832 results about "Regression analysis" patented technology

The invention discloses an exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention discloses a territorial space planning index dynamic evaluation model, and particularly relates to the field of territorial planning, and the model comprises a multi-source heterogeneous data fusion module, an index system construction module, a dynamic adjustment model construction module, a scheme evaluation and optimization module, and a model application and dynamic updating module. According to the method, various spatial data are integrated through a multi-source heterogeneous data fusion technology, and the data quality is ensured by adopting a three-stage cleaning assembly line and an intelligent correction algorithm; the method comprises the following steps: constructing an index system based on six core contents of territorial space planning, and determining an index weight by applying a Delphi method and an analytic hierarchy process to realize adaptive adjustment of an index threshold; the model adopts time sequence analysis, regression analysis and a machine learning algorithm to predict a development trend, a dynamic feedback mechanism is established in combination with a system dynamics method, and an evaluation scheme is quantified through an analytic hierarchy process; the model performance is evaluated by adopting indexes such as accuracy rate and precision rate, and parameters are continuously optimized based on an evaluation result; finally, the model is integrated to a service system to realize real-time monitoring, and dynamic updating is realized through three mechanisms of data driving, performance triggering and regular maintenance.

The invention provides a safety monitoring method and system for a financial information service platform. Wherein a collaborative encryption transmission channel between a budgeting unit and an auditing department is constructed, a joint key is generated based on a multi-party computing protocol, and the ciphertext operation authority of a sensitive field is dynamically configured; performing ciphertext variance calculation and regression analysis on the encrypted budget execution record through the security operation node, generating a verifiable statistical result, binding the authority, and synchronously updating the sensitive field access frequency; a hierarchical security threat baseline is generated in combination with permission, frequency and historical query records, and cross-project high-frequency query and data operation deviation are associated; and when the query deviates from the baseline, calculating the risk confidence coefficient according to the deviation, triggering multi-party verification path adjustment and collaborative alarm, and dynamically limiting the ciphertext permission of the abnormal query. According to the technical scheme provided by the invention, cross-department data security sharing and dynamic risk collaborative prevention and control are realized.

The invention relates to a building material mining life cycle process carbon emission improvement potential quantification method comprising the following steps: constructing a full life cycle carbon footprint digital model covering mining, transportation, processing and restoration, and integrating a digital twinning and BIM technology dynamic correlation carbon emission factor library; key carbon emission influence factors are identified through Monte Carlo simulation and regression analysis; establishing a multi-objective optimization model, generating an optimal process parameter combination by adopting an intelligent algorithm, and quantifying the emission reduction potential; model parameters are dynamically calibrated in combination with real-time data, and a closed-loop optimization mechanism is formed; space-time distribution visualization and block chain evidence storage technologies are introduced, and accurate monitoring, intelligent decision making and credible tracing of carbon emission are achieved. According to the method, the problems of data lag and single optimization in a traditional method are solved, the accuracy of carbon emission and the feasibility of an emission reduction scheme are remarkably improved, meanwhile, carbon sink offset evaluation is supported, and a whole-process technical support is provided for mine green transformation.

The invention relates to the technical field of power system automation, and discloses an automatic control method and system for a multifunctional ring main unit. The method comprises the following steps: acquiring voltage, current and temperature parameters of a ring main unit in real time, analyzing load characteristics based on a clustering algorithm in combination with historical operation data, and generating a classification result including a load type, a seasonal trend and a load distribution thermodynamic diagram; a load change trend is extracted based on time sequence regression analysis, a fault prediction model is constructed by using a long short-term memory network, and a dynamic alarm threshold value is set; a grading alarm mechanism is triggered through comparison of real-time data and a model, a fault type and a positioning result are extracted in combination with a Bayesian network algorithm, the fault type is positioned, and finally automatic control is executed according to the fault type. Through data-driven intelligent analysis, the problems that a traditional ring main unit is complex in debugging and lagged in fault response are solved, the operation reliability, the maintenance efficiency and the power grid stability are remarkably improved, and predictive maintenance and self-adaptive control are achieved.

