1646 results about "Weather prediction" patented technology

The invention provides a cold machine and water pump linkage energy-saving control method under a low-load working condition, which relates to the technical field of information and comprises the following steps of: dynamically predicting load requirements under the low-load working condition by analyzing historical operation data of a refrigeration system and combining weather forecast information, equipment operation parameters and data of building personnel activity rules; obtaining a cooling capacity demand curve and a load demand prediction result in future preset time; establishing a system low-load operation mode optimization model, taking the screened control combination meeting the preset energy-saving operation target as model input, and obtaining an optimized system low-load operation mode through the training model; the optimal energy efficiency interval is determined by analyzing the relation between equipment energy consumption and cold storage capacity, and during load demand prediction and control optimization, the interval serves as a target constraint to guide a refrigerator and water pump equipment to operate in a high-energy-efficiency area. And the operation efficiency and the energy-saving effect of the refrigerating system under the low-load working condition are improved.

The invention belongs to the technical field of intelligent control of heating, ventilation and air conditioning systems, and particularly relates to an intelligent optimizing and energy-saving control method for a central air conditioner based on deep learning, which comprises the following steps of: acquiring operation data of a central air conditioning system in real time through an internet of things technology; the operation data comprises operation parameters of cold and heat source equipment, flow and lift parameters of a water pump, fan frequency parameters of a cooling tower, temperature and humidity data of an air conditioner terminal, environment temperature and humidity data, weather forecast data and the like. Through deep integration of Internet of Things perception, deep learning prediction and a multi-objective optimization technology, the limitation of a traditional control framework is broken through, meanwhile, accurate prediction of building cooling and heating loads is realized through construction of a hybrid deep learning model, an optimization objective of a full life cycle perspective is established in combination with an equipment performance degradation model, and the system performance is improved. A federal learning framework is innovatively introduced into region-level energy efficiency management, and the model generalization ability is improved on the premise of ensuring data privacy.

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

The invention discloses a multi-target intelligent optimization air conditioner control method and system based on multi-dimensional machine learning, and aims to acquire outdoor environment data by acquiring personnel flow and environment data inside and outside a building in real time and combining weather forecast information and the like, and predict future personnel flow trend and environment change conditions by utilizing a machine learning algorithm, so as to realize intelligent optimization of the air conditioner. And data support is provided for an artificial intelligence control strategy of the air conditioning system. Based on the prediction results, a multi-target optimization control algorithm is used for conducting multi-dimensional parameter regression on the air conditioner operation effect, and operation parameters such as the temperature, the humidity and the wind speed of the air conditioner system are dynamically adjusted. The air conditioning system is suitable for various large buildings such as office buildings, shopping malls and airports, the energy utilization efficiency of the air conditioning system is remarkably improved and the energy consumption is greatly reduced on the premise that the indoor environment comfort is ensured, and the air conditioning system has remarkable energy-saving and carbon-reducing effects, wide application prospects and remarkable economic and environmental protection benefits.

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention relates to the technical field of electric power meteorological prediction, and aims to solve the problem that a current power grid natural disaster early warning system has defects in feature extraction and cannot fully utilize knowledge in related fields to enhance the prediction capability of a model. The invention discloses a power grid natural disaster early warning method based on a knowledge graph, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: S1, constructing a power meteorological knowledge graph based on meteorological data, geographic information and power equipment data corresponding to a power grid natural disaster; s2, performing feature enhancement on the meteorological data according to semantic features in the knowledge graph to form fusion features; s3, according to the fusion features, constructing a disaster weather prediction model based on a graph convolutional neural network and a self-attention mechanism; and S4, according to the disaster weather prediction model, performing early warning on the natural disasters of the power grid in the target weather. By adopting the method, the accuracy of power grid natural disaster prediction can be improved.

