73197results about "Complex mathematical operations" patented technology

The present invention relates to a low-voltage power distribution network hybrid energy storage optimization configuration strategy considering an incentive-based demand response. The method comprises the following steps: step 1: an optical storage system considering an incentive-based demand side response; step 2: a hybrid energy storage system configuration strategy considering the incentive-based demand side response; step 3: a hybrid energy storage system optimization configuration model considering the incentive-based demand side response; and step 4: exemplary application analysis. The present invention has the advantages of fully using the complementation characteristic of resources, maximally exerting the adjustment capability of flexible resources, having a good effect of stabilizing the fluctuation of a net load curve, and being fast in solving speed and high in calculation precision.

The invention discloses an experimental calibration method based on multi-sensor signal fusion processing, particularly relates to the field of data analysis, and comprises the steps of a stepped confrontation disturbance protocol, data stream processing and real-time analysis, and multi-stage logic judgment and calibration execution. A stepped confrontation disturbance protocol is created for the first time, potential errors of the sensor are excited in real time and corrected in a closed loop mode under the condition that system operation is not interrupted, and a traditional shutdown calibration mode is thoroughly replaced; a multi-stage disturbance defense system is constructed, and composite interferences such as spectrum aliasing, overload distortion and signal-to-noise ratio attenuation caused by electromechanical thermal multi-physical field strong coupling are effectively suppressed; laser space calibration and dynamic rigidity modulation technologies are fused, and space drift errors caused by deformation of the mounting base and mechanical loosening are accurately diagnosed and compensated. Finally, long-term precision stability and data fusion reliability of the multi-source sensor under complex working conditions are achieved.

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 relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

A method and device for evaluating ecological cumulative effects of surface mining areas are provided. The method includes: constructing a surface mining areas eco-environmental evaluation index (SMAEEI) suitable for semi-arid grasslands; constructing a first eco-environmental quality condition adjustment coefficient R(Si,j,th) based on the actual land cover classification result and the SMAEEI; obtaining the actual ecosystem service value ESV per unit area of the area to be evaluated in several years through the unit area ecosystem service value coefficient VCif of the area to be evaluated and the first eco-environmental quality condition adjustment coefficient R(Si,j,th); based on the time-series trajectory integral value of the actual ecosystem service value ESV per unit area and the ideal ecosystem service value ESV′ per unit area undisturbed by human beings, performing residual analysis to obtain the ecosystem service value accumulation induced by anthropogenic factors (ESVA-AF).

The invention provides an environment monitoring data anomaly detection method, a medium and a system, and belongs to the technical field of electrical digital data process.The method comprises the steps that firstly, an environment monitoring data original matrix is constructed, time sequence segmentation is conducted, and a rule matrix and an anomaly matrix are obtained through matrix decomposition; calculating a historical mean value, a historical standard deviation, a spatial feature and a time feature of the environmental parameters, designing an anomaly detection optimization equation set based on the features, including an environmental parameter weight equation, an anomaly threshold equation and a comprehensive anomaly degree equation, and calculating an anomaly detection result by calculating an environmental parameter weight vector, a dynamic anomaly threshold matrix and a comprehensive anomaly degree index. Finally, dynamic optimization of anomaly detection is achieved through the gain vector and the feedback vector, anomaly detection of real-time environment monitoring data is completed, and the technical problem that in the prior art, an environment monitoring data anomaly detection method is difficult to give consideration to dynamic relevance of multi-dimensional environment parameters and the accuracy of anomaly detection at the same time is solved.

A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention provides an electromagnetic interference source identification and positioning method based on multi-band EMI. The method comprises the following steps: carrying out preprocessing and standardization processing on acquired electromagnetic characteristic data, real-time environment data of a sensor network and operation state data of electronic equipment; based on the predicted electromagnetic environment perception result, extracting electromagnetic environment feature data, performing noise reduction and preprocessing on the electromagnetic environment feature data of different dimensions, performing weight distribution and optimal combination, and performing iterative calculation to obtain an interference source position coordinate; the optimized sensor array layout and parameter setting are applied to the test process of the electronic equipment, performance parameters of the electronic equipment in different electromagnetic environments are obtained, the performance parameters comprise electromagnetic compatibility and anti-interference capacity, and a test result is compared with a preset standard value and calibrated.

