344 results about "Weighted average method" patented technology

The invention provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

The invention provides a product quality detection method and system for an extruder, and the method comprises the steps: collecting image data of the surface and interior of a product in real time, and installing an intelligent sensor at the same time to collect multi-modal sensing data in the production process; processing the surface image data by using a convolutional neural network, identifying surface defects and internal defects in real time, and integrating the surface defects and the internal defects to obtain a final defect graph; feature extraction is carried out from the stored data and defect graphs, and real-time monitoring and anomaly detection are carried out; constructing a self-adaptive control model, and adjusting operation parameters of the extruder in real time according to an analysis result; and multiple machine learning algorithms are selected for integration, and prediction results of multiple models are fused by using a weighted average method to obtain an optimal product quality detection result. According to the invention, high-precision detection of surface and internal defects of an extruded product can be realized, and process parameters in the extrusion process can be monitored and optimized in real time, so that the consistency of product quality and the production efficiency are greatly improved.

The invention discloses a water level monitoring and management method based on multi-source information fusion, and relates to the technical field of water level monitoring and management. Comprising the following steps: acquiring a water level and water level related data by adopting a sensor, and acquiring surface feature information and meteorological data through GIS data; performing data cleaning and standardization on the collected data; a convolutional neural network is adopted to analyze infrared cloud picture data obtained by satellites to predict precipitation, a weighted average method is adopted to carry out multi-source information fusion, and water level prediction is carried out through a hydrological model; and performing real-time water level monitoring and water level management decision according to accurate water level data obtained by data fusion. Through the above steps, the system can provide a more comprehensive view angle for water level management by fusing multi-source information, and can more accurately predict the water level change trend, thereby optimizing the water resource management strategy and flood control measures.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention discloses a natural gas station intelligent distribution method based on the Internet of Things, and relates to the technical field of natural gas station distribution design. Flow, pressure and valve state data are obtained in real time through an Internet of Things sensor, and a weighted average method and Kalman filtering are used for data fusion and optimization; meanwhile, the relative position relation between the flow meter and the adjusting valve is judged in real time, branch configuration changes are dynamically recognized, self-adaptive branch matching logic is triggered, and therefore control logic is adjusted; meanwhile, the opening degree of the valve is controlled through PID, self-adaptive dead zone compensation is conducted in combination with historical operation data of the valve and the current state, valve response delay is eliminated, the adjusting precision is improved, and flow fluctuation is reduced; in addition, in the feedback adjusting stage, the adjusting result is continuously monitored, the actual flow and the pressure value are compared with the target value, the opening degree of the valve is dynamically adjusted through real-time error calculation, and the output is kept consistent with the target.

The invention discloses a multi-mode instruction interaction method and system based on a virtual character, computing equipment and a storage medium, and the method comprises the steps: obtaining and recognizing user posture data, and obtaining a first operation instruction corresponding to the posture data; voice data of the user are obtained and recognized to obtain intention data of the user, and a corresponding second operation instruction is obtained according to the intention data; the first operation instruction and the second operation instruction are subjected to timestamp alignment, the similarity of the first operation instruction and the second operation instruction in the semantic space is calculated, the first operation instruction and the second operation instruction are fused and dynamically adjusted through a weighted average method, and an instruction with the highest priority is obtained to serve as a final operation instruction to control an object in the virtual scene to move. According to the method, the gesture instruction and the voice instruction are deeply fused, the interaction space between the user and the virtual environment is greatly expanded, the limitation of a traditional single interaction mode is broken through, and more natural, efficient and intelligent immersive experience is provided for the user.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention discloses a GPS interference phased array monitoring direction-finding antenna system for a 1575 frequency band, and relates to the technical field of intelligent antennae, comprising a data integration module for integrating data of satellite ephemeris, carrier attitude and airspace coverage through a weighted average method and generating an environment parameter set, a prediction calibration module for predicting the data of the satellite ephemeris, the carrier attitude and the airspace coverage through a weighted average method and generating an environment parameter set, and a direction-finding antenna module for performing direction-finding on the environment parameter set. The phase drift prediction module is used for inputting an environment parameter set into a bidirectional LSTM network, obtaining a phase drift prediction value, carrying out nonlinear calibration on the phase drift prediction value by adopting a Kalman-particle hybrid filtering algorithm, and synchronously monitoring an IQ signal by utilizing a closed-loop feedback mechanism, and the beam generation module is used for carrying out orthogonal modulation and radio frequency up-conversion processing on the IQ signal, and outputting the processed IQ signal. According to the method, the bidirectional LSTM network is combined with the Kalman-particle hybrid filtering algorithm, so that phase drift caused by factors such as carrier vibration and the like can be accurately predicted and calibrated in real time.

