2456 results about "Dynamic energy" patented technology

The invention relates to the technical field of intelligent energy management, in particular to an energy consumption prediction and optimization system for energy-saving management and control, which comprises a data acquisition core module, a dynamic energy consumption prediction core module, an intelligent optimization control core module, a self-adaptive calibration core module, a user interaction core module and the like. The data acquisition module acquires energy consumption, equipment state and environment data from multiple sources; the dynamic energy consumption prediction module fuses improved time series decomposition and a multi-modal LSTM model to realize accurate prediction; the intelligent optimization control module is combined with strategies such as time-of-use electricity price and equipment linkage to generate an optimal instruction; the adaptive calibration module dynamically optimizes the model through Kalman filtering and incremental learning; the user interaction module supports visual display and strategy self-definition; in addition, the system is provided with an edge computing node to guarantee offline operation, an SM4 algorithm and a block chain technology are adopted to guarantee data security, and the system is compatible with various industrial protocols. The energy utilization efficiency is effectively improved, the operation cost is reduced, and the system safety and reliability are enhanced.

The invention discloses a BIM (Building Information Modeling)-based building energy consumption prediction and dynamic energy-saving optimization method, which comprises the following steps of: extracting key characteristic parameters of geometry, materials and construction of a building from a BIM model, and obtaining multi-dimensional data of building types, structural characteristics, material attributes and environmental factors; cleaning the acquired multi-dimensional data, removing repeated, wrong or abnormal data points, and performing standardization processing through a minimum-maximum scaling method or performing normalization processing through a z-score standardization method; the adjusted AI model is used for building energy consumption prediction, cost models of material, labor force, maintenance and energy cost factors are combined, and a dynamic energy-saving strategy is generated through a genetic algorithm; the generated dynamic energy-saving strategy is converted into a specific system adjusting instruction to be applied to an actual building, and energy-saving control is achieved by adjusting the air volume of a building ventilation system, the brightness of an illumination system and the temperature set value of a heating ventilation air-conditioning system.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention relates to the technical field of power distribution, in particular to a construction method and system for a digital twinborn model of a power distribution network. Establishing a digital element of the multi-physical field coupling characteristic of the power distribution network equipment based on quantum state space mapping, and simulating a collaborative degradation process of insulation aging and electromagnetic interference of the equipment; constructing a dynamic energy flow entanglement relationship among the nodes of the power distribution network according to the equipment degradation characteristic tensor; based on the quantum entanglement relationship between the device degradation feature tensor and the topology connection hypergraph, establishing a time-space coupling eigenmode of the device multi-physical field state and network energy flow distribution, and generating a quantum topology covariant tensor of a twin model; and carrying out dynamic fusion on real-time characteristic data and the quantum topology covariant tensor to form the digital twin model of the power distribution network. According to the method, the digital twin model capable of accurately reflecting the power distribution network equipment aging and energy transmission spatio-temporal evolution law is constructed, and a high-fidelity simulation platform is provided for power distribution network state evaluation, fault early warning and optimization regulation and control.

The invention discloses an energy efficiency data detection processing method and system for a data center, and the method comprises the steps: deploying a sensor and a distributed collection network, and achieving the collection of energy efficiency data through the calculation of an energy efficiency change rate and the adjustment of a self-adaptive sampling frequency; dynamic energy efficiency prediction is realized by adopting adaptive time window adjustment, short-term and long-term error feedback correction, energy efficiency trend modeling and multi-level prediction fusion; an energy efficiency perception load evaluation index is introduced to realize energy efficiency maximization; an intelligent group decision model is constructed, and global allocation of computing resources is realized through dynamic balance scheduling optimization, task allocation stability analysis and adaptive convergence adjustment; based on an energy efficiency autonomous learning framework, initial strategy training, reinforcement learning optimization scheduling strategy, adaptive tuning and dynamic adjustment are carried out by utilizing imitation learning, and adaptive optimization is realized. According to the invention, under the condition that the business of the data center changes rapidly, the allocation of the computing resources can be adjusted accurately, rapidly and dynamically, and the optimal energy efficiency is achieved.

