195 results about "Correlation index" patented technology

The invention belongs to the technical field of wind-solar power generation power monitoring, and particularly relates to a new energy wind-solar power data monitoring system and method. According to the method, power fluctuation characteristics and equipment state correlation indexes are extracted through abnormal value detection and time sequence analysis, so that the precision and efficiency of data processing are improved, and in the aspect of prediction, dynamic comparison between a historical operation data set and the current power fluctuation characteristics is utilized, and environmental parameter change trends are combined. A power prediction curve and a confidence interval are generated, so that the system can recognize potential abnormal operation in advance, decision support time is provided for operation and maintenance personnel, in addition, the system has corresponding fault diagnosis and optimization capabilities, when the prediction deviation degree exceeds a preset threshold value, an early warning mechanism is automatically triggered, a fault source is positioned through a fault diagnosis process, and the fault diagnosis efficiency is improved. And finally, according to a fault diagnosis result, a power prediction curve is corrected and optimized to form a closed-loop optimization mechanism, and the prediction precision and the operation efficiency of the system are continuously improved.

The present invention is applicable to the technical field of carbon emission monitoring. Provided are a carbon emission prediction method and a related apparatus. The method comprises: acquiring total carbon emission and total power consumption of each industry; calculating a correlation index of the total carbon emission and the total power consumption, so as to screen a target monitored industry; acquiring carbon emission and power consumption of the target monitored industry; on the basis of the carbon emission and the power consumption, calculating an electricity-to-carbon emission transfer coefficient based on the target monitored industry; on the basis of the target monitored industry, matching a target monitored enterprise, and on the basis of randomness, periodicity and climate factors, constructing a power consumption prediction model of the target monitored enterprise; acquiring actual power consumption of the target monitored enterprise and inputting same into the power consumption prediction model for training; matching an outputted predicted power consumption value with the electricity-to-carbon emission transfer coefficient; and generating a predicted carbon emission value. On the basis of the linear relationship between carbon emission and power consumption, the present invention uses power consumption prediction to estimate predicted carbon emission values, thus effectively improving the efficiency and accuracy of carbon emission prediction.

The invention relates to the technical field of geological data processing, in particular to a prospecting method and system based on geological text data, and the method comprises the following steps: obtaining exploration drilling data, extracting lithologic description, mineral composition and fracture filling conditions of a rock core, calculating the correlation index of drilling depth and mineralization thickness, screening the change trend of ore grade, and obtaining the geological text data. And obtaining mining area borehole lithology distribution data. According to the method, by deeply analyzing drilling data and lithologic characteristics, accurately recognizing a mineralized fluid accumulation area and improving the prediction precision of an ore body occurrence position, boundary control of a mineralized zone is optimized through measurement and calculation of spatial homogeneity, the extension precision of the mineralized zone is enhanced, scientific adjustment of a prospecting range is ensured through detailed analysis of altered mineral characteristics, and the method is suitable for large-scale popularization and application. The reasonability of demarcation of the prospecting target area is improved, multi-dimensional data and parameter analysis are integrated, a complete prospecting logic chain is constructed, and mineral product prediction precision and resource exploration efficiency are remarkably improved.

The invention relates to the field of data processing, in particular to a server fault diagnosis method and system for a server group, and the method comprises the steps: obtaining and preprocessing server operation state data; constructing a running state isolated tree by using an isolated forest algorithm, analyzing over-limit and low-limit conditions of data of each dimension, determining a neighbor monitoring sequence and constructing a threshold offset index; based on the index, analyzing association characteristics among the dimension data to construct a fault association index; further analyzing data anomaly differences between leaf nodes and child nodes in the isolated tree, and constructing an intra-layer isolated index; and calculating an abnormal isolated weight and a significant abnormal weight, improving an isolated forest algorithm, and realizing accurate diagnosis of server faults. According to the method, the significant abnormal weight is introduced to improve the isolated forest algorithm, and the threshold offset index and the fault correlation index are constructed, so that the server fault diagnosis precision is significantly improved, false alarm and missing alarm are reduced, operation and maintenance personnel are helped to accurately position faults, and stable operation of the server is guaranteed.