The invention discloses a substation equipment state monitoring and intelligent fault early warning method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed multi-source state data set; s2, generating a high-dimensional equipment state feature matrix; s3, a fault sensitive deep belief network model is adopted to form a preliminary fault state recognition result; s4, obtaining an optimized sensitive depth belief network model; s5, performing online analysis on the multi-source state data acquired in real time by using the optimized sensitive deep belief network model, generating a real-time fault prediction result of the equipment state, and classifying and grading fault risks; and S6, according to a real-time fault prediction result, triggering a remote fault early warning mechanism, and sending fault early warning information including a fault risk level, an early warning signal and an emergency processing suggestion to a substation operation and maintenance center. According to the invention, intelligent alarm linkage and hierarchical control of the scheduling system are effectively supported.

Disclosed in the present invention is a power distribution network point cloud model construction system based on three-dimensional visualization, the system comprising: a data collection module, which collects model data of a power distribution network by means of receiving a signal reflected by a LiDAR scanner; a data denoising module, which performs denoising processing on the collected model data, so as to eliminate a data error caused by noise; a point cloud reconstruction module, which simplifies denoised point cloud data, screening important features in the power distribution network, and constructing a point cloud model using the screened data; a visualization module, which uses a visualization technique to present the constructed model, so as to realize an interactive visualization operation; and an operation state evaluation module, which evaluates the safety performance of the power distribution network on the basis of fault data and a historical operation record of the power distribution network. In this way, the operation condition of the power distribution network can be visually known by means of a three-dimensional visualization interface, and thus the operation and maintenance efficiency is improved, and accurate data support is provided for the management and operation and maintenance of the power distribution network.

The invention provides a new energy battery internal microcrack detection method, which comprises the following steps: acquiring a battery internal three-dimensional structure image by adopting a high-resolution X-ray tomography technology, extracting a pole piece area through an image segmentation algorithm, and carrying out bending deformation simulation on a pole piece to obtain a pole piece internal stress distribution cloud picture; and combining the retrained health state evaluation and residual service life prediction model with battery electrochemical performance characterization data, coupling microcrack evolution and battery performance degradation, predicting a future crack propagation trend and a corresponding performance degradation condition of the battery, and guiding quality control in a battery design and production process.

The invention relates to the technical field of foundation pit detection, and discloses a foundation pit deformation real-time monitoring method, device and system based on a BIM technology. The method comprises the steps of sensor layout and data acquisition, data transmission and preprocessing, nonlinear dynamic model construction, data fusion and optimization, foundation pit deformation prediction, BIM visual presentation and data display, intelligent support optimization and construction scheme adjustment. The device comprises a sensor assembly, a data acquisition interface, a 5G communication module, a central processing unit, a fusion and prediction unit, a man-machine interaction interface and an execution mechanism interface. The system comprises a data acquisition module, a data transmission module, a data processing module, a deformation modeling module, a data fusion module, a deformation prediction module, a visualization module and an intelligent regulation and control module. By adopting the multi-type sensor fusion and data assimilation technology, the high-precision and low-error foundation pit deformation monitoring effect is achieved, the monitoring data is more stable, and the monitoring precision is ensured to meet the engineering safety management requirement.

A method and a system for constructing a soil water movement model based on the physical information neural networks are provided. The soil water movement model is constructed by fusing the physical information neural network and a control equation of the soil water movement model, and the model adopts automatic differentiation to replace difference operation of grid scale, so that the calculation error caused by equation discretization in the solving process of numerical differentiation is avoided, and the calculation accuracy is improved; and the automatic differentiation mode is mainly carried out aiming at the output of a neural network, so that the numerical value of gradient calculation is more accurate.