The invention discloses an intelligent air conditioner water chilling unit control method and system based on predictive optimization. Operation data and weather forecast data are collected to establish a dynamic response reference, building cold load sudden change opportunity is recognized, the mismatching relation between magnetic suspension frequency and wet bulb temperature is detected, and energy-saving opportunity recognition data is generated to determine a cooling starting judgment table; identifying equipment response delay characteristics by using a dynamic response reference, extracting time sequence advantage parameters to determine dislocation configuration among multiple pieces of equipment, and generating coordination control parameters; cloud-local transmission delay analysis is carried out according to the coordination control parameters, and a coordination control sequence is generated through buffer opportunity identification and delay compensation; mechanical refrigeration suppression data is generated through energy efficiency mode classification, free cooling potential mining is implemented to form a cold source optimization factor, and an emergency response strategy is generated; an emergency response strategy is used to identify a multi-device linkage trigger critical zone, a prediction correction feedback network is constructed, a global collaborative optimization instruction is generated, and the system operation efficiency and the control precision are improved.

The invention discloses a hybrid deep learning model construction and output interpretation method for spaceborne GNSS-R sea surface significant wave height inversion. The method comprises the steps of obtaining CYGNSS L1-level GNSS-R data, an European mid-term weather forecast center (ECMWF) ERA5 reanalysis data set, IMERG rainfall product data and WaveWatch III SWH data; carrying out preprocessing and quality control on the acquired data; performing space-time matching on a data set obtained after quality control, and dividing the data set into a training set, a test set and a verification set according to a certain proportion; the method comprises the following steps of: constructing a hybrid deep learning model which is combined with efficient multi-scale attention and CNN-Transform-BiConvLSTM, and carrying out training and verification on the model; a MultiScaleWaveFormer model is created to deeply learn an SHAP interpreter, and the importance of the input features is calculated and visualized; the method comprises the following steps of: inputting an inversion result into a trained MultiScaleWaveFormer model to obtain an inversion result; and finally, comparing the SWH inversion result of the model with the SWH inversion result of the existing model, and evaluating the performance of the model. According to the invention, the five modules are combined, higher efficiency and functionality are realized, and the accuracy of spaceborne GNSS-R data inversion significant wave height is further improved.

Provided in the present invention is a power prediction method for distributed photovoltaic power stations, the method comprising: acquiring a satellite cloud image of distributed photovoltaic power stations at a historical moment and a satellite cloud image of the distributed photovoltaic power stations at a current moment to predict satellite cloud image features at a future moment; selecting several photovoltaic power stations to install solar irradiance collection apparatuses, and on the basis of the satellite cloud images, collected solar irradiance, and location information of the photovoltaic power stations, calculating the solar irradiance of all the photovoltaic power stations; on the basis of the satellite cloud image features at the future moment and the collected solar irradiance of the photovoltaic power stations, obtaining the solar irradiance of the distributed photovoltaic power stations at the future moment; and on the basis of the solar irradiance of the distributed photovoltaic power stations at the future moment and weather forecast information at the future moment, predicting the power of the distributed photovoltaic power stations at the future moment. In the solution of the present invention, solar irradiance data is expanded, and a mapping relationship between satellite cloud images and solar irradiance and a mapping relationship between solar irradiance and power are constructed by means of prediction, so that the power of distributed photovoltaic power stations is predicted, thereby improving the prediction precision.

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 discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

The invention discloses a new energy charging pile charging control method and system, and relates to the technical field of new energy, and the method comprises the steps: collecting user historical charging data, power grid load data, fixed electricity price information and weather forecast data in real time, and recognizing user charging habits and demands; generating a personalized charging scheduling strategy according to the charging habit of the user and the real-time power grid state; the availability of various energy sources is monitored in real time, the generating capacity and the cost of each energy source are evaluated, the priority of energy source selection is adjusted by using a personalized charging scheduling strategy, and an optimal charging mode is automatically selected; executing charging operation based on the charging mode selected by the user, and performing adjustment according to the power grid load and the energy consumption condition; the availability of various energy sources is monitored in real time, the generating capacity and the cost are evaluated, and the priority of the energy sources is dynamically adjusted, so that the response capability of the charging pile to environment change is improved, the charging cost of a user can be reduced, and renewable energy sources are fully utilized.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