The invention relates to the technical field of storage equipment management, in particular to an intelligent control method and system for storage equipment, and aims to solve the problems of task allocation, path optimization and equipment collaboration in a complex storage scene. According to the method, a storage equipment function model comprising an upper-layer frame model and a lower-layer equipment model is constructed, task allocation and path planning are optimized by using a neural network model in combination with task requirements, and a storage equipment control instruction is generated. In the task execution process, task priorities and path planning are adjusted in real time to adapt to dynamic environment changes, and it is ensured that tasks are completed efficiently. According to the method, a task distribution diagram and a path planning scheme are further optimized through a dynamic task diagram and a dynamic energy field, the problems of equipment congestion, too high energy consumption and task delay are effectively solved, the collaboration efficiency and the resource utilization rate of storage equipment are improved, and the method is suitable for various complex storage scenes.

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

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 relates to the technical field of electric vehicles, and discloses a full-automatic AGV charging collaborative optimization system which comprises an energy consumption prediction module, a consensus mechanism design module and an AGV scheduling optimization module. The energy consumption prediction module is used for acquiring a pose topological feature vector and a potential margin attenuation gradient, constructing a multi-dimensional input vector, and generating a dynamic energy consumption prediction value based on an energy consumption track residual model of multi-physics field coupling; the consensus mechanism design module is used for constructing and verifying a dynamic consensus factor and providing a priority decision basis; the AGV scheduling optimization module is used for discretizing an operation space of a battery swap station, generating a space-time folding unit, constructing a space-time conflict resolution atlas, and generating an optimal battery swap sequence and a collision avoidance path; through multi-module cooperation, efficient cooperation of multi-AGV charging and battery swap is realized, and congestion of the battery swap station and resource waste are avoided.

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the technical field of energy management and optimization, and discloses a resource allocation dynamic simulation optimization method based on energy condition constraints, which comprises the following steps: step 1, deploying a sensor network and a data acquisition system, monitoring an energy supply condition and a consumption mode in real time, and performing multi-dimensional collection on data; 2, the collected data are cleaned and analyzed, a dynamic energy condition report is generated, and the report comprises energy supply and demand prediction, energy price fluctuation and carbon emission intensity information; and step 3, constructing a resource allocation dynamic simulation model according to the collected data and reports. The resource allocation dynamic simulation optimization method based on the combination of real-time energy state recognition and self-adaptive weight adjustment is adopted, the technical effect of flexible scheduling according to dynamic changes of energy conditions is achieved, and it is ensured that the resource allocation process can be responded in time, and the system operation efficiency is optimized.

The invention discloses an energy-saving control method and system for an airplane ground air conditioning unit, and belongs to the technical field of air conditioning system energy saving.The method comprises the steps that internal and external environment parameters of a cabin are collected in real time, temperature prediction is conducted based on the internal and external environment parameters of the cabin, and a predicted temperature change curve is constructed; running state parameters of the aircraft ground air conditioning unit are called, dynamic energy-saving control is carried out in combination with the predicted temperature change curve, and an energy-saving control strategy is formulated; cabin internal variable parameters are introduced to optimize the energy-saving control strategy, and an optimized energy-saving control instruction set is generated; and the optimized energy-saving control instruction set is executed for cooperative driving, cooling circulation is conducted according to the cooperative driving result, and self-adaptive energy-saving control over the aircraft ground air conditioning unit is achieved. The technical problems of high energy consumption, unstable temperature control and low equipment operation efficiency caused by the lack of precise response capability of an air conditioning unit to dynamic changes of internal and external environments of a cabin in the prior art are solved.

The invention discloses a distributed wind set virtual inertia active support control method suitable for a weak power grid, and the method comprises the steps: dynamically adjusting the inertia support strength according to the frequency disturbance of a power grid through adaptive virtual inertia adjustment; multi-mode cooperative control is adopted, and an operation mode is switched according to the frequency deviation and the change rate so as to provide an optimal response; by means of dynamic damping compensation, frequency oscillation is suppressed in combination with multiple factors and an optimization algorithm, and system damping is improved; multi-time-scale cooperative control is implemented, and inertia support, power adjustment and rotating speed recovery are coordinated at a millisecond level, a second level and a minute level; and rotor kinetic energy graded release is adopted, kinetic energy release is accurately controlled according to the frequency drop degree, and sudden rotation speed drop is avoided. In addition, additional frequency damping control is introduced to enhance suppression of frequency fluctuation. The method can significantly improve the response speed and adaptability of the wind turbine generator to the frequency fluctuation of the weak power grid, ensures the frequency stability of the power grid, and provides key technical support for stable grid connection of wind power in the weak power grid.

