2172 results about "Linear regression" patented technology

The invention relates to the field of nutrition and health management, and discloses a nutrition evaluation and prediction method and system based on big data, and the method comprises the following steps: firstly collecting the physiological data, diet record and activity data of a user, and carrying out the preprocessing of the data, and guaranteeing the data quality; dimensionality reduction is carried out through feature extraction and principal component analysis, and key features are extracted; the method comprises the following steps: evaluating daily nutritional requirements of a user by using a multivariable linear regression model, a Bayesian network and causal inference, predicting a nutritional requirement change trend of the user in a future period of time based on a long-short-term memory network, and optimizing daily diet selection of the user by using a Markov decision process; and finally, generating a personalized nutrition scheme by adopting linear programming and a genetic algorithm, and ensuring that the nutritional requirements and health targets of the user are met. According to the method, personalized and scientific diet suggestions can be provided, and the quality and effectiveness of individual nutrition management are improved.

The invention relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention relates to the technical field of gas detection, and discloses a gas detection method and device based on an MEMS gas sensor array. The method comprises the following steps: performing grouping and detection data acquisition on a plurality of MEMS gas sensors according to target gas types to obtain an original detection data set; performing curve fitting and feature extraction to obtain a feature parameter set, and constructing a sensor array feature model; correcting the original detection data set to obtain standardized response data; extracting a plurality of response characteristic quantities and inputting the response characteristic quantities into an unknown input observer for residual calculation and signal abnormal point identification to obtain abnormal response data; performing piecewise linear regression and dynamic calibration to obtain a target detection data set; and inputting the target detection data set into the sensor network model for array deployment optimization to obtain an array deployment optimization scheme. The adaptive deployment optimization of the sensor array is realized, and the space coverage efficiency of the detection network is improved.

The invention discloses a crop canopy height estimation method based on laser radar and multispectral fusion, and the method comprises the steps: scanning a crop field through the laser radar, and obtaining the field point cloud data; aerial photography is carried out on a crop field through an airborne multispectral camera to obtain field multispectral image data, and after data image splicing, crop region extraction and land parcel segmentation, two point cloud feature parameters with the strongest correlation with crops are extracted from point cloud data to obtain a point cloud feature parameter; the method comprises the following steps of: extracting a vegetation index with the strongest correlation with the crop height from multispectral image data, solving through multiple linear regression, and establishing a height estimation model; according to the invention, the multispectral vegetation index and the laser radar data are combined, so that the crop canopy height monitoring precision is improved, the crop growth monitoring precision and reliability can be improved, the crop growth condition can be comprehensively understood, and an agricultural decision maker can determine the crop canopy height based on the crop canopy structure data. And a more accurate, timely and effective farmland management decision can be made.

The invention discloses an intelligent shield muck improvement method and system based on machine learning, and the method and system achieve the intellectualization and precision of muck improvement in the tunneling process of a shield tunneling machine through integrating muck image classification, improvement parameter prediction and model optimization. Firstly, a YOLOV8 network is utilized to classify muck images in real time, classification randomness is corrected in combination with an XGBOOST model, and classification accuracy and stability are improved. Secondly, performing time sequence prediction on the muck improvement parameters based on an LSTM network, and introducing a linear regression and genetic algorithm to improve the prediction precision; and finally, optimal solution is carried out on the muck improvement parameters through a particle swarm optimization (PSO) algorithm, and the stability and efficiency of shield tunneling are remarkably improved. The method solves the problems of randomness and hysteresis caused by dependence on manual adjustment in the prior art, has the advantages of high efficiency and accuracy, and is suitable for automatic application scenes of shield engineering.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention provides an HPLC (High Performance Liquid Chromatography) dual-mode communication stability method, medium and system, and belongs to the technical field of dual-channel multiplexing communication, and the method comprises the following steps: firstly, collecting HPLC low-frequency / high-frequency carrier signals, working voltage / current, temperature and electromagnetic interference data in real time; stable, variable and noise components of a carrier signal are respectively obtained through empirical mode decomposition, and a correlation coefficient matrix of the variable components and interference factors is established by using multiple linear regression and singular value decomposition. And calculating a signal intensity, a signal-to-noise ratio and an error rate evaluation matrix based on the stability coefficient matrix, and constructing an abnormal feature matrix in combination with a communication protocol specification. And finally, inputting the features into a five-layer full-connection neural network, outputting fault type probability distribution through a softmax function, and taking the type corresponding to the maximum probability value as a diagnosis result. According to the invention, the technical problem of rapid and accurate diagnosis of HPLC communication stability in a complex and changeable low-voltage distribution network environment is solved.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and system based on dynamic data monitoring, and the method comprises the following steps: obtaining geological displacement monitoring data, setting a time window range, respectively calculating a local extreme point and a local zero point of the monitoring data in a time window, and calculating the local extreme point and the local zero point of the monitoring data in the time window; obtaining a multi-scale reference value; and based on the multi-scale reference value, calculating a comprehensive statistical magnitude, and generating a data quality evaluation parameter. According to the method, a local extreme point and a zero point are dynamically calculated by setting a time window range, a multi-scale reference value is constructed to carry out comprehensive statistic generation and data evaluation, and collaborative discrimination of piecewise linear regression and threshold comparison are executed to form a critical index; and performing characteristic quantity recursive residual spectrum analysis and matrix decomposition operation by combining the power spectral density distribution and the spectral density matrix to establish an early warning characteristic sequence, and realizing multi-stage early warning indication output based on a grading rule.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is 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.

