical field of weather prediction, and discloses a weather prediction method based on an improved quantum long short-term memory network, which comprises the steps of inputting weather data into a CGRU model to perform spatial feature extraction on the weather data, and then inputting an output hidden state sequence into an HAQLSTM model to perform prediction, the HAQLSTM model is an improvement of a quantum long short-term memory network model, and the quantum long short-term memory network model is an improved quantum long short-term memory network model. A parameterized variable component sub-circuit is adopted, an attention mechanism and residual connection are added, a quantum long-short-term memory network serves as a time modeler, the residual connection enhances information transmission, the attention mechanism dynamically balances the importance of different parts of input data, in the processing process, a self-attention mechanism is adopted to calculate the correlation weight of input sequence elements, and the correlation weight of the input sequence elements is calculated. Four parallel parameterized variable component sub-circuits are used for carrying out key calculation so as to improve the model performance; the problems that an existing weather prediction model network is insufficient in expression ability, and the efficiency of capturing the long-term dependency relationship in the sequence is low are solved.

The invention relates to the technical field of coal-fired power plant environmental protection, in particular to a coal-fired power plant intelligent environmental protection island control system based on multi-source data fusion. Comprising a multi-source data acquisition module, a data transmission module, a data preprocessing module, a multi-source data fusion module, an operation state analysis module, an intelligent control decision module, a control instruction execution module and a man-machine interaction module. According to the coal-fired power plant intelligent environmental protection island control system based on multi-source data fusion, equipment operation, environmental monitoring, power grid, fuel and meteorological data are widely acquired through the multi-source data acquisition module, the limitation of a single data source of a traditional system is broken through, a distributed Kalman filtering algorithm is combined with a neural network algorithm for data fusion, and the data fusion efficiency is improved. According to the method, the incidence relation between data can be deeply mined, temperature and pressure parameters of desulfurization equipment and flue gas sulfur dioxide concentration data are subjected to fusion analysis, the reason of desulfurization efficiency reduction can be accurately positioned, and compared with traditional single data source judgment, the fault diagnosis accuracy is improved.

The invention discloses an intelligent scheduling method and system for road construction, and relates to the technical field of intelligent scheduling, and the method comprises the following steps: analyzing to obtain a road construction knowledge spectrogram and first scheduling data, and compensating the first scheduling data; according to the method, the historical construction data is analyzed, the road construction knowledge graph is constructed, the construction process, resource investment and construction period arrangement can be dynamically adjusted according to the real-time change of weather by introducing the real-time meteorological data for the complex association among scattered experience, rules, cases and entities, and the dynamic adjustment is realized through meteorological compensation, so that the construction efficiency is improved. Non-productive consumption such as mechanical idling and staff waiting caused by bad weather can be reduced, experience reuse and scientific decision are realized by constructing a road construction knowledge graph, environmental changes are coped with through dynamic compensation of meteorological data, and intelligence, refinement and high efficiency of construction scheduling are finally realized through the synergistic effect of the two.

The invention provides an ultra-short-term photovoltaic power combined prediction method based on multivariate meteorological data, belongs to the technical field of photovoltaic power generation power prediction, and aims to solve the problem that in photovoltaic power generation prediction, model training time can be increased by considering excessive non-key influence factors, and then convergence speed and prediction precision are affected. The method comprises the following steps: acquiring a photovoltaic power generation power sequence, and performing multi-scale decomposition and K-L divergence screening; performing correlation analysis on the random meteorological variables in the photovoltaic power generation power sequence and the to-be-predicted indexes, and selecting meteorological data having the maximum correlation with the to-be-predicted indexes according to a correlation analysis result; an ECA-TCN-BiLSTM combined prediction model is constructed; and inputting the decomposed and screened photovoltaic power generation power data and the selected meteorological data into an ECA-TCN-BiLSTM combined prediction model, and superposing and integrating prediction results of the IMF components to obtain a final ultra-short-term photovoltaic power prediction value.