2279 results about "Prediction algorithms" patented technology

The invention provides a solenoid valve precise control method based on flow dynamic adjustment, which comprises the following steps: acquiring working parameters in real time through a sensor, obtaining real-time working condition data of a solenoid valve, and carrying out noise reduction and normalization preprocessing on the data to generate a comprehensive working condition feature vector; and on the basis of the feature vectors and the target output parameters, flow, temperature and outlet pressure values of future working conditions are calculated through a prediction algorithm. And in combination with the calculated future working condition and real-time working condition data, an optimized control signal is generated and used for dynamically adjusting the opening degree of the electromagnetic valve, and initial dynamic control over the flow and the pressure is achieved. Outlet pressure and flow are monitored in real time through high-frequency sampling, the deviation between actual output and a target value is calculated, and a control signal is corrected based on the deviation. The electromagnetic valve is suitable for high-precision flow and pressure regulation under complex working conditions, has the characteristics of quick response and high regulation precision, and can be widely applied to the fields of environmental protection, industrial automation and fluid control.

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention discloses a data-driven cross-domain intelligent asset knowledge reasoning and value evaluation method and system, and relates to the technical field of intelligent assets, and the method comprises the steps: extracting multi-domain data features of intelligent assets based on a deep learning model, and constructing a cross-domain intelligent asset knowledge graph; analyzing a user evaluation target based on a pre-trained language model, retrieving related assets in a knowledge graph, and performing knowledge reasoning by using an attention mechanism and a graph reasoning algorithm; and on the basis of the value evaluation model of reinforcement learning training, a final evaluation value and an evaluation report containing a confidence interval are generated in combination with a time sequence prediction algorithm. According to the method, multi-source heterogeneous data can be effectively fused, knowledge reasoning and value evaluation of cross-domain intelligent assets are realized, and the evaluation efficiency and accuracy are improved.

The invention relates to the technical field of prediction algorithms, in particular to an oil and gas well digital twinborn modeling and prediction analysis system which comprises an underground dynamic data analysis module for extracting variables based on pressure, flow velocity and temperature data, performing data association calculation, analyzing the pressure change rate and the flow velocity trend, calculating the temperature fluctuation range and establishing time period variable analysis; and generating a dynamic parameter mode. According to the invention, through an autoregressive integral moving average model, the dynamic data of each time period is integrated and smoothed, the long-term trend of the data is captured, the subtle change in the production fluctuation is identified, the accurate future yield fluctuation trend is generated, a density clustering algorithm is adopted, key variables are analyzed, and the change mode of abnormal fluctuation is extracted. According to the method, potential fault types are screened, similar data points are classified through the density relation between data, dependence on parameter setting is reduced, abnormity is recognized more accurately under the unsupervised condition, and the fault recognition capability is improved.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention relates to the technical field of operation and maintenance scenes, in particular to a fault diagnosis prediction method and system based on an operation and maintenance scene, and the method comprises the steps: collecting equipment data, and constructing a scene fault diagnosis model; performing nonlinear fusion on the collected operation and maintenance data and historical maintenance records to obtain a real-time data fusion result; constructing an adaptive prediction algorithm based on interaction of a scenarized fault diagnosis model and real-time data fusion; and establishing a multi-level warning system according to a prediction result. According to the method, through multi-dimensional data acquisition, nonlinear data fusion, adaptive algorithm prediction and a multi-level warning system, accurate prediction and effective prevention of equipment faults are realized. Experimental results show that compared with a traditional method and a general machine learning method, the method has remarkable advantages in the aspects of early warning accuracy, false alarm rate, early warning time in advance and the like, meanwhile, the maintenance cost is effectively reduced, the downtime of equipment is shortened, and the method has good application prospects.

The invention relates to the technical field of server temperature control, and discloses a liquid cooling server intelligent temperature control method based on local software monitoring, and the method comprises the following steps: data collection: collecting hardware temperature data and task load data of a liquid cooling server through an embedded real-time monitoring module; temperature prediction: based on the hardware temperature data and the task load data, utilizing a prediction algorithm to analyze the temperature change trend of the server in a period of time in the future; regulation and control decision making: according to the temperature change trend, generating regulation and control parameters for regulating the flow of the cooling liquid and the target temperature of the cooling liquid; the invention further discloses a liquid cooling server which comprises an embedded real-time monitoring module, a distributed control unit, a cooling loop module and a local off-line temperature control module. Through the dynamic temperature control algorithm, the distributed control unit and the local temperature control module, real-time accurate regulation and control, efficient response and independent operation under the condition of network disconnection are achieved, and the reliability and adaptability of the system are improved.