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention relates to the technical field of metering pumps, in particular to an external meshing gear metering pump flow calculation method based on linear regression and curve fitting, which comprises the following steps: generating an integrated data set by monitoring the flow rate, pressure, temperature and viscosity in real time, setting flow rate, pressure and temperature thresholds based on the data set, judging the flow state and recording abnormity, and calculating the flow rate of the external meshing gear metering pump. Flow velocity and viscosity are selected for flow calculation, a corrected flow value is generated, whether flow is normal or not is analyzed and determined, a flow stability evaluation result is generated, and a visual interface is created to display parameters and a calculation result. According to the method, the multiple sensors are installed at the inlet and the outlet of the metering pump, the flow speed, the pressure, the temperature and the viscosity are monitored in real time, an integrated data set provides a precise basis for flow calculation, a threshold value is set, data are monitored, it is ensured that parameters exceeding the normal range can be recorded and analyzed in time, and the process improves the accuracy of flow calculation and improves the working efficiency. And flow evaluation is optimized, and the operation safety and efficiency are improved.

The invention discloses a supply chain production scheduling optimization method and system based on multi-source data. According to the method, supplier purchase data is extracted through a data crawler, raw material sharing is analyzed in combination with a knowledge graph, and a raw material sharing list is generated; on the basis of delivery records and product attributes, a quality stable value is predicted through linear regression, process similarity is calculated through a text similarity algorithm, and a process similarity matrix is constructed; in combination with historical sales data, a market demand prediction model is constructed by applying multiple linear regression and time sequence analysis. A complex relation among raw material sharing, process similarity and market demands is described through a graph neural network, a product association network graph is generated, an optimal balance point is found between delivery time and a resource utilization rate through Pareto optimization, and a production scheduling scheme is formed; and finally, integrating the performance score, the quality stable value, the raw material sharing list and the production scheduling scheme through an integer programming algorithm, and optimizing a supplier combination. According to the invention, the production scheduling efficiency and precision are improved, and complex business requirements are met.

The invention relates to the technical field of medical image processing, and discloses a craniofacial dynamic reconstruction method and system based on multi-modal data fusion, and the method comprises the steps: arranging a multi-modal data collection device in a target craniofacial region, and obtaining a static CT image, a static MRI image, a dynamic expression video sequence and a surface electromyogram signal; preprocessing the static CT image, the static MRI image, the dynamic expression video sequence and the surface electromyogram signal; inputting the preprocessed data into a multi-scale finite element model, and simulating a coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniofacial model; and fusing the geometric error and the motion consistency score of the real data by adopting a linear regression method, and outputting a comprehensive reconstruction quality index. According to the method, the problems of low craniofacial dynamic modeling accuracy and poor robustness in the prior art can be solved.

A method for carbon tracking and analysis in a city is provided. Basic information of a target city is acquired. A three-dimensional visual city model is constructed based on the basic information. The target city is divided into a plurality of sub-regions. A carbon emission monitoring plan is formulated based on regional properties of each sub-region. According to the acquired carbon emission monitoring data, a carbon emission change of each sub-region within the current preset period is analyzed by means of linear regression to obtain carbon emission change trend data of each sub-region. Carbon emission tracking is performed based on the carbon emission change trend data. A current carbon tracking route and a carbon prediction route are generated by means of a preset ant colony optimization algorithm. A system non-transitory computer-readable storage medium for implementing carbon tracking and analysis in a city and are also provided.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

The invention discloses an extreme low water change attribution and low water resource potential evaluation method, which belongs to the technical field of water resource calculation, and comprises the following steps of: obtaining raster data of runoff and driving factors of the runoff in a research area, performing data preprocessing, deducing the frequency distribution of runoff depth of each grid unit by adopting a non-parametric kernel density estimation method, and calculating the runoff depth of each grid unit; and counting the proportion of the grids meeting the guarantee rate runoff depth threshold in each year in the total grid, and further determining a low water typical year under each guarantee rate. A nonlinear regression simulation model of coupling optimization deep learning is adopted to carry out extreme low-water runoff depth regression simulation research under different driving factor combinations, and a key driving factor combination influencing low-water runoff depth change is mined based on a regression simulation precision result; and analyzing the constraint effect of each key driving factor on the extreme low water and a possible threshold value by adopting a constraint line deduction technology, and evaluating the low water resource potential of a typical year of the extreme low water.

