1692 results about "Power efficient" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention discloses an intelligent regulation and control method and system for full-amount resource collaborative power grid power flow, and relates to the technical field of resource collaborative power grid power flow, and the method comprises the steps: obtaining a plurality of partition groups, and combining the partition groups with a power grid topology; obtaining a plurality of voltage grades, and randomly combining the partition groups with the voltage grades to obtain a plurality of efficiency optimization groups; voltage fluctuation data and power flow burden data in the power flow regulation and control process are obtained, and a power flow evaluation model is constructed based on a long-short-term neural network; and matching and constructing a point cloud grid model according to the output of the power flow evaluation model and the efficiency optimization group, performing surface analysis on the point cloud grid model to obtain the selected efficiency optimization group, and applying the selected efficiency optimization group to power grid power flow regulation and control, thereby solving the problems of contradiction between power grid power flow calculation precision and regulation and control efficiency and insufficient dynamic adaptability.

The invention discloses an aircraft wheel cooling method and device, and the method comprises the steps: collecting the temperature of a brake disc and the air pressure of a hub in real time through a temperature and pressure sensor, calculating the temperature change rate and the pressure difference through an embedded processing module, and generating a thermodynamic initial parameter set; calculating a thermal stress distribution value of the brake disc based on the initial parameter set, and generating a cooling liquid flow and spraying angle instruction set in combination with a material threshold database; the opening degree of an annular pipeline piezoelectric valve is regulated according to an instruction set, an injection area is regulated according to flow velocity-surface area heat exchange efficiency, and waste heat is synchronously recovered to optimize power consumption; the thermal infrared imager obtains temperature field distribution, the initial parameter tolerance range is compared, the spraying angle and the flow instruction are corrected through a fuzzy PID algorithm, and temperature gradient errors are eliminated; and based on the temperature drop rate, the pressure stability and the waste heat recovery efficiency, an efficiency index is generated by using a neural network scoring model, and algorithm weights and piezoelectric valve response parameters are dynamically adjusted. The cooling efficiency is effectively improved, and the thermal stress risk is reduced.

The invention discloses a system for testing an electromagnetic shielding coupler of a testing transformer based on intelligent sensing, and relates to the technical field of electrical equipment testing, and the system comprises a flexible distributed intelligent sensing array which is composed of a plurality of high-density miniature electromagnetic sensors and a temperature-vibration composite sensing unit, a non-uniform topological structure is embedded into a seam and an insulating interface area on the surface of the electromagnetic shielding coupler. According to the test system of the test transformer electromagnetic shielding coupler based on intelligent sensing, through dynamic electromagnetic field reconstruction of the flexible distributed intelligent sensing array and multi-band interference coupling modeling, the limitation of a traditional static test is broken through; and real-time capture and multi-dimensional decoupling of shielding effectiveness attenuation characteristics in a complex alternating electromagnetic environment are realized. Compared with a traditional method, the method has the advantages that the shielding effectiveness dynamic quantization error is reduced, and the engineering guidance value of test data is improved.

The invention provides a dynamic configuration computing resource management method and system based on a power wireless terminal, and the method comprises the steps: obtaining a real-time operation state data set and a power business demand data set of the power wireless terminal, and carrying out the multi-dimensional feature fusion processing of the real-time operation state data set and the power business demand data set; the method comprises the steps of generating a resource adaptation feature set, performing real-time strategy matching processing on the resource adaptation feature set based on a preset resource adaptation rule base, generating a computing resource configuration strategy set, performing adjustment operation on computing resources of the power wireless terminal, and generating a resource allocation verification result and terminal operation efficiency feedback data. And continuously comparing the terminal operation efficiency feedback data with a preset efficiency optimization threshold value, and carrying out iterative updating processing on the resource adaptation rule base according to a comparison result. According to the invention, accurate matching between computing resource allocation and power business demand fluctuation can be realized.

