71 results about "Polynomial regression" patented technology

The invention relates to the field of data processing, in particular to an intelligent deviation correction method and system for strip steel tailoring, and the method comprises the steps: firstly obtaining and smoothing deviation contour data of strip steel in real time; then, by calculating the local variation energy of the second derivative of the deviation profile, identifying the region with the form having significant change, and further calculating the feature scale of the region; then, dynamically generating a self-adaptive analysis window according to the feature scale of the macroscopic deviation; and finally, utilizing low-order polynomial regression in the window to accurately decouple the complex deviation form into basic parameters such as translation, inclination, C-shaped bending and S-shaped bending, and transmitting the basic parameters to a controller so as to realize rapid and accurate differential deviation correction. According to the method, the macroscopic deviation is identified by calculating the local variation energy and the feature scale, the adaptive analysis window is constructed, and the deviation form is decoupled by using local polynomial regression, so that accurate control is realized.

The invention provides a transformer anti-short circuit capability quantitative evaluation method and system based on multi-physics field simulation mapping, and belongs to the technical field of transformer anti-short circuit capability evaluation. Comprising the following steps: obtaining structure parameters of a to-be-tested type transformer, and constructing a multi-physics field coupling simulation model; running a preset defect working condition by using the multi-physics field coupling simulation model, and calibrating an anti-short-circuit capability limit value corresponding to the defect working condition according to the running data; applying a unit pulse voltage excitation signal, and obtaining a feature vector corresponding to the defect working condition based on the frequency domain response curve; analyzing the mapping relation by using a polynomial regression algorithm to obtain an anti-short-circuit capability quantitative evaluation function; and in a power failure off-line state of the transformer, injecting a unit pulse voltage excitation signal into the winding by using a pulse frequency response tester, calculating an anti-short-circuit capability limit value corresponding to an actually measured operating temperature value according to the anti-short-circuit capability quantitative evaluation function, and evaluating the anti-short-circuit capability of the transformer in combination with a rated thermally stable current.

The invention belongs to the technical field of power system and equipment management, and provides a system and a method for evaluating the life extension of an immediate electric energy meter based on anomaly evaluation, and the system comprises a data collection module which is used for collecting and obtaining the power utilization data of a transformer area group and the work operation data of the immediate electric energy meter; the batch division management module is used for carrying out batch division and management on the immediate electric energy meters based on the work operation data in combination with the immediate electric energy meter equipment parameter data; the anomaly analysis module is used for carrying out anomaly analysis on the electric energy meters which are divided in batches and are in time on the basis of an anomaly detection algorithm in combination with the working operation data to obtain an anomaly analysis result; and the life extension evaluation module is used for calculating a comprehensive life extension score by using a polynomial regression function according to the abnormal analysis result and the marketing data of the electric energy meter, and obtaining life extension evaluation results of the plurality of batches of immediate electric energy meters according to the comprehensive life extension score. According to the invention, online intelligent diagnosis, batch intelligent division, batch abnormity monitoring and batch quality evaluation can be realized.

A computer device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: a) receive, from the image capture device, a plurality of images for a continuous scan phase shift interferometry (PSI), wherein each image of the plurality of images includes a first plurality of pixels of a first item and a second plurality of pixels of a partial cavity; b) perform intensity scans of each pixel in the second plurality of pixels; c) identify zero transitions for each of the second plurality of pixels; and d) statistically evaluate the identified zero transitions by polynomial regression analysis.

The invention discloses an automatic raw material feeding control system for PVC plastic tile production, and relates to the technical field of building material manufacturing, and the system comprises a raw material identification module, a PLC central control unit configured on a production line, a bar code and RFID technology for identifying different types of raw materials and obtaining material type information, a preprocessing module, and a control module for controlling the automatic feeding of raw materials based on the material type information. And the weighing module is used for conveying the pretreated raw materials to a weighing assembly at the bottom of the feeding barrel through an automatic conveying belt and an elevator, weighing treatment is carried out, and the weight change trend of the raw materials is obtained. According to the invention, by integrating the dual identification technology of the bar code scanner and the RFID reader-writer, the identification precision is greatly improved, various types of raw materials can be effectively processed, and the adaptability and efficiency of production are improved. A nonlinear mathematical model constructed on the basis of polynomial regression in combination with a neural network is adopted, and a multi-target particle swarm algorithm is matched to perform mixing optimization, so that accurate control of the raw material flow velocity is realized.

In order to explore the effectiveness of the functional data analysis method in the hyperspectral image processing, the application proposes a classification method combining the Gaussian regression mixture model and the MRF hyperspectral function data; first, the polynomial regression is used to fit the hyperspectral image pixel spectrum curve, so as to express the pixel spectrum information in the form of function; then, the neighborhood relationship is introduced to establish the Markov random field model, and the neighborhood Gaussian regression mixture model is established in combination with the Gaussian regression mixture model; finally, according to the maximum posterior probability criterion, the final hyperspectral image classification result is obtained. Since the spatial-spectral information of the hyperspectral image is fully combined, the algorithm has high-precision classification result, and effectively improves the classification performance of the hyperspectral image.