The invention provides a 110V transformer winding insulation resistance absorption ratio detection method, which belongs to the technical field of transformer windings, and is characterized in that transformer operation state parameters and environmental influence factors are acquired through a multi-channel synchronous acquisition system, and thermodynamic and electrical models are established. The research adopts methods of principal component analysis, numerical integration and the like, an insulation resistance change rule is analyzed under a standard test voltage, a weight coefficient matrix is constructed in combination with an operation state and an environmental factor, a measured value is corrected through multiple linear regression and a correction coefficient method, and finally, the insulation resistance is obtained through statistical average and standard deviation analysis. The accurate evaluation of the insulation resistance absorption ratio of the transformer winding is realized; according to the method, the influence of the complex working environment and the operation state of the transformer on the insulation performance is fully considered, and the problem that various complex factors in the operation process of the transformer cannot be comprehensively considered and compensated in the prior art is solved by adopting the means of mathematical modeling, data correction, statistical analysis and the like; therefore, the technical problem of large deviation of the measurement result is solved.

Techniques are described for image processing. For example, a computing device can segment, using a first neural network, image(s) of a scene to determine respective segments for each of the image(s). The computing device can determine, using a second neural network, normal vectors for each of the image(s). The computing device can generate a graph based on each respective segment for each image, each respective normal vectors for each image, and estimated planar distances. The computing device can partition, based on the normal vectors and the estimated planar distances, the graph to determine indexes associated with Gaussian primitives. The computing device can assign, using linear regression, each descriptor of a plurality of descriptors to an index of the plurality of indexes based on a respective weight. The computing device can merge, using a Gaussian tree, Gaussian primitives of the Gaussian primitives with associated indexes that are similar to each other.

The invention relates to the technical field of electrical measurement, and discloses an electrical measurement instrument dynamic calibration error compensation method, which comprises the following steps: S1, multi-source data real-time acquisition: integrating a temperature sensor, a humidity sensor and an electromagnetic interference detection module in a measurement instrument, configuring voltage, current, frequency and other measurement channels, and carrying out multi-source data real-time acquisition at the frequency of not less than 100HZ; environmental parameters (temperature T, humidity H and electromagnetic interference intensity E) and measurement data (model characteristic parameters X1, X2,..., Xn such as voltage, current and frequency) are synchronously acquired. According to the electrical measuring instrument dynamic calibration error compensation method, through multi-source data real-time acquisition and composite error model construction, in combination with multiple linear regression and an improved BP neural network, environment and measurement signal changes are comprehensively captured, an error compensation value is accurately calculated, a measurement error can be effectively controlled within an extremely small range, and the error compensation accuracy is improved. Compared with a traditional method, the measurement precision is greatly improved.

The invention relates to the technical field of crop remote sensing monitoring, and provides a crop and bean mixed planting crop growth estimation method and device based on a multispectral technology, and the method comprises the steps: collecting multispectral data of a mixed planting region, calculating a vegetation index and a texture index according to the multispectral data, and determining the biomass and nitrogen content of crops in the mixed planting region. Based on a general linear regression algorithm and a partial least squares regression algorithm, constructing a plurality of estimation models for estimating biomass, screening out a target estimation model from the plurality of estimation models, and estimating the growth condition of crops in the mixed planting region through the target estimation model. According to the method, an estimation model with high estimation precision is established by combining texture indexes and vegetation indexes which express space detail difference characteristics, and the estimation model is established for a mixed planting region with complex crop object phenotypic characteristics and high estimation difficulty, so that the biomass and the nitrogen content of the mixed crop of rice and beans are estimated losslessly, efficiently and accurately.