The invention discloses a fan inertia support evaluation and control method considering power grid inertia space-time distribution. Based on wide area measurement system data, a power grid inertia space-time distribution model is constructed, inertia contribution characteristics of different areas are analyzed, and the influence of extra-high voltage direct current transmission on inertia distribution is considered. And calculating the inertia response power of the fan, quantifying the support contribution of the virtual inertia of the fan based on the frequency change rate and the regional equivalent inertia, and establishing an inertia support efficiency evaluation index system. A dynamic feedback control strategy is put forward, a reinforcement learning or adaptive gain adjustment method is adopted in combination with the real-time distribution condition of the inertia of the power grid, the virtual inertia control parameters of the fan are optimized, and the response accuracy is improved. Based on a wide-area coordination optimization method, a fan inertia support scheduling strategy is optimized, and the system frequency stability is improved. The method can effectively improve the frequency supporting capability of the new energy power grid, improves the new energy consumption level, enhances the operation safety of the power grid, and is suitable for a high-proportion new energy grid-connected smart power grid.

The invention relates to an air compressor energy-saving control method, device and equipment based on an AI algorithm, and the method comprises the steps: collecting the operation data of an air compressor in real time; the real-time energy efficiency ratio of each air compressor is dynamically calculated according to the energy consumption and the gas production rate, and the air compressors with the energy efficiency ratios lower than 75% are marked as low-efficiency equipment; on the basis of the historical gas consumption data, the production line schedule, the environmental parameters and the equipment state, the gas consumption demand in the future time period and the future efficiency of the air compressor are predicted through the air pressure demand prediction model and the EER prediction model; on the premise of meeting the gas demand in the future period, with the goal of minimizing the total energy consumption and the constraint conditions that the total gas production rate meets the predicted gas demand and the load rate of the frequency converter is limited to be 50%-100%, an optimal switch combination and load distribution scheme is obtained, and a corresponding control instruction is generated and executed, so that the production demand is accurately matched, and meanwhile, the production efficiency is improved. The energy consumption of the system is reduced, the equipment management efficiency is improved, and the purpose of refined and intelligent energy-saving control of the air compression system is achieved.

The invention relates to the technical field of fault diagnosis, in particular to a fault self-diagnosis system and method for a high and low temperature aging test chamber of a storage chip and computer equipment. The temperature control abnormal index is obtained through the data of the temperature control system sensor, early warning can be performed when the temperature control system has abnormal fluctuation, the performance evaluation basis of the air circulation system is provided by the air system efficiency coefficient, the heating efficiency and the refrigeration coefficient are obtained through the data of the heating and refrigerating system sensor, and the performance of the heating and refrigerating system is evaluated. An electrical stability index is obtained through data of a test system sensor, the operation stability of an electrical system is evaluated, the stability of the electrical system is detected in time, and a fault probability index is obtained through a system entropy increase rate, the electrical stability index, a thermal coupling factor, a wind system efficiency coefficient and a temperature control abnormal index. Therefore, the stability and the reliability of the equipment can be improved, the maintenance cost can be reduced, the use efficiency of the equipment is improved, and the accuracy and the safety of test results are ensured.

The invention relates to the technical field of heat dissipation control, in particular to an intelligent heat dissipation control system for a lithium battery pack, which obtains temperature data of a battery pack and an environment in real time, automatically adjusts a heat dissipation mode according to temperature change, effectively keeps the temperature of the battery pack within a preset range, avoids influence of too high and too low temperatures on battery performance, and improves the service life of the battery pack. The temperature difference of each area is analyzed through the long-short-term memory network, the heat dissipation requirement is dynamically adjusted according to the temperature data, the heat dissipation accuracy and response speed are improved, the energy consumption of invalid heat dissipation is reduced, the temperature control accuracy is improved, and in the heat dissipation efficiency adjusting process through the greedy algorithm in combination with the real-time temperature monitoring data, the heat dissipation efficiency is improved. By judging the difference between the cooling effect and the heat dissipation target, effective cooling equipment optimization scheduling is carried out, the efficiency of the heat dissipation system is improved, fine control over the temperature is achieved, and the temperature control precision and stability of the battery pack and the overall efficiency of the system are improved.