The invention provides a digital twin water conservancy hyper-fusion method for coupling multi-source data and an all-in-one machine, and relates to the technical field of intelligent water conservancy. By constructing a unified water conservancy spatio-temporal data link protocol, efficient fusion of multi-source heterogeneous data of hydrology, meteorology, engineering operation and the like is realized, and the problem of water conservancy system data islands is solved; a coupling distributed hydrological model and a hydrodynamic model are established, and real-time accurate simulation of the watershed hydrological process and the river channel hydrodynamic process is achieved; a multi-target dynamic weight reinforcement learning algorithm is adopted to optimize a water conservancy scheduling scheme, model parameters are dynamically corrected in real time through a time sequence error regression analysis model, and closed-loop adaptive optimization is achieved; according to the method, closed-loop cooperation of water conservancy data fusion, real-time accurate simulation, intelligent scheduling optimization and dynamic parameter self-correction is realized, and the real-time performance and accuracy of water conservancy intelligent decision making are remarkably improved.

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

The invention discloses a blood pressure measuring method, and belongs to the technical field of medical health monitoring. Comprising the following steps: synchronously acquiring an ECG signal and a PPG signal, and preprocessing the ECG signal and the PPG signal; performing denoising and feature extraction on the ECG signal and the PPG signal; a weighted kernel function regression model fused with multi-modal features is constructed, and a blood pressure predicted value is generated in combination with random forest feature selection and a piecewise linear model dynamic switching mechanism; the model weight is updated based on a recursive least square method, dual-mode calibration is carried out by adopting an event triggering and period triggering mixed mechanism, and a final blood pressure value is obtained through global fine tuning. Compared with the prior art, the method has the beneficial effect that the accurate prediction of the blood pressure can be realized by combining the regression analysis principle with the individual physiological characteristics.

The invention relates to an aging test method and device and a medium, and relates to the technical field of aging tests.The method comprises the steps that based on a distributed data acquisition system architecture, performance parameters of a to-be-tested product are synchronously acquired at a plurality of test points, data acquired at all the test points are synchronously integrated through timestamp alignment, and integrated data are obtained; based on a preset data analysis model, performing time sequence analysis on the continuously collected performance parameters, fitting a performance degradation curve by using a regression analysis method, determining a degradation function, and predicting the performance failure time of the to-be-tested product according to the degradation function; and determining a plurality of accelerated aging test schemes based on the performance failure time and a combination of a plurality of environmental factors, and executing all the accelerated aging test schemes through a simulator to obtain a plurality of life prediction results. The effect of improving the accuracy of performance evaluation and life prediction of the electronic product is achieved.

The invention discloses a BIM-based water conservancy project construction quality intelligent management method and system, and relates to the technical field of intelligent management. During operation of the system, various parameters related to the water conservancy project construction quality are acquired based on construction of a BIM model, data from a data acquisition module are preprocessed and integrated, and a BIM model is established; the method comprises the following steps: establishing a mathematical model for water conservancy project quality monitoring, establishing a dynamically updated and optimized model through regression analysis of historical data and a machine learning algorithm, predicting potential risks and quality problems in a construction process, and calculating a dynamic monitoring index Dtjk according to parameters output by a BIM model establishment module, the real-time monitoring and early-warning module is used for monitoring a BIM model, comparing the real-time monitoring and early-warning module with a preset qualified threshold value in the BIM model, giving out early warning according to the preset threshold value, reminding management personnel to take intervention measures, and providing improvement suggestions and optimizing a construction management scheme according to feedback information of the real-time monitoring and early-warning module and changes of a dynamic monitoring index Dtjk.

The invention discloses an artificial precipitation data acquisition system and evaluation method based on a remote sensing image, and relates to the technical field of ecological environment restoration and meteorological science. Comprising the following steps: acquiring remote sensing image data and rainfall data of meteorological stations in a region, performing geometric correction and radiation correction on the remote sensing image data based on ENVI software to obtain corrected remote sensing data, and calculating the rainfall data of the meteorological stations on the basis of the corrected remote sensing data and regional historical rainfall data. The rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are respectively obtained through a regression analysis method and a pixel dichotomy method, the rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are compared, the influence of rainfall enhancement on vegetation recovery is quantitatively analyzed, and the rainfall enhancement effect is analyzed by utilizing spatial-temporal dynamic change. According to the method, the influence of rainfall enhancement on the ecological environment can be evaluated more comprehensively and accurately, and a new scientific basis is provided for ecological restoration of arid regions.