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

The invention relates to the technical field of air conditioner control, and discloses an intelligent building air conditioner group control energy-saving system and method based on multi-source data fusion, and the method comprises the steps: collecting heterogeneous tracks of the activities of a person in a building, and constructing a space-time energy demand coupling map after cross-modal coupling and abnormal outlier suppression processing; an optimized hang compensation factor beta'is obtained based on the atlas, and an energy demand significance region and a non-energy demand significance region are divided; and finally, making an intelligent air conditioner group dynamic energy-saving control strategy according to a region division result and the optimized hang compensation factor. According to the method, the air conditioner can be accurately regulated, the energy utilization efficiency of the intelligent building air conditioning system is remarkably improved, and energy consumption is reduced.

The invention discloses a power supply management method and system based on a new energy charging pile, and relates to the technical field of power supply management, and the method comprises the steps: collecting power supply source data, calculating a charging pile power supply risk, calculating a risk weight through an entropy weight method, and constructing a two-stage LSTM model to predict photovoltaic power generation and EV demand data. And determining the power supply priority of the charging pile based on the risk weight, constructing a dynamic energy distribution model, determining the power distribution of a power supply source by using a Pareto optimization algorithm, and carrying out charging power distribution by using a stable matching algorithm according to a utility function. According to the method, influence degrees of photovoltaic power generation fluctuation risks, energy storage performance risks and power grid price fluctuation risks are quantified through entropy calculation, importance of risk indexes is accurately reflected as weights, data optimization of an input layer of a double-stage LSTM model is guided, the risk indexes are brought into priority calculation, the power supply weight of high-risk energy is reduced, and the power supply efficiency is improved. And power supply fluctuation or cost increase caused by high risk is avoided.

The invention discloses a mobile communication load collaborative scheduling system and method based on energy efficiency perception, and belongs to the technical field of mobile communication, energy consumption data of each functional module in a base station and external network environment parameters are collected in real time through a multi-source sensor network in the base station, and a dynamic energy efficiency map is generated; based on historical energy efficiency data and real-time load characteristics, constructing an energy efficiency construction prediction model, and predicting an energy efficiency hotspot area; based on the historical movement track of the user and the spatial and temporal distribution characteristics of the service flow, the movement trend of the user and the change trend of the service flow are analyzed, and in combination with an energy efficiency prediction model, a potential overload base station is identified, and overload early warning is triggered; after receiving the early warning information, screening candidate target base stations according to a preset radius by taking the overload base station as a core; and generating a load idle list in a descending order according to the real-time energy efficiency ratio of the candidate base station, and performing energy efficiency sensitivity priority grading on the to-be-migrated service, thereby generating a collaborative scheduling strategy and executing load migration.

The invention provides an injection molding machine mold opening and closing parameter optimization method and system oriented to energy conservation and efficiency improvement, and relates to the technical field of industrial process intelligent control. The method comprises the steps that mold temperature, hydraulic pressure and vibration signals are collected in real time through a multi-source sensor, and a composite optimization target of a dynamic energy consumption coefficient and a process stability index is constructed; an improved multi-target genetic algorithm is adopted to iteratively generate a candidate parameter set meeting energy consumption and stability constraints, and the algorithm fuses hydraulic system safety boundary dynamic constraints and an adaptive variation strategy; a parameter adjustment effect is rehearsed through a digital twin model, and motion track deviation is calibrated online in combination with a recursive least square method, so that parameter dynamic injection is realized; and triggering abnormal working condition recovery and material self-adaptive optimization based on a closed-loop feedback mechanism. The system comprises a multi-source data acquisition module, a composite optimization calculation module, a dynamic parameter injection module and a closed-loop feedback control module, and the real-time performance and generalization ability are improved through an FPGA hardware acceleration optimization engine and a transfer learning algorithm.