The invention discloses an industrial internet of things real-time monitoring and predictive maintenance system based on digital twinning, and relates to the technical field of industrial digital twinning operation and maintenance, the system comprises a multi-modal data acquisition module, a sensor network is deployed, and edge calculation preprocessing is carried out; the digital twinning construction module is used for constructing a high-precision model by fusing a physical law and deep learning; the real-time monitoring module is used for detecting abnormity by using a space-time diagram neural network; the predictive maintenance module is used for optimizing a maintenance strategy in combination with a probabilistic algorithm; and the man-machine interaction module supports AR / VR and brain-computer interface operation. In addition, the system integrates functions of block chain security, energy management and the like, and realizes full-life-cycle intelligent management of equipment. The operation and maintenance efficiency of the industrial equipment is greatly improved. The data acquisition precision reaches the nanoscale, and the early warning time is advanced to 72 hours; the maintenance cost is reduced, and the equipment availability is improved; the AR interaction enables the operation efficiency to be improved and the training period to be shortened. And meanwhile, energy consumption reduction is realized.

The invention relates to the technical field of battery management, and discloses a battery management system with an active monitoring function, which comprises a processor, a battery state monitoring module, an active equalization control module, a performance evaluation module and a fault early warning module. The battery state monitoring module collects various parameters of a battery at different stages, and generates an abnormal signal by using a dynamic threshold algorithm. The active equalization control module adjusts an equalization strategy based on a fuzzy logic algorithm, and a fuzzy rule base can be updated. And the performance evaluation module tracks and evaluates an equalization process, analyzes equalization efficiency and quality, and optimizes an equalization strategy accordingly. The fault early warning module displays abnormal signals in a grading mode and carries out early warning, and the processor is further connected with the aging feature extraction module and the thermal runaway prediction module to achieve battery life prediction and thermal runaway early warning. According to the invention, comprehensive monitoring, efficient equalization control, accurate performance evaluation and reliable fault early warning of the battery are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention belongs to the technical field of optical storage cooperative control, and discloses a new energy grid-connected optimization control method based on an energy storage system, which comprises the following steps: collecting a power grid impedance fluctuation amplitude, a harmonic distortion rate and environment temperature data, obtaining an initial SOC value, correcting the SOC value, calculating an adjustable power margin and a response delay parameter of a photovoltaic unit, and calculating the optimal power grid impedance fluctuation amplitude and the optimal power grid impedance fluctuation amplitude. Generating an efficiency responsibility table; constructing a response cluster, generating a frequency modulation task package, and further generating a three-dimensional efficiency mapping table; the inertia demand is dynamically calculated in combination with the power grid frequency change rate, a responsibility transfer strategy is automatically activated in a high-altitude and high-temperature scene, and an efficiency instruction set is generated; analyzing response deviation by simulating typical disturbance of a high-altitude weak power grid scene, positioning a deviation source in combination with historical data, and dynamically adjusting an efficiency weight correction factor; and updating a responsibility allocation rule of the efficiency responsibility table, disrupting the low-efficiency cluster, and recombining the high-adaptability unit.

The invention relates to the technical field of photovoltaic power station management, and discloses a photovoltaic power station Internet of Things intelligent management and cloud edge collaborative optimization system. The system comprises an operation data acquisition module which is used for acquiring power station data in multiple dimensions, quantitatively identifying an abnormal operation state and generating a management early warning signal; the equipment state evaluation module is used for extracting equipment characteristics and dynamically evaluating the self-maintenance capability to obtain a state evaluation reference value; the edge processing module is used for monitoring parameters such as data traffic and predicting and analyzing edge processing efficiency to obtain an edge calculation evaluation value; the cloud edge collaboration module is used for jointly analyzing collaborative optimization efficiency and generating a regulation and control signal; the optimization parameter generation module is used for generating equipment maintenance and computing power distribution parameters; the system further comprises an authority control module and a dynamic adjustment module. According to the system, intelligent management and cloud edge collaborative optimization of the photovoltaic power station are realized, the operation efficiency and reliability of the power station are improved, and the safety and dynamic adjustment capability are enhanced.