The invention provides a genetic disease gene detection data analysis method and system based on big data, and relates to the technical field of gene detection data analys.The genetic disease gene detection data analysis method comprises the steps that quality control and duplicate removal are conducted on gene sequencing original data, sequence comparison and variation detection are conducted, and a standard variation detection report is generated; performing multi-scale feature sampling and optimization by using an improved random field diffusion model, inputting high-dimensional feature distribution into a dual self-activation iterative network for feature extraction and integration to obtain a feature mapping matrix and a feature evolution trajectory, and inputting the feature mapping matrix and the feature evolution trajectory into an integrated predictor to obtain an integrated predictor; and in combination with Gaussian mixture process regression analysis and an improved Bayesian reasoning network, establishing a risk association network and training a deep hierarchical decision tree, and outputting a multi-dimensional risk assessment report.

The invention discloses an ecological carbon sink measurement and estimation data fusion platform for the thermal power industry, and belongs to the technical field of data fusion. The method is realized through five steps of a data acquisition module, a carbon sink measurement and calculation module, a prediction model construction module, a data fusion module and a decision support module. According to the invention, through butt joint with a thermal power enterprise energy management system, production data including generating capacity and coal consumption are acquired in real time, and the timeliness of the data is greatly improved. Ecological environment data and meteorological data around a thermal power enterprise are comprehensively collected by using a satellite remote sensing technology and a ground sensor, and the comprehensiveness and accuracy of the data are ensured. According to the method, a dynamic adjustment mechanism is adopted, and the pre-estimation model is continuously optimized according to real-time data feedback, so that the model can adapt to the change of the environment. And time sequence analysis and regression analysis are combined to form an integrated model, so that the accuracy and reliability of a pre-estimation result are greatly improved.

The disclosure discloses a method for strengthening shear strength of a RC beam with a compressed steel bar, including step one: analyzing and concluding that the compressed steel bar has a positive effect of on shear strength of the RC beam via experimental testing and / or establishing a truss model; step two: carrying out a finite element simulation; step three: comparing a result obtained by the finite element simulation with that obtained by the experimental testing, to verify a reliability of the finite element model; step four: systematically researching a comprehensive impact of a reinforcement ratio of the compression steel bar, a stirrup ratio ρsv, a reinforcement ratio ρst of a tensile longitudinal steel bar, a shear span ratio / and a concrete strength grade (fco) on the shear strength of beam; and step five: obtaining a mathematical model of an increment of shear strength via regression analysis.

The invention relates to the technical field of finance, in particular to a real-time investment decision-making system and method based on multi-modal fusion, and the method comprises the steps: integrating social media emotion data, public opinion event classification data and market structured data through a multi-modal emotion information fusion module, generating a comprehensive emotion representation vector, the market state judgment module judges market states based on historical market data and generates time-weighted market state representations, the reinforcement learning combination management module generates asset allocation decisions according to the market state representations and the time-weighted market state representations, and the multi-strategy collaborative decision module integrates reinforcement learning, decision trees and regression analysis strategies and generates collaborative decision signals. The risk self-adaptive control module predicts the market fluctuation rate, assesses the investment risk and adjusts decision signal execution parameters, the decision signal generation module generates a final investment decision signal and outputs the final investment decision signal to the transaction execution system, and the transaction execution system performs multi-modal information fusion and a time-sensitive attention mechanism. And the comprehensive understanding capability and the time sequence change adaptive capability of the market are improved.

The invention relates to the technical field of new energy automobile generator performance detection, and discloses an automobile generator performance detection method and device. The method comprises the steps of collecting working condition data such as voltage, current, rotating speed and internal temperature of a generator in real time through a multi-source sensor, establishing a correlation model among the voltage, the current, the rotating speed and the internal temperature based on a regression analysis method, and dynamically adjusting safety thresholds of the voltage and the current according to the model. Compared with the prior art that monitoring depends on a fixed threshold value, especially under complex working conditions such as high load, rapid acceleration and high temperature conditions, the technical problem that working condition changes cannot be adapted in real time is solved, real-time monitoring is achieved by dynamically adjusting the voltage and current threshold values and combining the rotating speed and the internal temperature change rate, and the real-time monitoring efficiency is improved. The stable operation of the generator under the multi-dimensional working condition is ensured, so that the problem of equipment damage caused by overload is avoided, the safety of the system is improved, and the service life of the system is prolonged.