The invention relates to the technical field of anomaly detection, in particular to a pantograph arcing anomaly detection and early warning method based on fusion of ultraviolet light and infrared imaging, and the method comprises the steps: collecting an ultraviolet spectrum signal of a pantograph region in real time, and extracting arcing feature data; acquiring infrared temperature distribution images of the pantograph and the overhead line system, and extracting temperature abnormal region data; timestamp synchronization and space coordinate mapping are carried out on the arcing characteristic data and the temperature anomaly region data, and a fusion data set with time-space alignment is generated; on the basis of the fused data set, calculating the space-time similarity between the arcing feature data and the temperature anomaly region data, and outputting an arcing-temperature correlation index; constructing a Gaussian mixture model, and dynamically judging an abnormal level; and when the abnormal grade exceeds a preset threshold value, a grading early warning signal is triggered, and a fault positioning report including an arcing position and a temperature abnormal range is generated. The method has high engineering practicability, and has important significance for guaranteeing safe and stable operation of a rail transit power supply system.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention relates to the technical field of cloth rolling machine control, in particular to a cloth rolling machine control method and system for automobile interior fabric production, and the method comprises the steps that correlation indexes are determined by analyzing the correlation between the average level of all rotating speed data in each rotating speed data window in each time period and a fluctuation abnormal value; respectively analyzing the difference between the rotating speed data and the correlation index and the corresponding fitting value, and constructing a first difference degree and a second difference degree to determine an abnormal coefficient at the current moment; and determining the tension complexity by analyzing the complexity of all tension data from the beginning of the fabric winding process to the current moment, and determining a tension abnormal value by combining the abnormal coefficient so as to control the rotating speed of a cloth roller in the fabric winding process at the current moment. Interference of mechanical abrasion of the cloth rolling machine in the fabric rolling process on rotation speed adjustment is reduced, and the tension adjusting precision in the fabric rolling process is improved.

The invention discloses a chemical plant safety early warning and multi-dimensional correlation analysis method and device and a storage medium. By collecting equipment operation parameters, personnel operation and environment data, a reference model library is constructed to realize dynamic threshold early warning; after the early warning is triggered, calculating a first correlation index, a second correlation index and a third correlation index respectively by adopting time sequence similarity analysis, historical statistical analysis and process knowledge graph reasoning; and positioning the core inducement through multi-index weighted fusion. The problems that a traditional method is poor in early warning adaptability, weak in system collaboration and insufficient in tracing depth are solved, and accurate early warning, deep tracing and closed-loop management are achieved.

The invention provides a trade whole industry chain data intelligent matching and resource docking method, and relates to the technical field of international trade data analysis and geographic information systems.The method comprises the steps that customs declaration data, logistics tracking data, enterprise supply chain data and market public opinion data are collected in real time through a standardized interface; constructing a multi-source heterogeneous data fusion system of block chain evidence storage; dynamically modeling a whole industry chain relation network based on the knowledge graph, and quantifying the supply chain dependency, the trade flow direction and the policy influence weight; calculating an industrial chain correlation index ICRI and a resource matching priority coefficient RMPC, optimizing a logistics path in combination with a geographic space analysis algorithm, and avoiding a policy restriction area in real time; and rendering a matching result through a three-dimensional visualization platform, and iteratively updating model parameters based on user interaction feedback. According to the method, the limitation of a traditional data island is broken through, dynamic quantitative analysis and accurate resource matching are realized, and the trade data integration efficiency is remarkably improved through intelligent path planning and interactive decision support.

The invention relates to the field of image processing, in particular to an oilstone static pressure forming quality detection method based on image processing, and the method comprises the steps: obtaining an infrared image set of an oilstone sintering and cooling stage, carrying out the superpixel segmentation to obtain a superpixel region of each frame of image, matching the superpixel regions of adjacent frames, and building a corresponding relation, and calculating a temperature anomaly characteristic value of each region. And analyzing the temperature change trend of each region, calculating a correlation coefficient, and introducing distance and temperature anomaly weight to carry out weighted fusion to obtain a comprehensive correlation index. And in combination with the temperature anomaly characteristic value and the comprehensive correlation index, evaluating the possibility value of occurrence of pore anomaly in each superpixel region, and judging whether pore distribution anomaly exists in the oilstone or not, thereby completing quality detection. According to the method, through superpixel segmentation and matching, in combination with temperature anomaly characteristics and comprehensive correlation analysis, precise detection of oilstone internal pore anomaly is realized, and the reliability of static pressure forming quality detection is improved.