The invention provides an electrocardiosignal reconstruction method based on hybrid optimization and multi-modal feature fusion, and the method comprises the steps: obtaining a 12-lead electrocardiosignal, processing the 12-lead electrocardiosignal through a linear regression model, a genetic algorithm and a simulated annealing algorithm in sequence to obtain an optimal three-lead electrocardiosignal, and reconstructing the optimal three-lead electrocardiosignal according to the optimal three-lead electrocardiosignal. The optimal three-lead electrocardiosignal is subjected to one-dimensional convolution layer and maximum pooling processing in sequence to obtain time domain features, the time domain features, the frequency domain features and expert features are subjected to multi-modal feature fusion to obtain fusion features, and the fusion features are input into a Transform mechanism to be processed to obtain a reconstructed electrocardiosignal. The method has remarkable effects in long-range time sequence modeling and local waveform detail optimization. The traditional CNN / RNN is limited by the problem of local receptive field or gradient disappearance, the global rhythm and the local form are difficult to balance, the QRS is adopted to perceive a Transform architecture, and a multi-head self-attention and nonlinear feed-forward network is combined, so that the width error of a QRS wave group is smaller.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention discloses a comprehensive evaluation method for drought resistance of white birch germplasm resources in the seedling stage, and belongs to the technical field of drought resistance evaluation. Under different gradient drought stress and normal conditions, 14 drought resistance indexes of 90 white birch half-sib families are measured, and the drought resistance is comprehensively evaluated by combining membership function analysis, principal component analysis and clustering analysis. A comprehensive drought resistance evaluation value (D value) is obtained according to a membership function, comprehensive clustering analysis is carried out according to the D value of each family, 90 families are divided into four categories at the Euclidean distance of 1.5, and BWY32 and BWY29 are screened out as drought-resistant families. A stepwise linear regression analysis method is utilized to respectively establish linear regression equations under drought stress 5d and 10d, and four important drought resistance indexes are screened out. Compared with the previous research, the method fully considers the difference of the sensitivity degree of each selected index to drought stress and the contribution rate of each index to drought resistance evaluation, and lays a good foundation for simplification and high efficiency of the drought resistance evaluation of the white birch.

The invention discloses a collaborative planning method for a regional integrated energy system, and the method comprises the steps: firstly collecting regional historical energy consumption data and various external factor data, and predicting an energy demand through employing a gray prediction and multiple linear regression combined algorithm; exploring existing energy supply facilities in the region and evaluating renewable energy potential; determining a candidate type of the integrated energy system component according to the result, establishing an optimal configuration model, and solving through a particle swarm optimization algorithm; constructing an energy network layout planning model, and determining an optimal topological structure and parameters of the network; establishing a dynamic operation model, and formulating a collaborative operation strategy by using a model prediction control algorithm; and finally, comprehensively evaluating the planning scheme, and analyzing and adjusting the scheme by utilizing sensitivity according to an evaluation result. According to the method, multiple factors are comprehensively considered, and the problems that in existing regional integrated energy system planning, demand prediction is not accurate, resource evaluation is not comprehensive, component configuration and network layout lack optimization collaboration, and the operation strategy is single are effectively solved.

The invention relates to the technical field of grounding resistance detection, in particular to a power transmission line tower grounding resistance monitoring system and a use method, and the system comprises a signal collection and correction module which collects and updates various parameters of each tower in real time, builds a mathematical model of environment parameters and grounding resistance values, and obtains the real-time environment parameters and the grounding resistance values after obtaining the real-time environment parameters; automatically correcting the measured resistance value by using the mathematical model; the fault early warning module is used for collecting historical monitoring data and calculating an abnormal risk probability in combination with real-time abnormal data; the fault positioning module is used for realizing accurate positioning of a fault tower through a time difference positioning method; and the remote control module is linked with the meteorological early warning platform, starts high-frequency monitoring before thunderstorm, and dynamically adjusts a ground resistance threshold value. The measurement value is automatically corrected through the linear regression model, interference of environmental fluctuation on resistance measurement can be effectively eliminated, and errors of traditional static measurement are reduced.

The invention discloses a hyperspectral technology-based inversion method for SPAD values and nitrogen concentrations of leaves in the whole growth period of rice. The hyperspectral data of the leaves of the rice are obtained by means of an ASD Field Spec4 portable ground object spectrometer in the initial differentiation period (PI), the heading period (HD), the middle filling period (MF) and the mature period (MA) of young ears; measuring the SPAD value of the rice as the chlorophyll content by using an SPAD-502 chlorophyll meter; determining the nitrogen concentration of the rice leaves by adopting a Kjeldahl method; performing correlation analysis on the hyperspectral data of the leaves, the nitrogen concentration and SPAD, and screening sensitive wavebands. According to the method, the hyperspectral characteristic wave band of the rice leaf is extracted through a correlation analysis method, so that the redundancy of full-spectrum data modeling is reduced, data are optimized, an inversion model of the nitrogen concentration and SPAD value of the rice leaf is constructed based on linear regression (LR), random forest (RF), support vector regression (SVR) and gradient boosting (GBRT), and the nitrogen concentration and SPAD value of the rice leaf are obtained. The optimal machine learning algorithm with high precision and good stability is obtained, and a scientific basis can be provided for non-destructive real-time leaf color monitoring and precise fertilization control of indica rice in the South China.