A method and system for AI based insight extraction from format-bound financial transaction data includes transforming a structured dataset in ISO format into a transformed dataset having metadata interpretable by a LLM Metadata includes descriptions of field names, expected values, entity relationships, and business rules. The transformed dataset is analyzed using machine learning models such as regression analysis, principal component analysis, predictive modelling, or anomaly detection. An intent and content of a natural language query are determined using an NLP model. Based on the intent, context, and metadata, the LLM generates a database query, which is executed on the structured dataset to retrieve relevant data. Insights are generated by combining the retrieved data with machine learning results.

The invention relates to the technical field of data statistics, in particular to an intelligent water meter missing data complementation method and system.The intelligent water meter missing data complementation method includes the steps that initial segmentation points of a water consumption data curve are determined by analyzing the slope change condition of the water consumption data curve, and the variation index of the initial segmentation points is further analyzed to determine segmentation points of the water consumption data curve; performing clustering analysis on all segmentation points in the water consumption data curve of the user in all days, and constructing a standard water consumption data curve of the user based on a clustering result; and respectively using a Bezier curve fitting method and a regression analysis method, and combining the change trend of the standard water consumption data curve to carry out data completion on the missing data segment. Through the method and the system provided by the invention, complementation data which conform to the overall trend and are in smooth transition can be obtained, the integrity and the accuracy of the data are remarkably improved, and a more reliable data basis is provided for subsequent water resource management and decision support.

The invention relates to the technical field of 5G-R network detection and monitoring, discloses a 5G-R network situation awareness method based on distributed monitoring and multi-source information fusion, and aims at solving the problems that a 5G-R network is large in scale, high in dynamic performance and complex in data isomerism. Multi-source data of a core network, a wireless network, special equipment, interface monitoring, detection equipment, a GIS and the like are collected in real time; according to the method, technologies such as Pearson's correlation coefficients, FP-Growth, Bayesian analysis, a time sequence point process, a Hookes theory, a Gaussian mixture model, a decision tree, S-ARIMA, Boxplot, N-sigma, iForest, regression analysis, a neural network and KL divergence are combined to realize multi-source data fusion, intelligent analysis and visual perception, including network alarm, application quality, operation and maintenance and resource management. According to the method, the comprehensiveness, the real-time performance, the fault diagnosis accuracy and the operation and maintenance efficiency of the 5G-R network are improved, and the requirements of low delay and high reliability of a railway scene are met.

The invention relates to the technical field of brain power assessment, in particular to an operator brain power assessment and monitoring system based on manned-unmanned flight cooperative tasks, which comprises the following steps: acquiring brain wave signals, voice fluctuation and finger muscle activity data of an operator, and generating operator brain power load data; setting a personalized load reference value and a threshold range of an operator; based on multi-dimensional regression analysis and a self-adaptive deep learning model, a nonlinear relation model between task characteristics and brain load is constructed, the load state is dynamically evaluated, and a real-time load change prediction value is output; predicting a future high-load risk period according to the fluctuation mode of the mental load fluctuation curve; dynamically adjusting task parameters in a multi-task execution process; according to the invention, the dynamic reference setting of the mental load of the operator is realized, so that the individuation and accuracy of load evaluation are ensured, the influence of key task characteristics on the load can be quickly reduced when the load of the operator is increased, and an efficient and accurate load reduction effect is realized.