The invention provides a transmitting power control method and a TOC controller. The transmitting power control method comprises the following steps that S1, quality information of a channel is collected and obtained based on a system receiving end, the obtained quality information of the channel is evaluated, and the quality information of the channel comprises signal to noise ratio (SNR) data, received signal strength (RSSI), bit error rate (BER) data and link quality (CQI) data; the signal-to-noise ratio (SNR) data, the received signal strength (RSSI), the bit error rate (BER) data and the link quality (CQI) data can be obtained through real-time measurement or a prediction algorithm of historical data; and S2, monitoring the network load condition and user distribution in real time based on the system. According to the transmitting power control method and the TOC controller provided by the invention, the channel quality, the network load and the interference condition in a communication system are monitored in real time, and a self-adaptive adjustment mechanism is combined, so that the transmitting power can be accurately controlled, the communication quality can be effectively improved, the power consumption is reduced, and the TOC controller can stably work in a complex environment.

The invention provides a bandwidth regulation and control and visitor flow routing optimization method and system for multi-scene live broadcast, and relates to the technical field of intelligent routing, and the method comprises the steps: collecting live broadcast data, constructing a feature vector, and training a scene classification model; analyzing bandwidth requirements by using a time sequence prediction algorithm, and formulating a differentiated bandwidth allocation strategy; and an edge node load monitoring mechanism is established, a multi-target routing optimization model is constructed, and real-time scheduling optimization of visitor flow is realized. The method can effectively improve the bandwidth utilization rate and the user experience quality of the live broadcast platform.

The invention relates to the field of source network load storage, in particular to a real-time coordination control method and system suitable for regional-level source network load storage. The technical problems that in the prior art, a regional-level source network load storage real-time coordination control method may cause missing and repetition of historical data and generation of abnormal values, resulting in influence on the accuracy of a prediction model, and may cause weak generalization ability and influence the prediction precision due to improper feature selection or unreasonable model selection are solved; according to the technical scheme, the real-time load-storage coordination control method and system suitable for the regional-level source network comprises the steps that predicted output power of new energy such as a photovoltaic power station and a wind power plant and predicted data of a power grid load are obtained in real time, and short-term and medium-term predicted output of the new energy is calculated through a prediction algorithm according to historical data and real-time data; according to the method, the data quality is improved through data arrangement, and the prediction precision and efficiency are improved by enhancing the generalization ability of the model and combining short-term and medium-term prediction.

The invention discloses a dynamic multi-modal data fusion and real-time analysis method, and particularly relates to the technical field of data analysis. Carrying out standardization and time alignment on the multi-modal data, evaluating the stability of each modal in the current scene, and calculating a stability score; extracting features by using a deep neural network, fusing the features, detecting abnormal synchronization offset between modes in real time, and calculating collaborative offset intensity; based on the combined feedback of the stability score and the offset intensity, soft shielding processing is executed on the suspected abnormal mode; the future fusion risk is further evaluated through a time sequence prediction algorithm, and the model execution process is interrupted and an exception handling mechanism is triggered when necessary, so that the reliability and the safety of the multi-modal system in a complex environment are remarkably improved.

The invention provides a single-tube boost PFC circuit control method and system for single-cycle control, and relates to the technical field of data processing, and the method comprises the steps: generating a self-adaptive PWM control signal according to a duty ratio adjustment amount and a frequency compensation factor, and dynamically limiting the pulse current amplitude of a power switch tube through a peak current prediction algorithm, a load current change rate is generated based on the real-time relevance between the pulse current amplitude and the load current; setting a first monitoring point in a dynamic duty ratio change path of the PWM control signal, and extracting a duty ratio change rate and a switching frequency fluctuation parameter in real time; setting a second monitoring point in a feedback path of a load current change rate, and extracting a load current slope characteristic and a transient change amplitude in real time; and based on the parameters of the first monitoring point and the second monitoring point, generating a correction factor through a dynamic response correlation model. According to the invention, a high power factor can be realized under wide input voltage and load fluctuation.

The embodiment of the invention relates to the technical field of computers, in particular to an energy conservation and emission reduction scheme generation method and system based on a digital factory, and the method comprises the steps: obtaining real-time energy consumption data streams and emission monitoring signals of a plurality of pieces of production equipment in the digital factory; extracting dynamic energy consumption characteristics from the real-time energy consumption data stream, and identifying periodic emission parameters from the emission monitoring signal; a target energy consumption prediction algorithm is called to process the dynamic energy consumption characteristics and the periodic emission parameters, and energy consumption prediction results and emission intensity distribution of multiple time periods are generated; and generating an equipment-level optimization regulation and control scheme and a production line-level scheduling strategy according to the energy consumption prediction result and the emission intensity distribution in combination with preset energy conservation and emission reduction constraint conditions. Therefore, collaborative optimization of equipment-level real-time regulation and control and production line-level flexible scheduling can be realized.