The invention provides a method and a system for drawing a regional energy consumption map of an electric vehicle. Wherein the acquired driving data and external meteorological information comprise multiple parameters and temperature gradients in the GB32960 standard. A dynamic grid model is constructed based on administrative division and a road network, track points are mapped into space grid units by using an adaptive algorithm, and the units combine information such as gradient, temperature and traffic flow. A trajectory analysis technology is adopted to segment a driving segment crossing a grid boundary, and feature vectors are generated; by establishing a mapping relationship between the feature vector and the energy consumption contribution degree, the original energy consumption is calculated, and multi-factor correction is performed to generate a dynamic energy consumption reference value. And associating the distribution density of the charging piles with the traffic flow weight by using a space-time coupling diffusion model, and generating a three-dimensional dynamic map through a rendering engine. According to the technical scheme provided by the embodiment of the invention, the accuracy of electric vehicle energy consumption estimation and the scientificity of charging facility layout are improved.

An energy-saving and environment-friendly park air monitoring device, comprising a stand (1), a detection apparatus body (2) being mounted on the stand (1) by means of a first mounting bracket, and a solar panel (3) being further mounted on the stand (1) by means of a second mounting bracket. A filter cylinder (21) is mounted on the detection apparatus body (2), a plurality of openings (4) being formed in the filter cylinder (21), a filter plate (5) being fixedly mounted in the openings (4). A dual-shaft motor (6) is fixedly mounted at the bottom of the filter cylinder (21). When dust and impurities adsorbed and accumulated on the filter plate (5) need to be removed, the dual-shaft motor (6) is started, the two output ends of the dual-shaft motor (6) rotate to drive a L-shaped rod (7) and a driving gear (8) to rotate synchronously, and the rotation of the L-shaped rod (7) can drive a cleaning brush (9) to clean the filter plate (5). In addition, by means of a transmission mechanism, the kinetic energy from the driving gear (8) can be transmitted to a blocking mechanism, so as to drive the blocking mechanism to rotate for blocking the openings (4), such that the dust raised during cleaning of the filter plate (5) is prevented from passing through the filter holes in the filter plate (5) and entering the filter cylinder (21), thereby avoiding contamination of a detection probe by the dust during the cleaning of the filter plate (5), and achieving a protective effect on the detection probe.

The invention relates to the technical field of electric power reactive power compensation, in particular to a novel static dynamic reactive power compensation device and a control method thereof, and the device comprises a power grid state monitoring module, a reactive power demand calculation module, a compensation control execution module, a harmonic suppression module and a dynamic energy storage support module. According to the method, through real-time extraction and segmented analysis of the dynamic operation parameters of the power grid, accurate perception of the state of the power grid is realized, the transient characteristics of operation are represented, an accurate basis is provided for subsequent compensation, decomposition and classified accumulation of reactive power requirements are realized, inductive and capacitive power differences are accurately identified, the calculation accuracy and efficiency are improved, and load adaptation is optimized; in harmonic suppression, resolution and response speed are enhanced through signal separation and frequency correction, energy storage and compensation linkage is improved through dynamic energy storage optimization, dynamic response capability is enhanced, loss is reduced, voltage stability and power factors are improved, stable guarantee is provided for complex fluctuation scenes, and electric energy is saved.

The invention provides a converter station in-station equipment energy consumption analysis model and method. Belongs to the field of converter station equipment energy consumption analysis. According to the method, a three-dimensional data acquisition system covering equipment in the whole station is established, electrical measurement data of the equipment is obtained in real time through a monitoring system, and meanwhile, a converter valve trigger pulse, a cooling water pump rotating speed adjusting signal and a circuit breaker opening and closing state are captured through an equipment digital twin interface. And carrying out real-time feature extraction by means of an edge computing node, and triggering high-speed data recording for a commutation failure event. And correcting abnormal data by combining a sliding window dynamic cleaning mechanism with a wavelet threshold denoising algorithm, and extracting a current harmonic distortion rate, a voltage fluctuation coefficient and equipment vibration spectrum characteristics. And further establishing a multi-dimensional characteristic matrix containing equipment operation conditions, environmental parameters and power grid interaction characteristics, performing energy consumption prediction by using a hybrid model, and generating a dynamic energy efficiency evaluation index which is applied to energy saving strategy optimization.