The invention discloses a ship main power and auxiliary power real-time switching method and system based on large model optimization, and relates to the technical field of ship power system control, and the method comprises the steps: collecting the operation state data and environment parameters of a ship main power system and an auxiliary power system in real time through a multi-mode sensor array; and performing space-time alignment and noise suppression processing on the operation state data and the environment parameters, inputting the feature subset into a lightweight large model inference engine, generating a multi-target optimization decision set, executing a main and auxiliary power dynamic switching control sequence in a preset time window according to the multi-target optimization decision set, and outputting the main and auxiliary power dynamic switching control sequence. And generating a self-optimization log including a switching efficiency evaluation report and a model iteration suggestion based on the deviation degree of the switching feedback data and a preset performance index. According to the ship main and auxiliary power real-time switching method and system based on large model optimization, stable operation of a ship power system is guaranteed, and meanwhile the service life of equipment is prolonged.

The invention relates to the technical field of energy management and intelligent scheduling, provides a photovoltaic energy storage motor collaborative scheduling optimization system based on multi-modal data, and constructs a collaborative scheduling system integrating data intelligent processing, high-precision prediction and multi-objective optimization by fusing photovoltaic module, energy storage equipment, motor and power grid side multi-modal data. Data quality is improved through Z-score cleaning and Kalman filtering, accurate prediction of photovoltaic power, load demand and energy storage aging is realized by using a long-short-term memory neural network and an Arrhenius equation, and a multi-target collaborative scheduling strategy including economic, reliable and environmental benefits is constructed based on a particle swarm optimization algorithm. Dynamically adjusting photovoltaic output distribution, energy storage charging and discharging and a motor speed regulation ratio; systematic innovation of data value deep mining, multi-target dynamic balance, equipment full-life-cycle management and renewable energy source efficient consumption is realized, and the intelligent level and comprehensive operation efficiency of the micro-grid are remarkably improved.

The invention discloses an electrical equipment operation data monitoring and management system and method.The system comprises a data monitoring unit, a scheme design unit, a fault maintenance unit and an optimization processing unit, the electric power resource configuration efficiency and the electric power equipment operation state are monitored and analyzed by collecting the operation data of electrical equipment; therefore, the fault risk degree of the electrical equipment is predicted and evaluated, an electrical equipment maintenance scheme is generated, and the fault risk of the electrical equipment is maintained in advance, so that the fault risk is suppressed in advance. The electrical equipment maintenance scheme is optimized and upgraded by performing optimization maintenance on the electrical equipment and comparing, analyzing and evaluating the maintenance efficiency of the electrical equipment, and the working efficiency of maintenance of the electrical equipment is improved and the stability of power resource configuration is ensured by judging the fault risk factor of the electrical equipment and performing optimization maintenance in a targeted manner. And effective configuration of power resources and equipment maintenance is realized.

The invention provides a garbage power generation optimization method, a storage medium and computer equipment. The garbage power generation optimization method comprises the steps of S1, garbage classification and preliminary data processing; s2, a boxing algorithm is applied, and the garbage is combined and optimized; and S3, the garbage incineration process is monitored, regulated and controlled. According to the invention, heat energy release in each incineration process can be ensured to be maximized; by introducing a heuristic method and combining a boxing algorithm, not only can garbage grouping and combustion strategies be optimized, but also dynamic adjustment can be made according to real-time monitoring data, the incineration efficiency and stability are improved, the stability of heat energy release is ensured, and then the heat energy recovery efficiency is improved; a systematic and global optimization scheme is formed, all links of garbage treatment can be considered, and the overall efficiency is improved; the method can be widely applied to waste incineration power plants, can greatly improve the heat energy utilization rate and the power generation efficiency of the incineration power plants, reduces emission of harmful substances, and improves environmental protection benefits.