The invention discloses a flood disaster early warning method and device, a medium, and the technical field of Internet of Things and artificial intelligence, and the method comprises the steps that a cloud platform collects key hydrological elements corresponding to a to-be-predicted region through a monitoring device, and obtains multivariate data corresponding to the key hydrological elements; key features in the historical multivariate data are extracted through an analysis algorithm, the historical multivariate data are divided into a training set and a verification set according to the key features, and a hydrological forecasting model constructed through multiple neural networks is trained based on the training set; acquiring real-time weather forecast data, and outputting forecast flow and water level through the trained hydrological forecast model; according to the forecast flow and the water level, the flood occurrence probability is obtained through the pre-trained risk assessment model, and corresponding early warning information is automatically generated based on the flood occurrence probability. Through the hydrological forecasting model, the flow of the key water body can be quickly predicted, and early warning information can be generated in a short time.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

The invention provides a distributed photovoltaic power generation grid-connected anti-countercurrent control system and control method. The system comprises a load prediction and optimization assembly, a countercurrent detection and response assembly and a control and optimization scheduling assembly. The method comprises the steps of receiving input of various data sources such as power grid dispatching data, user power utilization habits and weather forecast, predicting load requirements and power grid capacity in a period of time in the future in real time, and providing a decision basis for a control and dispatching component; receiving the load demand and the power grid capacity predicted by the load prediction and optimization component, and real-time power grid state data; when it is detected that countercurrent in the power grid is about to occur, an instruction for adjusting photovoltaic output is sent to the control and optimization scheduling assembly; receiving the predicted load demand and power grid capacity, and an instruction for adjusting photovoltaic output; and optimizing the scheduling strategy of photovoltaic power generation, dynamically adjusting the output of photovoltaic power generation, and sending a control signal to photovoltaic power generation equipment. According to the invention, power grid faults and loss caused by countercurrent can be reduced.

The invention discloses a photovoltaic power generation day-ahead prediction method based on causal inference and multi-scale feature fusion, and belongs to the technical field of photovoltaic power generation prediction. According to the method, historical meteorological characteristic data is divided into a plurality of samples, and generated power of the samples in the future M steps is used as corresponding labels. A PCMCI causal inference method is used to calculate a causal inference weight between a sample and a label, the sample is weighted and then is input into three base learners of BiLSTM, TCN and GAF-SE-ResNet50 in parallel, the trend of an input sequence is recognized from three dimensions of global, local and periodical, and a photovoltaic power generation power day-ahead preliminary prediction value is obtained through corresponding training. Wavelet features of the local weather forecast sequence of the prediction point are extracted, and a final prediction value is obtained through fusion of a cross attention mechanism and the initial prediction value of the base learning device. Accurate and stable photovoltaic power generation prediction in a complex climate change environment is realized.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention discloses a medium and long term weather prediction system based on a physical law correction deep learning technology, and the system comprises a high-resolution data input module, a multi-resolution network construction module, a sub-graph processing module based on a graph neural network, and a linear attention processing module. The system comprises an advanced attention processing module used for reducing secondary calculation complexity to linear complexity, a feature fusion and adaptive fusion module, a multi-level decoding and physical corresponding area optimization correction model, a rolling forecasting module and a multi-step training fine tuning module based on DORA. The medium and long term weather prediction system based on the physical law correction deep learning technology can process multi-resolution input data, and realizes high-precision and long-time weather prediction. Furthermore, global weather forecast with the resolution ratio of 16 days and 13 kilometers can be achieved, and meanwhile refined forecast with the higher resolution ratio is achieved in China.