The invention provides a photovoltaic power station energy efficiency evaluation method. A data acquisition layer module, a dynamic energy efficiency model module, an energy efficiency evaluation module and a fault positioning module are included. Wherein the dynamic energy efficiency model module establishes a theoretical power generation model based on an AI prediction technology, and the model dynamically updates model parameters according to real-time environment parameters and equipment states; the energy efficiency evaluation module calculates the actual energy efficiency according to the theoretical generating capacity predicted by the dynamic energy efficiency model and the actually collected inverter output power data, and performs energy efficiency loss grading; the fault positioning module discriminates an abnormal group string through a group string current dispersion rate and determines a hot spot position by means of an infrared image, relates to the technical field of photovoltaic power generation, and achieves precise and real-time power station energy efficiency management through fusion of dynamic environment correction, equipment health degree diagnosis and energy efficiency loss classification, thereby improving the efficiency of power station energy efficiency management. The problems that the evaluation deviation is large and the accuracy is reduced along with time in the existing evaluation technology are effectively solved.

The invention relates to the technical field of charging pile control, in particular to a charging pile dynamic energy-saving method based on a cooling algorithm, and the method comprises the following steps: carrying out the real-time data collection of a charging pile cooling device, and collecting the temperature and flow velocity of a cooling medium, and the temperature and voltage data of a battery in a charging state; according to the method, the energy consumption evaluation performance function is constructed based on the data, and the analysis of cooling energy consumption optimization is achieved in combination with the charging efficiency. The calculation of the gradient data provides an optimization direction in flow and temperature parameter adjustment, and the unreasonable phenomenon of cooling resource allocation is avoided. Iterative optimization of a parameter adjustment result is combined with continuous monitoring of energy consumption and equipment efficiency, and it is ensured that the cooling system can still operate in a dynamic optimization mode under complex conditions. The prediction analysis of the temperature change range in the peak period is combined with the real-time data to adjust the cooling resource allocation rule, and the adaptability of the cooling system in a complex scene is improved.

The invention belongs to the technical field of energy storage resource regulation and control, and particularly relates to a distributed energy storage network resource regulation and control decision-making method, which comprises the following steps of: collecting and processing multi-level data; a two-way interaction mechanism with a user side is established, the electricity demand and the energy storage intention of the user are collected, and a demand response signal is sent to the user to encourage the user to adjust the electricity consumption behavior when the electricity is insufficient or the electricity price fluctuates; and based on the multilevel data and the user demand response result, constructing a dynamic energy routing model of the distributed energy storage network, and predicting future energy demand and supply conditions. According to the method, multi-level data can be comprehensively utilized, the accuracy of energy demand prediction is improved, the flexibility and the intelligent level of a regulation and control strategy are enhanced, a global optimization method is adopted for resource regulation and control, and the robustness and the reliability of the system are improved.

The invention discloses a method and a system for monitoring energy conversion carbon measurement based on dynamic carbon monitoring. The method comprises the following steps: acquiring energy flow data, carbon emission parameters, equipment working conditions and environment data in real time; constructing a carbon emission correction model containing a dynamic energy conversion coefficient and real-time fuel components; adopting an efficiency compensation function and a working condition self-adaptive correction function related to an equipment load rate; and visually displaying the real-time numerical value of the carbon emission and triggering the early warning of the carbon emission based on the model output. The system comprises a multi-source data real-time acquisition module, a dynamic carbon measurement model and a real-time monitoring and early warning platform, the method has the advantages of accurate decision support, environmental supervision enhancement and adaptation to energy transformation.

The invention discloses a control method and system of a multi-source intelligent power manager based on 5G communication, and the method comprises the steps: obtaining multi-source heterogeneous power data of the multi-source intelligent power manager, constructing a dynamic energy topology through employing a multi-scale pulse fusion tensor decomposition algorithm, and generating a power state joint tensor with time-space alignment; inputting the power supply state joint tensor into a physically constrained adversarial prediction network, and outputting an energy dynamic balance vector including power supply margin prediction; performing multi-target dynamic game optimization on the energy dynamic balance vector, and outputting an anti-interference multi-source cooperative power supply strategy matrix; and inputting the multi-source cooperative power supply strategy matrix into a distributed cooperative control framework driven by edge computing, and finally outputting a multi-source intelligent control instruction set meeting low delay and high reliability. According to the embodiment of the invention, the flexibility and response speed of power management can be improved, and the stability and reliability of power supply are ensured.