The invention discloses a lithium ore exploration system and method based on data analysis, and relates to the technical field of lithium ore exploration. In the operation process of the system, data from different sources are collected, geological abnormal parameters, lithologic density fluctuation parameters and gravity gradient parameters are obtained, the preprocessed data are analyzed, and a lithium ore exploration result is obtained; the method comprises the following steps: extracting geological features related to mineral resources, carrying out pattern recognition by using a deep learning technology, extracting key features from geological data, obtaining lithologic density Sgeo, rock magnetic parameters Wrock and rock resistivity Rgrad, and generating a three-dimensional geological model of a mining area through an AI algorithm based on the extracted features. The AI algorithm comprises regression analysis and a convolutional neural network (CNN) to process spatial data and generate three-dimensional visualization of a geologic structure, historical mining area distribution data is combined, the system uses a prediction algorithm random forest or a support vector machine (SVM) to predict lithium ore distribution, survey feedback is generated according to a prediction result, and a survey scheme is dynamically adjusted.

The invention discloses a production processing data collaborative management system and method of a curtain wall single aluminum plate, and relates to the technical field of data collaborative management. According to the method, the production processing data and the quality detection data of the curtain wall single aluminum plate are collected, the dynamic time warping algorithm is adopted to analyze the time sequence data change between the working procedures, the time deviation and the performance fluctuation between the working procedures are recognized, then the working procedure coordination index is obtained, and the time synchronism and the efficiency matching degree between the working procedures are measured. And performing classification and regression analysis on the quality index of each processing procedure through a decision tree algorithm to obtain a quality stability index, and identifying and predicting quality fluctuation and abnormal modes in different procedures. And judging whether the sequence and priority of the production processes need to be adjusted or not through comprehensive analysis of the process coordination index and the quality stability index. And time inconsistency, efficiency bottleneck and quality fluctuation in the production process are identified, intelligent optimal scheduling of the production process is realized, and the collaborative management level of the production process is improved.

The invention provides a prediction method and prediction equipment for a building deformation point cloud time sequence, and relates to the technical field of deformation monitoring. The method comprises the following steps: analyzing an aligned point cloud set to obtain a point cloud set of an abnormal region; based on the multi-resolution features of the point cloud set of the abnormal region, extracting topological features of a preset connection structure of the abnormal region, and constructing a topological graph of the abnormal region; carrying out stress constraint and material characteristic constraint on the topological graph of the abnormal region, and determining submerged space characteristics of the topological graph; based on a latent diffusion model, performing interpolation and extrapolation processing on the latent space features, and determining a prediction vector field and a suspected damage area of the latent space features; performing multi-scale regression analysis on the suspected damage area to obtain local parameters; and carrying out fusion and consistency evaluation on the three-dimensional deformation prediction field corresponding to the prediction vector field of the submerged space features and the local parameters to obtain a fused global deformation prediction field, and obtaining high-risk deformation distribution. According to the method, deformation can be accurately predicted.

The invention relates to the technical field of network optimization, and discloses an AI-driven real-time network optimization algorithm, which comprises the following steps: data optimization: preprocessing network state data by using a generative adversarial network, extracting optimization features and generating enhanced data; topology modeling: modeling network topology by adopting a graph neural network, and updating node features; resource optimization: dynamically adjusting calculation, storage and bandwidth resources by using deep reinforcement learning; global scheduling: optimizing resource allocation based on a multi-objective optimization method; secondary optimization: combining a Lagrangian relaxation method and a graph optimization technology to further optimize a scheduling strategy; and adaptive learning: improving the adaptive capacity of the system in a dynamic environment through self-supervised learning and meta-learning. Through a matrix operation technology based on a regular equation and in cooperation with an efficient data preprocessing process, the effect of improving the solving speed of the regression model is achieved, the problem that calculation is slow on a big data set in a traditional method is solved, and rapid and accurate regression analysis is achieved.

The invention discloses a mobile phone cloud computing power lease scheduling method for a virtual computer, and relates to the technical field of resource scheduling, and the method comprises the steps: based on the historical load data of construction hardware of all cloud computing power lines, analyzing the performance change trend of the historical load data of the construction hardware, and evaluating the dynamic load index of each cloud computing power line; acquiring historical cloud computing power user lease data, analyzing the computing power overhead demand of the user lease data, and constructing a user computing power lease preference portrait; for regression analysis, predicting a to-be-executed lease task demand of a unit time node; and according to the dynamic load index of each cloud computing power line and the to-be-executed lease task demand of the unit time node, constructing a cloud computing power resource scheduling model to maximize the utilization of the computing power resources of the cloud computing power lines, and generating a dynamic cloud computing power lease scheduling task. The method has the advantages that the user experience and the resource utilization rate are improved.