The invention relates to the technical field of automatic control, in particular to an automatic control method of a BOG low-temperature recovery system for a storage tank for an LNG (liquefied natural gas) ship, which comprises the following steps: acquiring BOG pressure fluctuation data and temperature difference trend, analyzing pressure rise rate and cooling efficiency change, identifying a pressure peak value and a cooling lag section, screening a low-efficiency section and marking an optimization unit. And identifying an energy matching degree, analyzing energy loss distribution, identifying an over-limit node and a fault signal, and obtaining an energy and fault synchronous control table. According to the system, through real-time data monitoring and intelligent analysis, the BOG recovery process can be accurately controlled, energy circulation and cooling treatment can be optimized, the efficiency of a low-temperature recovery system can be improved, BOG pressure and temperature difference changes can be monitored, pressure fluctuation and cooling lag can be analyzed, continuous improvement of condensation efficiency can be ensured, control parameters can be accurately adjusted, energy loss is avoided, and the energy efficiency of equipment can be improved; energy consumption and equipment fault management are optimized, and efficient and safe operation of the low-temperature recovery system is ensured.

The invention discloses an air conditioner efficiency test system and a control method of an air conditioner efficiency test platform. The method comprises the following steps: dynamically adjusting the air dry bulb / wet bulb temperature of a test room of an existing test bed based on a dynamic temperature control adjusting technology; acquiring a real-time building thermal load of the test room through a building thermal load prediction simulator; the dynamic change of the air dry-bulb temperature of the test room is calculated in real time through the indoor air dry-bulb temperature prediction simulator; and the dynamic change of the air wet bulb temperature of the test room is calculated in real time through the indoor air wet bulb temperature prediction simulator. According to the invention, the problem that the dynamic operation energy efficiency and performance parameters of the tested air conditioner cannot be obtained through the enthalpy difference method test bench because the existing enthalpy difference method test bench cannot control the dynamic change process of the building thermal load of the test room along with the refrigeration / heating capability of the tested air conditioner in the prior art is solved; and the actual operation performance level of the air conditioner cannot be reflected.

The invention discloses a commercial building energy monitoring and intelligent control method and device and a storage medium, and belongs to the technical field of building intelligent control, and the method comprises the steps: collecting data, building a nonlinear mapping relation, and generating an energy consumption demand prediction tensor; injecting an adversarial disturbance sample, and evaluating the robustness of the prediction model; in combination with the energy consumption baseline, performing cross confirmation and correction on the prediction data exceeding the threshold value; performing attribution analysis on the corrected energy consumption sequence to generate an energy consumption attribution map; adjusting the solution of a multi-objective optimization function according to the atlas, and generating an optimal cooperative control strategy; and the comprehensive efficiency is used as a reinforcement learning reward, strategy parameters are iteratively updated, and a control knowledge base is formed. According to the method, a closed-loop control framework integrating robust demand prediction, dynamic attribution analysis, collaborative optimization decision and a self-evolution strategy is adopted, intelligent regulation and control of building energy consumption can be realized, and the long-term adaptive optimization capability is improved.

The invention relates to the technical field of photovoltaic power generation. The invention provides a photovoltaic module cooling method, system and device and a storage medium, and the method comprises the steps: collecting the working condition parameter data of a photovoltaic module and the environment parameter data in a corresponding range of the photovoltaic module in real time, and obtaining a basic data set; based on the basic data set, a relation model between the working condition parameters and the environmental parameters of the photovoltaic module is established, the relation model is utilized to predict the temperature change trend and the output power change condition of the photovoltaic module under different environmental conditions, and a prediction result is obtained; and starting a liquid cooling, air cooling and phase change material collaborative cooling mechanism according to the prediction result, continuously monitoring the working condition parameters of the photovoltaic module in the cooling process, and dynamically adjusting and optimizing the cooling mode and the corresponding parameters according to the monitoring result. The problems that an existing photovoltaic module lacks an effective temperature adjusting means, the output power is obviously reduced when the working temperature rises, and the overall efficiency and economic benefits of a photovoltaic power generation system are reduced are solved.