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention belongs to the technical field of fault detection, and particularly relates to a wire drawing machine fault detection method based on the Internet of Things. Historical vibration and temperature and real-time data of operation of the wire drawing machine are collected through an Internet of Things sensor and are transmitted to a server through an Internet of Things protocol. After data are subjected to preprocessing denoising such as variational mode decomposition and a FastICA algorithm, a vibration anomaly judgment index (including effective anomaly time period judgment, vibration mode matching degree calculation and index synthesis) and a temperature anomaly judgment index (including correlation index calculation, heat conduction model establishment and index synthesis) are calculated, and then the two indexes are fused to obtain an anomaly index. A prediction model is constructed by using a neural network algorithm, after optimization of a genetic algorithm, a fault probability is output in combination with real-time data, and finally, a probability value and an abnormal index are normalized and averaged to obtain a fault score, so that fault detection and evaluation are realized. The method improves the fault detection accuracy and the equipment operation reliability, and reduces the maintenance cost.

The invention provides a mechanical and electrical product cross-domain semantic matching method and system, and the method comprises the steps: obtaining a sample set, the sample set comprises sub-samples related to mechanical and electrical products in a plurality of domains, and the sub-samples comprise labeled samples and unlabeled samples, the annotation sample comprises a query statement, text data and a correlation index between the query statement and the text data; performing incremental pre-training on the pre-training model according to the unlabeled samples to obtain an intermediate model; performing data enhancement on the labeled sample by utilizing a large language model to obtain an enhanced sample; and performing supervision fine tuning on the intermediate model according to the labeled sample and the enhanced sample to obtain a cross-domain semantic matching model. According to the method, the problem of poor generalization of a mechanical and electrical product cross-domain semantic matching model can be improved.

The invention discloses a document reordering method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps that in response to received query information, at least one candidate document matched with the query information is obtained, and the candidate document comprises at least one candidate sentence; dividing the query information into a plurality of query elements, and for each query element, based on the similarity between each candidate sentence in the at least one candidate document and the query element, obtaining k first candidate sentences of which the similarity with the query element is greater than a preset value in all candidate sentences in the at least one candidate document; based on the number of the first candidate sentences included in each candidate document, obtaining a correlation index of each candidate document and the query information; and according to the correlation index of each candidate document, reordering each candidate document. Through the method, the calculation complexity of document reordering can be reduced, and the efficiency can be improved.

The invention discloses an FME-based adjacent pattern spot fusion method and system, and relates to the technical field of adjacent pattern spot fusion, and the method comprises the steps of data processing, parameter setting and rule configuration, adjacent pattern spot fusion execution, result verification and output and feedback and optimization, fusion standard and rule determination, index system unified evaluation, and spatial relationship optimization processing. Manual intervention is reduced through the automatic working process, and the processing efficiency is improved; the invention further provides an adjacent pattern spot fusion system based on FME, the adjacent pattern spot fusion system comprises a data preprocessing module, a data input module, a parameter setting and rule configuration module, a comprehensive correlation index calculation module, a pattern spot fusion module and a result verification and output module, and the adjacent pattern spot fusion method based on FME is executed.

The invention relates to a monitoring and early warning method and device for coal mine rock burst display. The method comprises the following steps: acquiring ground sound frequency data and ground sound energy data of a target monitoring area in a coal mine in real time; storing the ground sound frequency data into a frequency data set, and storing the ground sound energy data into an energy data set; based on the frequency data set and the energy data set, determining a first relevance index value between the rock sound frequency and the rock sound energy interval in the current rock burst period; based on the energy data set and a plurality of preset energy intervals, determining a second relevance index value between the rock noise energy and the energy intervals in the current rock burst period; according to the first relevance index value and the second relevance index value, the danger degree of rock burst in the target monitoring area is determined, and early warning is carried out based on the danger degree. According to the scheme, the accuracy and timeliness of rock burst early warning are improved.