The invention relates to the technical field of digital twinning, in particular to a pile foundation construction whole process digital twinning system which comprises a twinning configuration module, a dynamic mapping module, a coupling analysis module and a twinning synchronization module. According to the method, a time axis driving model sequence is constructed based on space-time interpolation, burial depth coordinates, pouring time and segment numbers are dynamically bound, three-dimensional evolution mapping of a pile body model is achieved, axis coordinate deviation is combined with perpendicularity threshold extraction, virtual model coordinates are corrected in real time through point cloud registration, and a pump pressure value and settlement are fused to generate a pile sinking rate index. A bidirectional coupling relation between parameters and stratum response is established in combination with stratum counter-force and injection rate, the thickness of the model is adjusted according to a settlement rate difference value, the similarity of the injection rate and a counter-force curve is matched, a model parameter reverse updating mechanism is formed, and real-time intervention of construction abnormity, dynamic optimization of parameter coupling and model iteration correction are achieved. And the process controllability and the quality tracing precision are improved.

The invention discloses a fluid mechanics equation solving method based on a physical information neural network, and relates to the technical field of fluid mechanics. The problems that in the prior art, a large amount of training data is needed, physical information is not embedded, prediction does not conform to physical laws, generalization is difficult, and sample generation consumes resources are solved. The fluid mechanics equation is solved through the physical information neural network, physical consistency and prediction precision of model training are ensured, mathematical descriptions of fluid motion and heat transmission are determined, accurate input is provided for the neural network, definition of a loss function is combined with a basic physical law of fluid mechanics, and the prediction precision is improved. The reliability of model output is guaranteed, the training and optimization process effectively guides the learning of the neural network and improves the solving efficiency, the numerical verification and prediction step combines the prediction capability of high-precision numerical simulation and the neural network, the solving precision is guaranteed, the prediction efficiency in an unsampled area is improved, and the prediction efficiency of the neural network is improved. And an efficient and accurate tool is provided for fluid mechanics research.

The invention discloses a power system Internet of Things intelligent detection method, and relates to the technical field of power detection, and the system comprises the following components: S1, data collection and preprocessing, S2, big data analysis and feature extraction, S3, equipment life prediction model construction and training, S4, fault prediction and evaluation, S5, predictive maintenance strategy making, and S6, adaptive adjustment. According to the method, the equipment life prediction model of deep neural network self-learning modeling is adopted, the quantum optimization algorithm is used for optimization, accurate prediction of the residual life and the fault occurrence probability of the power equipment is achieved, and meanwhile, the power equipment state comprehensive evaluation model based on the combination of fuzzy logic and the evidence theory is combined, so that the power equipment state comprehensive evaluation efficiency is improved. The equipment health state grade can be comprehensively evaluated, a scientific basis is provided for formulating a predictive maintenance strategy, a maintenance plan can be reasonably arranged in advance, the maintenance cost is reduced, the service life of equipment is prolonged, and economic benefits and social benefits of a power system are improved.

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention relates to the technical field of detection, and discloses a polyurethane material performance detection method, which comprises the following steps: clamping a material to be detected on a loading platform, cooperatively controlling and simulating the stress effect of a target application scene, and capturing a time sequence image of the microstructure of the surface of the material by adopting a visual unit; synchronously acquiring internal strain distribution data through a distributed strain sensing array, acquiring a dynamic stress response spectrum in combination with a piezoelectric mechanical sensor, and generating a multi-modal deformation characteristic data set; performing crack density and texture orientation analysis on the time sequence image, extracting a crack growth rate and a direction consistency coefficient, and marking a strain concentration area; calculating an apparent elastic modulus attenuation rate based on the dynamic stress response spectrum, establishing a material performance degradation model, generating a dynamic flexible performance index through a weighted fusion algorithm, and performing graded evaluation on the material; material detection is upgraded from a single mechanical test to a multi-physics coupling intelligent evaluation system, and accurate and reliable detection support is provided for multiple application scenes.