The invention relates to an optical storage and charging integrated station energy storage system scheduling method based on a CNN-SAC algorithm, and belongs to the technical field of optical storage and charging integrated station energy storage systems. According to the technical scheme, an energy storage system operation efficiency evaluation model and a battery capacity decline prediction model are constructed; establishing an operation analysis model with the optimal economic benefit of the optical storage and charging integrated station as a target; and an energy storage system optimization mechanism based on deep reinforcement learning is established, and autonomous learning and decision making of an optimal strategy are realized by applying a CNN-SAC (Consensus Networks-Consensus Consensus) algorithm and a deep reinforcement learning algorithm. According to the method, a multi-dimensional operation analysis model is constructed by taking the optimal economic benefit of the photovoltaic charging station as a target, so that the operation cost can be reduced, the synergistic effect of photovoltaic power generation and an energy storage system can be fully utilized, and the overall economic benefit is improved. By introducing the CNN-SAC deep reinforcement learning algorithm, the efficient intelligent scheduling of the energy storage system of the optical storage and charging integrated station is realized, and the operation efficiency and decision precision of the system are remarkably improved.

The invention discloses a power generation efficiency optimization method for a floating photovoltaic power generation system. The method comprises the following steps: analyzing the motion response of a floating body in a marine environment, and determining the boundary condition of the floating body swinging under the action of waves; based on hydrodynamic analysis software AQWA, a hydrodynamic analysis model of the floating body is built, and the pitching angle of the floating body is verified; in combination with an inclined surface irradiance calculation model, an improved particle swarm algorithm is adopted for optimization, and the initial inclination angle of the photovoltaic panel when the photovoltaic panel receives solar radiation energy to the maximum extent is solved; in combination with different wave conditions of a certain sea area, simulation software is used for performing dynamic simulation on the floating body and the assembly, the influence of different environmental conditions on the power generation efficiency is analyzed, and the effectiveness of the scheme is verified. According to the method, the optimal initial angle is determined by optimizing the initial mounting inclination angle of the assembly, fully considering the movement condition of the floating body under the action of waves and combining the influence of different wave conditions, the radiation quantity received by the assembly is maximized, and the defects in the aspects of laying design, dynamic adaptability and power generation efficiency in the prior art are overcome.

Extension of power efficient paging in a wireless network. In some examples, information is buffered for a time period corresponding to a paging slot, or both the paging slot and at least one adjacent slot. The buffered control information is processed to detect paging downlink control information (DCI) and a tracking reference signal (TRS). Based on the processing, the paging message is scheduled in a physical downlink shared channel (PDSCH) in the paging slot. In some examples, quasi-co-located (QCL) processing may be performed on the TRS and the paging DCI paging message, and a paging message is scheduled, based on the QCL processing, in a physical downlink shared channel (PDSCH) in a same or offset paging slot.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.

The invention discloses an off-line function verification system for an intelligent electric actuating mechanism, and the system comprises an off-line function verification unit which achieves the data interaction through the connection with a system signal terminal unit of the intelligent electric actuating mechanism, so as to achieve the off-line function verification function of the intelligent electric actuating mechanism. The offline function verification unit is an integrated module and comprises a man-machine interaction module, a host control module, an interface module, an instruction control module, a control power supply control module and a power supply control module; the problems that a large amount of manpower and time are consumed, the test process is tedious, the work efficiency of the original test process is low, human errors and test record detection errors are prone to occurring, data analysis depends on manpower, and the error risk is increased due to the fact that a traditional test method at the present stage consumes operations such as frequently changing used tools and modifying wiring in the test process are solved.