The invention relates to the technical field of anti-seismic performance detection, in particular to an anti-seismic performance detection method and system for an assembled steel-wood composite structure, and the method comprises the steps: arranging a multidirectional acoustic emission sensor in a steel-wood structure node area, applying simulated seismic vibration, and synchronously collecting acoustic emission signals; the method comprises the following steps: preprocessing collected acoustic emission signals, dividing the acoustic emission signals into signals corresponding to microcracks and contact slippage, respectively calculating an energy attenuation rate and a phase offset, and constructing a matrix; micro-impact is applied to the steel-wood node through an electromagnetic excitation device, a response signal is collected, resonant frequency is extracted, the drift distance between the resonant frequency and a reference value is calculated, and the severity of interface damage is determined according to the value of the drift distance; the damage correlation index calculation takes the characteristic value of the interface wave propagation characteristic matrix and the resonance frequency drift distance as input, the damage correlation index is calculated through the correlation coefficient, and the damage grade is divided according to the correlation coefficient. And differential characterization of different damage types is realized through the interface wave propagation characteristic matrix.

The invention relates to the technical field of hydraulic ring geology monitoring, in particular to a hydraulic ring geology monitoring system and a monitoring method thereof.The hydraulic ring geology monitoring method comprises the steps that a microseismic sensing array and a distributed optical fiber temperature sensing network are arranged, and three-dimensional vibration waveform data and axial temperature gradient data of a target area are collected; and feature parameters such as micro-seismic event space positioning, vibration energy release and temperature anomaly gradient are extracted through space-time registration processing. And calculating a seepage mutation correlation index based on the characteristic parameters, inputting the seepage mutation correlation index into a pre-constructed three-dimensional seepage-stress coupling model, generating a formation pore pressure distribution cloud picture and a rock mass stability coefficient K, and improving the seepage mutation monitoring precision by dynamically adjusting a weight coefficient of the seepage mutation correlation index and updating boundary conditions of the coupling model. According to the invention, the detection capability of seepage mutation is improved, a real-time and accurate prediction result can be provided for a complex geological environment, and effective support is provided for safety monitoring in the fields of mines, underground engineering and the like.

The invention provides an energy storage equipment service life prediction method and system, electronic equipment and a storage medium, and belongs to the technical field of equipment health prediction, and the method comprises the steps: obtaining operation data corresponding to target energy storage equipment; a target correlation index is obtained, the target correlation index is a correlation index between the multiple factors and the service life of the energy storage equipment, and the target correlation index is obtained through calculation based on historical operation data of the multiple pieces of energy storage equipment; target nonlinear change features are obtained, the target nonlinear change features are nonlinear change features between a multi-factor association vector extracted based on a causal index and the service life of the energy storage equipment, and the multi-factor association vector is constructed based on data corresponding to multiple factors; and inputting the target correlation index, the target nonlinear change feature and the operation data corresponding to the target energy storage equipment in the target time period into an energy storage equipment life prediction model for equipment life prediction to obtain a life prediction result. The accuracy of predicting the service life of the energy storage equipment can be improved.