The invention discloses an optimization method for a new energy ship energy management system, and the method comprises the steps: S1, obtaining a priority assumption condition related to the energy use of a new energy ship according to a pre-created new energy ship system; s2, constructing a ship energy management mathematical model about a new energy ship system based on priority hypothesis conditions; s3, according to the constructed ship energy management mathematical model, obtaining an objective function model and optimization constraint conditions of ship energy for quantitatively evaluating advantages and disadvantages of different energy use schemes; s4, ship operation energy consumption and historical navigation data are collected and obtained; s5, according to the historical data, obtaining a neural network prediction model for pre-estimating the photovoltaic generating capacity and the ship navigation power demand based on weather prediction data; and S6, according to the neural network prediction model, obtaining estimated data of the photovoltaic power generation amount and the ship navigation power demand, and based on the target function model of the ship energy and the optimization constraint condition, obtaining an optimal energy use scheme of a corresponding new energy ship energy management system according to an optimization algorithm. The problems that an existing ship energy management system and a calculation method thereof have remarkable defects in the aspects of energy efficiency optimization, multi-energy coordination, dynamic response, operation simplicity and convenience, intelligent management, safety, new energy compatibility, comprehensive optimization strategies and data processing capacity, and complex requirements and environmental protection requirements of modern shipping are difficult to meet are solved.

The invention discloses a refrigeration equipment control method and system for a communication machine room, and relates to the field of heat dissipation control, and the method comprises the steps: obtaining machine room indoor temperature and equipment heating data of the communication machine room, and determining a preset refrigeration index according to the machine room indoor temperature and the equipment heating data; the operation parameters of the air conditioning equipment are adjusted to refrigeration operation parameters; when the temperature difference value between the indoor temperature of the machine room and the outdoor environment temperature is higher than a preset starting threshold value, starting parameters of the natural cold energy exchange unit are determined; the natural cold energy exchange unit is controlled to operate according to the starting parameters, and operating parameters of the air conditioning equipment are adjusted to be optimized operating parameters; determining a temperature change trend curve of the outdoor environment temperature based on the weather forecast information, and determining an optimization adjustment node according to the temperature change trend curve; and when the adjusting nodes are optimized, the starting parameters of the natural cold energy exchange unit and the operation parameters of the air conditioning equipment are adjusted. By implementing the application, the energy use of the refrigeration equipment in the communication machine room can be saved.

An AI-powered personalized travel recommendation system for tourists, consisting of: a user interface configured to receive user input regarding travel preferences, interests, budget constraints, and desired length of stay; a data processing unit comprising one or more machine learning algorithms configured to analyze user data, including but not limited to past travel behavior, demographic information, and real-time location data, to generate personalized travel recommendations; a recommendation engine that is operationally connected to the data processing unit and configured to synthesize and aggregate various data sources, including social media activity, online reviews, weather forecasts and local events, to compile a list of tailored destinations, activities, accommodations and dining options; an adaptive feedback loop that changes recommendations based on user interactions and feedback, with the feedback being used to improve the machine learning algorithms over time; a travel itinerary generator that uses the results of the recommendation engine to create an optimized itinerary, making it easier to discover popular tourist attractions, hidden gems, and off-the-beaten-path experiences; and a sharing module that allows users to share their personalized itineraries and ratings with other users of the system, thus promoting a community-oriented approach to travel planning.