The invention provides an integrated dynamic drug metabolism monitoring and early warning system, and relates to the technical field of drug metabolism monitoring and early warning, the integrated dynamic drug metabolism monitoring and early warning system comprises a data acquisition module, a data processing module, a model construction module, an early warning mechanism module and a user interaction module, the data acquisition module is composed of an optical sensor, a chemical sensor and an electrophysiological sensor, according to the system, the concentration of drugs and related physiological parameters are monitored in real time, different types of biological signals are captured, and the sensitivity and selectivity of the sensors are considered in the design of the data acquisition module so as to ensure the stability and accuracy in different physiological states. Drug concentration and physiological parameters are monitored in real time from multiple dimensions, sensors select and cooperate to form an efficient acquisition network according to performance expression, data quality is optimized in combination with a multi-source data fusion algorithm (such as Kalman filtering and a weighted average method), and a data processing module adopts a statistical analysis algorithm to clean standardized data.

The invention discloses an intelligent model data processing method applied to metal smelting, and relates to the technical field of metal smelting, and the method comprises the following specific steps: S100, collecting metal smelting data in real time, S200, establishing different sub-models, S300, outputting a prediction result based on each sub-model, and carrying out uncertainty evaluation. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process, and through a weighted average method, the optimal decision output is obtained through the intelligent fusion of the output results, and the comprehensive optimal decision output is obtained, and S500, the actual data in the production process is continuously monitored, and the operation parameters of equipment are adjusted. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process. And the model prediction results corresponding to different targets are intelligently fused to obtain a comprehensive optimal decision output, so that the metal smelting process can realize balance and optimization among a plurality of key indexes, and the demand of an enterprise for maximizing comprehensive benefits is met.

The invention discloses a user demand comprehensive analysis method based on online user comment data. The method comprises: calculating a user demand intensity value; calculating a user demand emotion score; calculating a user demand weight value by adopting an IGR-AHP model based on the user demand emotion score; and respectively endowing the user demand intensity value and the user demand weight value with weight coefficients by using a weighted average method, thereby calculating a user comprehensive evaluation index value, and finally carrying out priority ranking on the user demands according to the user comprehensive evaluation index value. The method has the advantages that the LDA topic model is used for text mining, and the problem of how to effectively extract user demand information from a large number of user comments is solved; a user demand emotion score is calculated by fine tuning the BERT model, and the problem that a traditional emotion dictionary analysis method is tedious in process is solved; and finally, combining and considering the strength value and the weight value of the user demand through a weighted average method, and carrying out priority ranking on the user demand.

The invention discloses a multi-dimensional drainage port overflow risk assessment system and assessment method, and belongs to the technical field of drainage port overflow risk assessment systems, and the system integrates ground monitoring and satellite remote sensing multi-source data, and collects related information of a drainage port in an omnibearing manner. The ground monitoring data provides accurate local hydrology, water quality and drainage facility operation state information, the satellite remote sensing data realizes large-range monitoring, the advantages of the ground monitoring data and the satellite remote sensing data are complementary, the data are fused through a weighted average method and a Kalman filtering method, the data accuracy and reliability are improved, and the weighted average method distributes weights according to data characteristics for preliminary fusion, so that the accuracy and reliability of the data are improved. The Kalman filtering method adapts to data dynamic change, such as water level data fusion according to a state equation and an observation equation optimization result, combines the advantages of ground high-precision monitoring and satellite wide-range coverage, accurately reflects water level change, quantifies the overflow risk of a drainage port, and provides effective support for urban water fine supervision.