The invention discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are improved.

The invention relates to the technical field of intelligent power grids, in particular to a power distribution network battery digital dynamic management system based on digital twinning. Comprising a data acquisition unit; the digital twinborn modeling unit is used for constructing a battery-power grid-environment multi-dimensional dynamic twinborn body and realizing virtual-real bidirectional mapping and adaptive updating by combining a multi-physics field coupling model and a long-short-term memory network time sequence prediction algorithm; a dynamic optimization unit; and executing the feedback unit. Through a distributed heterogeneous sensing network of a data acquisition unit, multi-dimensional operation data of a battery pack and a key node of a power distribution network are acquired, and a high-fidelity data set containing four-dimensional labels of a battery state, a power grid parameter, time and a position is generated in combination with a spatial-temporal feature extraction technology; the deep fusion of the full life cycle state of the battery and the global operation data of the power distribution network is realized, and the comprehensive data support covering the global is provided for the optimization decision.

The invention relates to the technical field of emergency command, and discloses an emergency command system and method based on a BIM digital base, and the method comprises the steps: building a unified data model through expanding an IFC standard, carrying out the semantic mapping and entity alignment of BIM building information, GIS geographic space data, monitoring data and an emergency plan document, and carrying out the construction of a unified data model. The method comprises the steps of generating an emergency knowledge graph with BIM building information as nodes, coupling physical simulation and an LSTM prediction algorithm, injecting monitoring data into a BIM model to deduce a disaster process, outputting a risk thermodynamic diagram and an evacuation forbidden zone, and calculating space-time accessibility of emergency resources through a reinforcement learning model according to a deduction result. An optimization scheme including an unmanned aerial vehicle path, a personnel evacuation route and an equipment scheduling instruction is generated, the instruction is synchronized to an augmented reality individual soldier terminal and a mobile command shelter through an edge computing node, a resource deployment state is labeled in real time in a BIM model, multi-terminal operation is synchronized, the timeliness and accuracy of disaster situation sensing are remarkably improved, and the disaster situation sensing efficiency is improved. And disaster loss is effectively reduced.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

Source code for a type of malware is received. For example, the source code may be source code from a type of computer virus. An Artificial Intelligence (AI) algorithm is identified. For example, the AI algorithm may be ChatGPT. The source code of the type of malware is run through the AI algorithm to produce mutated source code for the type of malware. A prediction algorithm is used to predict a signature of the mutated source code for the type of malware. For example, the prediction algorithm is trained using existing source code of different types of malware to generate a prediction model. The signature of the mutated source code for the type of malware is then compared to a signature of a potentially new type of malware to determine if the signatures are similar.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The invention discloses a new energy equipment health prediction and intelligent maintenance method based on multi-source data fusion, and the method comprises the steps: building a dynamic health state evaluation model through a system in combination with equipment operation parameters and environment variables; monitoring the running state of the equipment in real time, and grading the health degree of the equipment; based on fault prediction of data, a deep learning algorithm is introduced to carry out modeling on time series data, and possible fault occurrence time and types of equipment are predicted by capturing potential trends and change modes in operation of the equipment; a dynamic prediction function is provided, a prediction result is updated in time, and synchronism of a model and an actual equipment state is ensured; and generating an optimized maintenance plan according to the health diagnosis and prediction result. Through innovative design of a multi-source data fusion technology and an intelligent prediction algorithm, a core pain point in operation and maintenance of current new energy equipment is solved, a feasible path is provided for full-life-cycle management of the equipment, and the method has remarkable economic value and industry popularization potential.

The invention relates to the technical field of temperature control, in particular to a temperature monitoring and adjusting method for a display screen, and the method comprises the following steps: capturing temperature data of the display screen in real time through a temperature sensor, evaluating the difference and complexity of the temperature data through information entropy, and obtaining an entropy analysis result; and identifying abnormal fluctuation in the data according to the entropy analysis result, and generating an abnormal fluctuation monitoring result. According to the invention, by introducing the information entropy to evaluate the difference and complexity of the temperature data, the abnormal fluctuation can be accurately identified, the output of the heat dissipation equipment can be timely adjusted, the response speed and prediction capability of the temperature management of the display screen can be improved, the temperature control precision can be obviously enhanced, and the stability of the equipment in a variable environment can be ensured; the designed temperature trend prediction algorithm utilizes historical and current data to predict future changes, the adaptive capacity is enhanced, the maintenance cost is reduced, the energy use is optimized, the energy consumption is reduced, and the equipment performance is continuously protected by dynamically adjusting a control strategy.