The invention discloses a variational deviation correction method and device, and relates to the field of weather forecast, and the method comprises the steps: obtaining the observation data of a current time window and an updated deviation correction coefficient of a previous time window; calculating a satellite observation deviation correction amount based on the deviation correction coefficient, the observation information and the mode background field, calculating a deviation between simulation observation and actual observation based on an observation operator, and carrying out correction and quality control on the deviation; according to the satellite observation data after quality control, on the basis of the improved target functional and the gradient thereof, in the minimization iteration process, under the joint constraint of the observation data and a numerical weather forecast mode, a deviation correction coefficient is updated online; wherein the improvement process of the target functional comprises the steps that a deviation correction coefficient is used as a control variable to be combined with a mode state variable to construct a new target functional, and variable transformation is carried out in an increment form. The assimilation analysis quality of observation data is improved, and the numerical weather forecast level is improved.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The present application relates to the technical field of offshore wind prediction. Disclosed are an offshore wind resource prediction method and apparatus, and a computer device and a storage medium. In the present application, a first offshore meteorological field data set is converted into a second offshore meteorological field data set, which can be identified in a weather research and forecast mode. Optionally, on the basis of a first anemometer tower wind-speed data set and a second anemometer tower wind-speed data set, advanced extreme gradient boosting is used to construct a target offshore wind resource prediction model, so that the accuracy of offshore wind resource prediction can be improved, a data fusion process can be simplified to a relatively great extent, and different pieces of data can also be compensated for on the basis of the comprehensive use of multi-source research and forecast data of a global ensemble forecast system, thereby giving full play to the advantages of various types of data, and thus a prediction result is more reliable.

The invention relates to the technical field of air conditioners, in particular to an air conditioner internet-of-things control method. An IoT (Internet of Things) module is integrated in each air conditioner, and environment data including temperature, humidity and air quality of a room are collected in real time through connection of an intelligent sensor and a cloud platform; data of all air conditioning equipment is summarized to form a centralized management system of equipment state and environment information; an intelligent cooperative control algorithm based on communication between the equipment is constructed by means of internet-of-things intercommunication between the air conditioning equipment; the air conditioning equipment detects environmental data in real time through a local sensor, accesses prediction data of an external environment including weather forecast and climate change trend, and adjusts a working mode in advance in combination with a self-learning function of the equipment; a multi-dimensional energy-saving optimization algorithm is adopted for intelligent control; meanwhile, the overall energy consumption is optimized in combination with external weather change and equipment use frequency; the optimization strategy comprises time period distribution and equipment priority control; and performing personalized user control based on big data analysis.

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 discloses a multimodal transport organization mode dynamic optimization method based on big data processing, which relates to the technical field of multimodal transport data optimization, and comprises the steps of collecting various data in a transport process in real time, and carrying out data optimization based on transport network nodes of a target area. A multimodal transport grid model is constructed, first site information and second site information in a multimodal transport planning path are collected, first weather prediction data and second weather prediction data are collected in real time, and the multimodal transport grid model based on weather changes is formed. An optimization algorithm is adopted to solve the multimodal transport path optimization model with constraint conditions and based on weather changes; according to the method, the influence of the weather factors on the transportation mode can be fully considered when the multimodal transportation organization mode is dynamically optimized, the situation that the established transportation mode cannot be safely and successfully executed on schedule due to the weather influence is avoided, the transportation mode can be flexibly adjusted by introducing the first weather prediction data and the second weather prediction data, and the transportation efficiency is improved. Therefore, the smooth operation of multimodal transport is ensured.

The invention discloses a photovoltaic module cleanliness evaluation method and system based on multi-dimensional data fusion. The method comprises the following steps: acquiring multi-dimensional data of a photovoltaic power station, including generating capacity data, power station state data, irradiation data, meteorological data, geographic position data, cleaning record data, weather forecast data and economic index data; preprocessing the data; constructing a multi-dimensional feature; a pre-trained Transform architecture time sequence model is adopted to carry out training; evaluating the cleanliness state of the photovoltaic module in real time and predicting the change trend; and determining the optimal cleaning time and generating decision suggestions based on the cleanliness evaluation result and economic analysis. Through multi-dimensional data fusion and a large model algorithm, precise evaluation and scientific decision making of the cleanliness of the photovoltaic module are realized, the evaluation accuracy is improved by 15-20%, and the method is suitable for photovoltaic power stations at different geographic positions and under different climate conditions.