The invention provides a multi-source data fusion-based high-wind disaster risk assessment method along a high-speed rail, and the method comprises the steps: obtaining the meteorological temperature distribution, the wind speed peak record, the rainfall cumulative value and the wind direction angle change of a meteorological station, carrying out the format conversion and time-space alignment of multi-source data through a preset standardization protocol, and obtaining a multi-source data format; generating a first data set with a unified spatial-temporal scale; acquiring a corresponding wind speed peak record, wind direction angle change and disaster loss degree through a similar case set in the third data set, calculating a first risk probability under the current condition by adopting a weighted average method based on historical cases, and generating a first risk value; updating a line state factor in the fourth data set through a fused probability value in a risk assessment result, adjusting a weight parameter of a wind direction matching coefficient by adopting an online learning method, and generating an optimized fifth data set; according to the continuous dynamic risk assessment sequence, the operation strategy of the high-speed rail line is adjusted in real time, and line safety is ensured.

The invention relates to an intelligent management and control method for a building energy-saving wall and a lattice structure wall applying the method, and solves the problems that although a traditional heat insulation material can reduce heat conduction, the function is single, and heat cannot be actively adjusted. Setting weights for the input data, and calculating the sum of the input data by adopting a weighted average method; according to a mapping relation between an interval range in which the sum of the input data falls and a grade, analyzing and determining the grade corresponding to the thermal working condition; according to the mapping relation between the thermal working condition grade and the overall regulation and control scheme, the overall regulation and control scheme is analyzed and determined, the determined overall regulation and control scheme is executed, and the overall regulation and control scheme comprises a phase change material regulation and control scheme, a lattice structure regulation and control scheme and a heat exchange fluid regulation and control scheme. The method has the following effects that the heat condition of the building is accurately regulated and controlled, the heat performance of the wall is improved, and efficient energy saving and intelligent management are achieved.

The invention discloses a short-term photovoltaic power prediction method based on optimized decomposition and a combined neural network, belongs to the technical field of photovoltaic power generation, and aims to solve the technical problems that in the prior art, prediction precision is greatly influenced by weather changes, modal aliasing and residual error mismatching are likely to occur in signal decomposition, and the adaptability of a single deep learning model is insufficient. According to the method, meteorological factors are screened by adopting Pearson and Kendall combined correlation coefficients, so that the comprehensiveness of feature screening is improved; weather types are divided through SOM and K-means collaborative clustering, and the clustering precision is improved; an improved artificial travel mouse optimization algorithm is utilized to adaptively optimize VMD parameters, and a local optimal solution is avoided; constructing a TCN-BiGRU combined neural network model, and fusing long-term feature extraction and bidirectional time sequence modeling capabilities; a dynamic weight weighted average method is adopted to reconstruct a prediction result, the accuracy is optimized, the precision and efficiency of short-term photovoltaic power prediction are remarkably improved, and the method is suitable for a photovoltaic power generation system under complex weather conditions.

The invention relates to an information pushing method and system based on smart medical big data, and the method comprises the steps: calculating a single-classification project enabling rate for each user classification and project classification combination, and carrying out the calculation of a plurality of single-classification project enabling rates through employing a weighted average method, and obtaining a comprehensive project enabling rate; setting a project active factor according to the value of the single-classification project enabling rate; setting user active factors according to the number of item types involved by a single user; generating an optimization scheme by combining the project active factor and the user active factor according to the single-classification project enabling rate; by calculating the starting rate of the single-classification item, the user and the smart medical item can be more accurately matched, the interest and the demand of the user are considered, the characteristics of the item and the target user group are combined, the characteristics and the preferences of different user groups are more met, and thus the pushing accuracy and the user experience are improved.

The invention discloses an unmanned aerial vehicle multispectral-based water quality monitoring method, which comprises the following steps of: constructing a two-layer learning model with a component learning layer and a meta learning layer, and after acquiring spectral characteristic parameters of a monitored water area, inverting the spectral characteristic parameters of the monitored water area by adopting the two-layer learning model to obtain water quality parameter prediction data; wherein the component learning layer is formed by stacking at least two base learning models of different types, each base learning model can independently carry out water quality parameter prediction based on spectral characteristic parameters to obtain a corresponding water quality parameter prediction result, and the meta learning layer is realized by adopting a meta learning algorithm. Performing weighted stacking on the water quality parameter prediction results obtained by the plurality of base learning models by using a weighted average method to obtain a final water quality parameter prediction value; the method has the advantage of high detection precision.