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

The present invention proposes a method for intelligent monitoring and management of enterprise carbon emissions based on big data analysis, which relates to the field of carbon emission supervision technology, including: collecting enterprise carbon emission related data; preprocessing the collected data to form a standardized data set; performing multi-dimensional analysis on the standardized data set to calculate the carbon emission assessment coefficient; based on the carbon emission assessment coefficient, using a mathematical model to calculate the enterprise carbon emission index, and quantitatively assessing the enterprise carbon emission status; based on the calculated carbon emission index, combined with industry standards and historical data, assessing the current operation of the enterprise, identifying abnormal emissions and optimization space; based on the assessment results, combined with the expert knowledge base, forming energy-saving and emission reduction decision-making suggestions and optimization plans. The present invention can achieve accurate quantitative assessment of the enterprise's carbon emission status, provide scientific energy-saving and emission reduction decision-making suggestions and optimization plans for enterprises, thereby effectively improving the enterprise's carbon emission management level.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

Key-value (KV) caching accelerates inference in large language models (LLMs) by allowing the attention operation to scale linearly rather than quadratically with the total sequence length. Due to large context lengths in modern LLMs, KV cache size can exceed the model size, which can negatively impact throughput. To address this issue, KVCrush, which stands for KEY-VALUE CACHE SIZE REDUCTION USING SIMILARITY IN HEAD-BEHAVIOR, is implemented. KVCrush involves using binary vectors to represent tokens, where the vector indicates which attention heads attend to the token and which attention heads disregard the token. The binary vectors are used in a hardware-efficient, low-overhead process to produce representatives for unimportant tokens to be pruned, without having to implement k-means clustering techniques.

The invention relates to a method and system which determine certainty, e.g. an aleatoric certainty score, as well as an uncertainty score, e.g. an epistemic uncertainty, based on input data. The input data includes static and dynamic data. A trained behavioral model generates trajectory predictions and the certainty and uncertainty score. A vehicle-based function is controlled based in the certainty score and the uncertainty score, e.g. increase following distance or hand over to a human driver.

The invention relates to the technical field of compression molding of carbon fiber prepregs, in particular to a partitioned pressurization control method and system for compression molding of the carbon fiber prepregs and a molding process. S2, initial pressure calculation; s3, gradient pressurization control; s4, dynamic pressure adjustment; and S5, a global pressure maintaining stage: when the resin curing degree alpha reaches 0.6-0.8, switching to a full-screen pressure maintaining mode, and maintaining the pressure of each region to be 90%-95% of the current maximum pressure value until curing is completed. The invention provides a zoning pressurization control method and system for compression molding of carbon fiber prepreg and a molding process, and realizes the compression molding method and system with accurate zoning pressure regulation and control and directional discharge of volatile components. And by integrating intelligent sensing, multi-channel hydraulic control and a data driving optimization algorithm, the limitation of a traditional process is broken through, and a technical support is provided for efficient forming of the high-performance carbon fiber composite material.

The invention relates to the field of welding process control, and discloses a pipeline all-position automatic TIG (Tungsten Inert Gas) welding method which comprises the following steps: collecting pipeline geometric parameters, welding position angles and material attribute data in real time through multi-source data fusion; constructing a Gaussian process regression dynamic response model, coupling a nonlinear mapping relationship among the process parameters, the molten pool morphology and the corrosion tendency index, and dynamically adjusting the weight of the model based on the welding position angle; a hierarchical strategy of Bayesian optimization and model prediction control is adopted to generate a parameter solution set meeting the fusion depth constraint and the corrosion threshold value, and the molten pool oscillation frequency is used as feedback to correct the current in real time; the heat accumulation evolution trend is predicted through a hidden Markov chain, and parameter closed-loop migration and trajectory compensation are achieved in combination with molten pool flow field coupling correction. The problems of uneven forming quality and corrosion risk caused by space displacement, dissimilar metal interface effect and dynamic disturbance in all-position welding are solved.

A knowledge distillation method and system based on embedded feature regularization. The method comprises: collecting labeled image data related to a recognition task; calculating a feature center of a teacher model in each category on a whole training set; projecting in the direction of a category center an embedded feature of a student model, rotating to the direction of the category center an embedded feature of the teacher model, and constructing a feature regularization loss by means of regularizing a projection feature of the student model and a rotation feature of the teacher model; increasing the norm of the feature of the student model, and constraining the direction of the feature of the student model to be consistent with the direction of the category center; inserting the feature regularization loss into an existing knowledge distillation framework, and training the student model; and deploying the trained student model to a terminal device, predicting a logits vector on the basis of new data received by the terminal device, and thus completing the related recognition task. In the present invention, by means of increasing the norm of a feature of a student and constraining the direction of the feature to be consistent with the direction of a category center, the knowledge distillation performance is better.