The invention discloses a TC path and intensity prediction method and device based on AI and physical characteristics and a medium, and aims to improve the short time scale prediction precision of the TC path and intensity, and through integration of a plurality of factors with definite physical significance, such as guide airflow, ventilation flow, vorticity, ocean surface temperature and the like, the TC path and intensity can be predicted. The physical factors are subjected to correlation analysis, regression analysis and the like, and it is ensured that the input factors have significant influences on TC paths and intensity changes. A plurality of AI models such as a convolutional neural network (CNN), a recurrent neural network (RNN), a multi-layer perceptron (MLP), a random forest (RF), a limit gradient boost (XGBoost) and a long short-term memory network (LSTM) are adopted to capture a complex nonlinear relationship between meteorological factors. In addition, by adopting a real-time feedback mechanism, new observation data are allowed to be dynamically input, model parameters are optimized, and the real-time performance of a prediction result is improved. The method is particularly suitable for the northwest Pacific Ocean region, has wide application prospects, and provides scientific support in the fields of meteorology, aviation, marine transportation and the like.

The invention discloses an intelligent classroom management system based on AI, relates to the technical field of intelligent classrooms, and mainly solves the problems of inaccurate teaching resource matching, inaccurate student performance mastering and unobvious feedback effect. Comprising a student information management module, a teaching resource library, an intelligent teaching auxiliary module, an interactive communication platform, a student behavior analysis module and a family-school communication module, and student demands are analyzed through a collaborative filtering algorithm to improve accurate matching of teaching resources; student behaviors are analyzed and evaluated through a regression analysis prediction model, and comprehensive mastering of students is improved; teaching experience is continuously optimized through feedback circulation, and the feedback effect is improved.

The invention provides a geotechnical engineering settlement prediction method which comprises the following steps: S1, performing settlement prediction by adopting a traditional regression analysis method to obtain a regression curve; s2, calculating a historical regression value on each historical time node and a historical deviation value of the monitoring data; the historical regression value is a value of a regression curve on a historical time node; s3, based on the historical deviation value of each historical time node, performing Kriging interpolation on a future time node, and calculating a regression prediction deviation value; s4, the regression prediction deviation value on the future time node obtained through interpolation and the regression prediction value on the future time node are added, a final settlement prediction value is obtained, and the regression prediction value is the value on the regression curve on the future time node. Optimization is carried out on the basis of a traditional regression analysis method, and the settlement prediction precision is effectively improved.

The invention relates to the technical field of lake chlorophyll a inversion, and provides an inland lake chlorophyll a inversion method and system based on hyperspectral data, and the content of chlorophyll a in a lake range is inverted through a multiband index model and a regression method. Firstly, hyperspectral image data of a lake is obtained and preprocessed to remove noise and improve data quality. Then, extracting a water body range by adopting an NDWI index, and extracting wave band information of different sampling points; based on optical characteristics of lake water quality, a multiband index model considering pigment absorption and remote sensing reflectivity is constructed, and an inversion model is optimized in combination with regression analysis. According to the method, the hyperspectral remote sensing image data are collected and fused only through actual measurement of a small number of points, the constructed model is applied to the lake chlorophyll a distribution characteristics on the scale of a monitoring area, accurate inversion of the concentration of the chlorophyll a in the whole area of the lake can be achieved, and the problem that the accuracy is low when remote sensing data is singly adopted for inversion is solved.

The invention discloses an asset use analysis method and system based on an intelligent prediction and optimization algorithm, and aims to solve the problems of low efficiency, insufficient prediction capability and unscientific purchase decision in existing asset management. According to the invention, asset use records, maintenance information and purchase history are obtained through the data acquisition module; constructing an asset use prediction model by using time sequence prediction, regression analysis and machine learning algorithms, and predicting future asset demand quantity and use trend; generating an optimized purchase plan and an asset allocation scheme in combination with an inventory state, an asset life cycle and a budget constraint; and the prediction result and the optimization suggestion are displayed through a visual tool, and support is provided for decision making. According to the invention, efficient planning and purchase optimization of asset use are realized, and the method can be widely applied to the field of enterprise asset management and supply chain optimization.