The invention discloses a ten-thousand-square-meter muddy deep foundation pit supporting structure deformation prediction method which comprises the following steps: establishing a soil-supporting interaction rheological model, simulating stress relaxation and creep effects of muddy soil in combination with a Burgers rheological model, and realizing dynamic evolution prediction of a supporting structure deformation field and an internal force field. According to the method, the particle filter algorithm is adopted to carry out denoising processing on real-time monitoring data, model parameters are updated through the Bayesian method, and the prediction precision is improved. Meanwhile, nonlinear mapping of the deformation rate and the internal force increment is established by using a random forest algorithm, and instability risk probability distribution is generated. According to the method, multi-stage collapse early warning thresholds are set, monitoring data and prediction results are fused by adopting a weighted average method, and abnormal points of a high-risk area are identified. For a high-risk area, a genetic algorithm is applied to optimize the rigidity and prestress distribution of a supporting system, effective control over deformation and internal force of a supporting structure is achieved by dynamically adjusting construction parameters, and construction safety of the deep foundation pit is ensured.

The invention discloses a high-speed rail vehicle-mounted equipment fault prediction and health management method, and relates to the technical field of high-speed rail vehicle-mounted equipment fault prediction, and the method comprises the steps: monitoring high-speed rail vehicle-mounted equipment through employing a high-precision sensor network, and obtaining an original monitoring data set; cleaning the original monitoring data set by adopting a multi-source data integration and preprocessing method to obtain a high-quality data set, and extracting key features based on the high-quality data set to obtain a feature data set; constructing a fault prediction model based on a deep learning method and the feature data set, inputting the high-quality data set to the fault prediction model, and outputting a fault early warning result; based on the fault early warning result, a decision support method is adopted to evaluate the equipment state, and a maintenance strategy suggestion is obtained; obtaining a comprehensive analysis report based on the maintenance strategy suggestion and the equipment evaluation state; environmental data and historical maintenance record data from a ground monitoring system are integrated, and the data are fused by using a weighted average method.

The invention relates to the technical field of viscosity measurement, and discloses a verification system for a viscometer, comprising: a dynamic temperature compensation module for collecting temperature data inside and outside the viscometer in real time; the embedded controller is responsible for receiving the temperature data, executing a temperature compensation process and outputting a temperature compensation coefficient and an initial viscosity value; the multi-modal data fusion calibration module is used for receiving a temperature compensation coefficient provided by the multi-modal data fusion calibration module and data from a pressure sensor, a shear rate sensor and a vibration sensor, and carrying out joint modeling on the data by utilizing a weighted average method; the invention also discloses a verification method for the viscometer, which comprises the following steps: acquiring temperature data inside and outside the viscometer through the temperature sensor, and performing temperature compensation through the embedded controller. According to the invention, the multi-modal data fusion calibration module is introduced, and data from a plurality of sensors are integrated through a weighted average method, so that the optimized viscosity value is more comprehensive and reliable.

The invention belongs to the technical field of point cloud data reconstruction, and particularly relates to a robust three-dimensional reconstruction method based on weighted local curved surface approximation. The method comprises the following steps: firstly, preprocessing input three-dimensional point cloud data, constructing a neighborhood relationship, and fitting a local curved surface in a local neighborhood of each point through a least square method; secondly, calculating the influence weight based on the neighborhood information of the point, and calculating the position of a final curved surface point by adopting a weighted average method so as to optimize the geometric accuracy of a local curved surface; then, screening and removing abnormal points by using an abnormal weight elimination strategy based on a mean square error, reducing noise interference, and improving stability and robustness of curved surface reconstruction; and finally, through weighted average, the precision of overall curved surface fitting is improved, so that the reconstructed curved surface can more accurately fit the original point cloud data, and the reconstruction precision of the point cloud data is effectively improved.