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention provides an examination room abnormal behavior analysis method and system for examinees in a computer examination, and the method comprises the steps: extracting the facial expression, body posture information, keyboard tapping frequency, mouse movement track and other behavior data of the examinees, and forming a comprehensive behavior feature set; a time sequence prediction algorithm based on a long and short term memory network is combined with an attention mechanism to analyze a behavior pattern in a time dimension, and abnormal behavior information is obtained; creating a target examination room environment graph through a graph neural network algorithm, analyzing abnormal behavior information, and identifying collaborative cheating behaviors; introducing a Q learning reinforcement learning framework, and dynamically adjusting a detection strategy; performing weighted fusion on the abnormal behavior information, the collaborative cheating recognition result and the detection strategy by using an extreme gradient lifting learning algorithm, calculating a comprehensive score, and generating an abnormal behavior judgment report based on the comprehensive score; according to the method, the detection capability and efficiency of the abnormal behaviors and the cheating behaviors in the online examination are improved, and the fairness and fairness of the examination can be maintained.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention provides a mining intelligent watering dust fall method and system, electronic equipment and a storage medium, and relates to the field of watering dust fall. The method comprises the following steps: acquiring multi-source heterogeneous environment sensing data; preprocessing the multi-source heterogeneous environment sensing data to obtain preprocessed multi-source heterogeneous environment sensing data, and constructing a mining area holographic environment model based on the preprocessed multi-source heterogeneous environment sensing data; based on the holographic environment model of the mining area, extrapolating and estimating the dynamic change trend of the wind dust concentration by using a space-time sequence prediction algorithm to obtain a wind dust concentration distribution prediction result of each area of the mining area in a future time period; according to the technical scheme, dust suppression is taken as an optimization target, water mist parameters and a nozzle arrangement scheme are taken as decision variables, a mining area holographic environment model and a wind dust concentration distribution prediction result are taken as constraint conditions, an optimal spraying strategy meeting dust falling requirements is solved through a heuristic search algorithm, and the dust falling effect is improved and the water consumption is reduced through the technical scheme.

The invention relates to the technical field of photovoltaic power station operation and maintenance, and discloses a photovoltaic power station intelligent operation and maintenance and fault early warning system. A multi-band image fusion algorithm and a temperature gradient enhancement algorithm are applied to identify composite defects; establishing a defect interaction influence model to analyze the synergistic effect of different defects; constructing a defect evolution prediction algorithm to predict a defect development trend; and developing a defect-performance mapping model and a processing priority algorithm to optimize a maintenance decision. According to the method, the defect detection sensitivity and accuracy are improved, accurate identification and evolution prediction of composite defects are realized, a prospective maintenance decision is effectively supported, the operation and maintenance efficiency of a photovoltaic power station is remarkably improved, the service life of the power station is prolonged, and the safety risk is reduced.

The invention relates to the technical field of geographic information systems, in particular to a map tile data efficient access method and system based on hybrid storage and intelligent layering, and the method comprises the following steps: constructing a multi-level storage architecture, carrying out dynamic heat analysis, carrying out intelligent layering scheduling, preloading and space prediction, and carrying out transmission and access optimization. The method has the beneficial effects that the multi-level storage pool is constructed by combining the advantages of object storage and a distributed file system; adopting a dynamic tile popularity analysis model to realize hot data cache acceleration and cold data hierarchical archiving; and meanwhile, a tile request preloading mechanism is introduced, and an access path is optimized in combination with a spatial locality prediction algorithm. According to the method, the storage cost can be remarkably reduced, the tile request delay is reduced, quick response in a high-concurrency scene is supported, and the method is suitable for application scenes such as Internet map services and smart cities.

The present application relates to a multi-terminal linkage energy scheduling method and apparatus. The method comprises: acquiring multi-terminal data, wherein the multi-terminal data comprises power supply side data, power grid side data, load side data, and energy storage side data; on the basis of the acquired multi-terminal data, predicting multi-terminal electricity amount related information within a future preset time period by means of a prediction algorithm, wherein the multi-terminal electricity amount related information comprises photovoltaic power generation related information, power grid power generation related information, power consumption information of a load side device, and electricity charging and discharging related information of an energy storage device; on the basis of an energy efficiency target, acquiring energy regulation information by means of the multi-terminal electricity amount related information; and controlling the energy storage device and / or the load side device on the basis of the energy regulation information. According to the solution of the present application, by means of a multi-party linkage mechanism, a scheduling plan formed on the basis of a preset scheduling target is more suitable for actual requirements and more accurate, and the energy storage device and the load side device can be automatically controlled on the basis of the scheduling plan.