The invention provides a low-voltage arc fault detection method and apparatus, and an electronic device. The method comprises the steps of obtaining m current data of a target low-voltage power distribution network within a preset time period; m is a positive integer greater than or equal to 1, feature extraction is performed on the m current data based on a preset feature extraction method, m first feature sets are obtained, and each first feature set comprises n first features; n is a positive integer greater than or equal to 24, performing correlation calculation on n first features in the target first feature set to obtain a correlation index scores, and performing feature selection on the target first feature set according to the a correlation index scores by adopting a preset feature selection method to obtain a target second feature set; and inputting the target second feature set into a preset arc detection model to obtain a target arc detection result. When arc fault detection is carried out, the accuracy and the stability of fault detection can be improved.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The invention relates to the technical field of circuit board analysis, in particular to an integrated circuit defect analysis method and system, and the method comprises the steps: extracting and screening E-test data of a target product from a big data platform for integrated circuit manufacturing; performing defect classification modeling processing on the screened E-test test data; calculating a defect correlation index according to the classification accuracy and the correlation metric value of each candidate E-test item; determining a defect judgment threshold value and a numerical value distribution direction related to the defect based on a logic regression classifier result of the key test item, and generating a binary defect distribution diagram corresponding to each jointed board; calculating the image similarity between the bad jointed board graph and each good jointed board graph; judging whether the image similarity is higher than a preset threshold value or not, and adding the jointed boards of the good jointed board graph into a virtual bad jointed board list; and based on the virtual bad jointed board list, analyzing and comparing the manufacturing process flow data of the virtual bad jointed board and the original defective jointed board, and determining the potential root cause causing the defect. According to the method, the problems that in the E-test scene of the circuit board, the number of defect samples is extremely small, categories are extremely unbalanced, an experience threshold value is difficult to set, and root causes are difficult to trace can be solved.

The invention discloses a GIS disconnecting switch full-state quantity edge intelligent analysis system based on the Internet of Things, particularly relates to the field of online monitoring of high-voltage power equipment, and is used for solving the problem of false correlation interference caused by lack of stage anchor points and causal constraints in an existing power sequence overall synchronization method. Therefore, the problems of the jitter of the opening and closing position criterion and the inconsistency of multi-mode diagnosis can be solved. According to the method, event anchor points are extracted from a power sequence, an auxiliary contact sequence and a travel sequence on the edge side, a unified time axis and a stage tag sequence are constructed, and a multi-modal fragment stream is segmented; generating a causal mask according to the stage label, calculating a masking mutual information matrix, and outputting a peak sequence and a peak sequence index; calculating a mutual information arc length ratio and a peak sequence accumulative homomorphism in the window, assigning a trust mark based on a prototype facet area coefficient, and outputting a position sequence in the stage; and splicing the position track and generating an evidence association index to realize full state quantity intelligent analysis.

The invention discloses an intelligent calibration method and system for the electric quantity of an instrument, and relates to the technical field of electric energy metering of an electric power system.The intelligent calibration method comprises the steps that an electric meter network diagram with a main meter and sub-meters as nodes is constructed, and robust composite correlation indexes are calculated on the basis of reading increments in a sliding time window; the interference of common background load and instantaneous noise is suppressed by using a threshold clipping and main table purification mechanism, so that the depiction capability of the correlation graph on a physical connection relationship is improved fundamentally, and the phase to which the sub-table belongs can be identified more accurately in a stock area with unknown phase or incomplete topology; a graph neural network model is introduced based on the network graph, electric meter embedding representation with physical meaning is obtained through neighborhood information aggregation, phase grouping is achieved in combination with unsupervised clustering, split-phase energy balance loss is designed in the training stage, and the difference between the reading of a main meter and the sum of the reading of sub-meters in each phase group is used as a constraint. Alignment of the embedding space and the energy conservation relation is pushed in a self-supervised mode.

The invention provides a wind power blade aerodynamic performance simulation analysis method, which comprises the following steps of: preprocessing original wind condition data by adopting a data cleaning algorithm according to time sequence data of physical quantities such as wind speed, wind direction and turbulence intensity, removing noise interference and abnormal points in the data through methods such as abnormal value detection and missing value filling, and calculating the aerodynamic performance of a wind power blade. Obtaining cleaned wind regime time sequence data; a data encryption and compression algorithm is adopted to encode a wind regime boundary condition file, the security and efficiency of data transmission and storage are improved, and seamless joint and quick calling of wind regime data and a computational fluid mechanics model are realized by establishing a correlation index between the wind regime data and a blade model.