The invention relates to the technical field of the Internet of Things, in particular to a lightweight distributed model aggregation method applied to a low-power-consumption chip of the Internet of Things, and is suitable for an Internet of Things system composed of a cloud server and a plurality of edge nodes. Each edge node learns an optimal strategy from offline data, and meanwhile, a complex teacher model is compressed into a lightweight student model through a knowledge distillation technology, so that the local calculation burden is reduced. And each edge node uploads quantized and sparse model parameters to the cloud server, and the cloud server performs federal aggregation by adopting a weighted average method to generate a global model and feeds back the global model to each terminal on the premise of ensuring data security. In order to adapt to the practical application of a low-power-consumption chip, a dynamic voltage frequency regulation technology is introduced in the hardware level, and the energy consumption and the communication overhead are effectively reduced. According to the method, the model training process of the edge nodes of the Internet of Things is optimized, and federated learning of privacy protection, efficient communication and cooperative self-adaption is realized.

The invention discloses a ship power grid transient electromagnetic interference grading evaluation method and system, relates to the technical field of ship power grid safety evaluation, realizes scientific grading evaluation of ship power grid transient peak interference, and provides a reliable basis for stable operation and equipment protection of a ship power grid. Comprising the following steps: measuring a transient peak sensitive threshold value of equipment by adopting a peak signal transduction sensitivity test; performing classification and importance coefficient assignment on the equipment according to the importance degree of the equipment; and calculating a weighted average sensitive threshold value of load equipment at the rear end of the distribution box by adopting a weighted average method. At the output end of a power distribution box of a ship power grid cabin, a transient peak conducted emission detection device is used for monitoring and collecting power grid transient peak conducted emission data in real time, and a power grid transient peak interference measured value is recognized from the power grid transient peak conducted emission data. Comparing the power grid transient peak interference measured value with the weighted average sensitive threshold value, and calculating a difference value between the two values; and according to a difference value result, carrying out ship power grid transient peak interference grading evaluation.

The invention belongs to the field of time synchronization and data processing, and discloses a satellite and Rowland timing data fusion method and related device.Firstly, original data are intercepted and subjected to mean value removal to eliminate baseline offset, and a frequency domain matrix is reconstructed by combining frequency domain conversion with singular value decomposition; according to the method, periodic term interference signals in a frequency domain are accurately recognized and filtered out through a dynamic threshold strategy, then effective components are reserved through time domain conversion, finally, a noise covariance matrix and observation model parameters are dynamically adjusted based on an adaptive Kalman filtering algorithm, and dynamic weight fusion of double-source data is achieved. By the adoption of the method, the defects that a traditional weighted average method is insensitive in fixed weight, Kalman filtering parameters are rigid and global interference suppression of wavelet transformation is insufficient are effectively overcome, the suppression capacity for non-stationary noise and periodic interference is remarkably improved, fused data have the high precision of a satellite system and the anti-interference characteristic of a Rowland system, and the method is suitable for being applied to the field of satellite communication. And finally, high-reliability and high-stability time synchronization performance is realized.

The invention relates to the field of image processing, in particular to a raw material quality detection method for a foreign fiber machine, and the method comprises the steps: obtaining raw material surface images at a preset angle and a preset wave band, respectively forming an image set A and an image set B, and carrying out the preprocessing of the two sets; calculating a first abnormal degree of each pixel point by using the reflection characteristic of the image set A, obtaining a fluctuation degree through the gradient value change of the image set B, and calculating a second abnormal degree; fusing the two abnormal degrees by adopting a weighted average method to obtain a comprehensive abnormal index, comparing the comprehensive abnormal index with an abnormal threshold value, determining abnormal pixel points, constructing an abnormal region, and evaluating the quality of the raw material surface image according to the characteristic parameters of the abnormal region. According to the method, the multi-angle reflection characteristic and the multi-band gradient value change are combined, the included angle weight and the band weight are introduced, the transparent or semitransparent foreign fibers are effectively recognized, and the detection accuracy and adaptability are improved.

The invention discloses a data security compliance evaluation and identification method and system, and relates to the technical field of data security compliance evaluation and identification, and the method comprises the steps: collecting regulation text data through an API interface, and carrying out the preprocessing of the regulation text data through a natural language processing library NLTK, and obtaining formatted regulation text data; mapping each word in the formatted law and regulation text data into a fixed-length vector by adopting a pre-calculation word embedding method, and generating a digital vector; marking important terms in the digital vector by adopting a named entity recognition NER method to obtain a key concept text; establishing an evaluation model based on a machine learning algorithm, inputting the digital vector into the evaluation model, calculating and outputting an evaluation value by adopting a weighted average method, setting an evaluation threshold value, comparing the evaluation threshold value with the evaluation value, judging whether the digital vector is compliant or not, and obtaining an evaluation result; and constructing a recognition model based on an anomaly detection algorithm, and inputting the key concept text into the recognition model.

The invention relates to the technical field of environment monitoring, and discloses an intelligent environment monitoring and GIS (Geographic Information System) data fusion system, which comprises the following steps: a data fusion algorithm module, performing deep fusion on preprocessed environment data and GIS information through space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method to obtain a GIS data fusion module; according to the intelligent environment monitoring and GIS data fusion system, space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method mathematical method are adopted, and the preprocessed environment data and GIS information are deeply fused. The environmental data set with spatial distribution characteristics is generated, a scientific basis is provided for environmental condition evaluation, trend prediction and anomaly detection, the environmental condition and the association between the environmental condition and the geographic position can be more comprehensively understood through data fusion, and the efficiency and accuracy of environmental management are improved.

The invention relates to the technical field of hybrid energy storage systems, and particularly discloses an intelligent charge and discharge power distribution method for a hybrid energy storage system, which comprises the following steps of: firstly, acquiring state data of an energy storage unit, and extracting key indexes including charge state, temperature, cycle index and response speed from the state data; then, based on the key indexes, distributing weights for each index, calculating to obtain a score of each key index, further, based on the score of each key index, adopting a weighted average method to obtain a comprehensive score of the energy storage unit, dividing the energy storage unit into an excellent energy storage unit and a common energy storage unit according to the comprehensive score, and finally, obtaining a final energy storage unit. Based on the total power requirement of the hybrid energy storage system, power distribution of the excellent energy storage unit and the common energy storage unit is dynamically adjusted, the overall efficiency and reliability of the energy storage system can be improved, and meanwhile the service life of the energy storage unit is prolonged.

The invention provides a mechanical arm imitation learning method and device based on DMP, and belongs to the technical field of robots. The method comprises the steps that a trajectory is fitted through a motion primitive method; according to the teaching or simulation track, the weight of the motion primitive is obtained through a least square method; a new trajectory regeneration method is provided, and new trajectories with the same geometric features are generated by setting a new starting point, a new ending point or a new scaling coefficient; a new track passing point strategy is provided, so that the generated new track can pass through a specific key point; applying a Gaussian process to generate a high-order smooth trajectory passing through a specific point; generating a high-order smooth curve meeting the geometric characteristics by using a weighted average method; substituting the generated new trajectory into the new DMP model, and establishing a dynamic system model; and according to the dynamic system model, the mechanical arm joints are driven through the joint speed track, and speed control is achieved. According to the method, a large amount of similar industrial programming work can be reduced, and the flexibility and efficiency of trajectory planning and control are improved.

The invention relates to the technical field of data integration and analysis, and discloses a digital twinning-oriented multi-dimensional data integration and analysis system, which comprises a data collection module, a data integration module, a data analysis module and a result output unit, in the digital twinning-oriented multi-dimensional data integration and analysis system, the same semantic elements in different data sources are accurately identified through a comprehensive algorithm in pattern matching, and the accuracy is improved; for data fusion, a weighted average method and a voting method are adopted for numeric data and classified data, reasonable fusion is achieved, and it is guaranteed that the result is reliable and effective; feature extraction can fully mine feature information from structured and unstructured data; according to attribute correlation analysis, the correlation relation between matching attributes is accurately grasped by means of a Pearson's correlation coefficient formula, and data understanding is deepened; results are output and displayed clearly, integration and analysis results can be presented visually, and subsequent decision-making and other operations are facilitated; the multi-dimensional data utilization efficiency and the analysis value in the digital twinning scene are effectively improved, and powerful data support is provided for development of related fields.