The invention provides an elastic monitoring alarm method and system based on multistage cooperative verification. The method comprises the step of remarkably improving the alarm accuracy through a three-level verification mechanism. The first-stage verification adopts intelligent anomaly detection, compares a current index with a historical baseline, and identifies a preliminary anomaly; in the second-stage verification, association index analysis is introduced, and pseudo anomalies caused by normal business fluctuation are filtered; and the third-stage verification performs cross-customer collaborative analysis to distinguish individual anomaly from global events. In addition, fault root causes are automatically positioned through dependency link analysis, and structured alarm information containing causes, confidence coefficients and processing suggestions is generated. And a closed-loop learning mechanism is established, operation and maintenance feedback is continuously collected, and alarm parameters are automatically adjusted by adopting Bayesian optimization. According to the method, the false alarm rate is reduced, the root cause positioning accuracy is improved, the average repair time is shortened, and the IT operation and maintenance efficiency is remarkably improved.

The embodiment of the invention discloses a power grid line state monitoring method and device based on vibration acoustics. The method comprises the following steps: acquiring a power grid power transmission line vibration signal and a sound wave signal; according to the topological structure of the power transmission line, the spatial distribution of adjacent lines and the measuring point positions of different sensors in the same line, constructing a signal propagation model, and calculating a multi-dimensional correlation index between the vibration signal and the sound wave signal, the correlation index including a time domain correlation coefficient, a frequency domain power spectral density and a phase difference feature; simulating signal propagation characteristics in a normal operation mode by using the signal propagation model, generating a reference correlation index, and identifying whether the power transmission line has an abnormal state; and carrying out accurate positioning on the abnormal state by adopting a spatial positioning algorithm, and carrying out multi-level evaluation according to the abnormal confidence coefficient and the influence range to generate a line abnormal report. According to the invention, real-time monitoring and accurate positioning of the abnormal state of the power transmission line are realized.

The invention discloses a natural grassland biomass remote sensing estimation method and system based on spatial layering heterogeneity, and relates to the technical field of biomass remote sensing prediction. A geographic detector method is used for carrying out driving force sensitivity screening on multiple remote sensing indexes according to the spatial layering heterogeneity of biomass; the defect that a traditional index screening method which only depends on experience judgment cannot accurately provide high-correlation indexes of the biomass continuation mechanism in a regional self-adaptive reaction region is overcome, and a regional self-adaptive high-driving-force index data set which accurately reflects the biomass continuation mechanism can be screened out. Meanwhile, according to the method, a biomass machine learning remote sensing prediction model is optimized, the model is constructed by using index combinations with clear physical significance, and optimization is carried out in various machine learning model methods, so that the estimation precision of the biomass is remarkably improved, and a biomass raster data set which is high in precision and conforms to the actual situation can be produced.

The invention discloses a data aggregation processing method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to data aggregation in the financial field. The method comprises the following steps: firstly, grouping dimension fields by utilizing a mapping rule and a hierarchical clustering algorithm; and secondly, dynamic grouping mapping is established, one-time scanning is performed on the source data table, and each data record is directly attributed to a corresponding grouping structure, so that the data processing efficiency is improved. Meanwhile, a correlation index of the index and the data partition is constructed based on a Hash mapping algorithm. Under the support of an indexed data structure, the aggregation calculation function can efficiently execute operations such as summation and averaging, and rapid aggregation of multi-dimensional data is realized. Generally speaking, according to the method, the correctness of an aggregation result is ensured, meanwhile, the calculation and storage pressure of the system is remarkably reduced, the timeliness and expandability of data analysis are improved, and the method is particularly suitable for real-time or quasi-real-time multi-dimensional analysis requirements in a large-scale business scene.

The invention discloses a hierarchical optimization search underwater acoustic Doppler estimation method and system, and the method comprises the steps: S1, a receiving end receives an OFDM signal frame, carries out the preliminary frequency offset scanning, and obtains a preliminary Doppler frequency offset value f corresponding to the maximum value of a correlation index and a coarse Doppler estimation interval [f-1, f + 1]; s2, randomly generating a group of initial particles in the coarse Doppler estimation interval [f-1, f + 1], each initial particle representing a candidate frequency offset value; and S3, calculating the amplitude of the mutual ambiguity function of each initial particle as a fitness function for continuous iteration until a convergence condition is met, and obtaining a fine Doppler frequency offset f1. The problem of high calculation complexity can be effectively solved under the condition that Doppler estimation precision is guaranteed.