The invention relates to the technical field of power system dynamic safety analysis, and discloses a mechanism-data fusion new energy power system minimum inertia demand rapid evaluation method, which comprises the following steps: in an offline stage, establishing an expansion system frequency response model containing new energy; generating a plurality of sample scenes in a given disturbance power range and a new energy permeability range; a model minimum inertia demand and a simulation minimum inertia demand are obtained through expansion of a system frequency response model and stepping type time domain simulation calculation, a polynomial regression method and a polynomial regression equation are utilized, and the coefficient and order of the polynomial regression equation are determined by adopting an optimization algorithm; during online evaluation, real-time disturbance power and new energy permeability of a power grid are obtained; and obtaining a model minimum inertia demand and a corresponding minimum inertia demand deviation compensation value, and outputting a final system minimum inertia demand evaluation result. According to the method, the final evaluation result has the calculation efficiency of the extended SFR model and the evaluation precision of stepping simulation.

The application relates to the technical field of fire rescue equipment monitoring, and discloses a firefighter air respirator residual amount prediction method and system based on dynamic pressure compensation. The method comprises the following steps: acquiring historical consumption time data of firefighters, using polynomial regression fitting to establish a theoretical pressure consumption rate function corresponding to an individual and establishing a theoretical prediction logic; receiving a combat organization instruction to obtain an initial full load pressure, starting timing and calculating and displaying a current theoretical prediction residual pressure; capturing an artificial interactive correction instruction, extracting a correction time node and an actual residual pressure, and solving to generate a dynamic compensation coefficient; applying the dynamic compensation coefficient to reconstruct and solve an actual prediction residual pressure and control and update display; comparing the actual prediction residual pressure with an alarm critical pressure value to trigger an alarm, and extracting actual combat data to iteratively update the theoretical function after the task is completed. The application realizes individualized benchmark prediction of air consumption and dynamic adaptive error correction of the working environment, and improves residual amount early warning accuracy.

The invention relates to the technical field of battery monitoring, in particular to a battery SOC (state of charge) estimation method and system based on whole-process monitoring, and the method comprises the steps: collecting terminal voltage, temperature, current and timestamp under constant-current discharge, and reading a relation curve of nominal capacity and open-circuit voltage state of charge; interpolating the discrete curve generation voltage to SOC, and obtaining an initial SOC according to the voltage at the first moment; calculating the sampling interval hour and the interval charge quantity, subtracting the percentage of the SOC accounting for the nominal capacity at the previous moment to obtain coulomb metering SOC, averaging the coulomb metering SOC and the interpolation SOC to obtain a reference SOC, and outputting a label; carrying out db1 wavelet decomposition and hard threshold processing on the voltage and the temperature, then carrying out inverse transformation to obtain a de-noising sequence, and calculating a signal-to-noise ratio according to a power ratio; polynomial regression or tree integration regression is trained through the four-dimensional features; and the evaluation index is combined with a signal-to-noise ratio comparison threshold value and a model combination, and the output of the target SOC estimator is solidified to estimate the SOC. The method can solve the problems that existing SOC estimation is highly dependent on standing conditions, coulomb metering errors are accumulated, noise interference is large and the like.

The application discloses an online roll temperature and thermal crown prediction method based on finite difference and polynomial regression, aiming at the technical pain point that the accuracy of the traditional analytical model is insufficient and numerical simulation cannot meet the online real-time calculation demand, a plurality of groups of working conditions are designed through an orthogonal experiment method, and a finite difference method is used to calculate roll steady-state temperature field data to construct a training data set, based on the data set, a polynomial regression algorithm with elastic network regularization is used to fit an axial temperature distribution general model with the roll temperature field as an input variable, after obtaining the online surface axial temperature distribution of the roll, the general model is substituted into the internal temperature field to restore the internal temperature field and calculate the radial thermal expansion amount at each position, so that compensation information is provided for a shape control system of a rolling mill. The application simplifies complex physical field simulation into lightweight algebraic formula operation, retains the high analytical accuracy of the finite difference method, realizes real-time prediction, and can significantly improve the rolling quality of silicon steel products.

The invention relates to the field of strain detection, in particular to a self-adaptive strain detection method and system and a medium. The method comprises the following steps: inputting grid point coordinates, noisy displacement data and a maximum smooth half window; calculating the overall noise level sigma through local linear regression analysis; extracting local strain curvature characteristic quantity through cubic polynomial regression; establishing a noise and curvature balance relation based on the sigma and the curvature characteristic quantity, and calculating an adaptive smooth half window; obtaining the strain estimation value of each grid point through linear polynomial regression, and finally obtaining the strain distribution of the whole field. According to the method, the optimal smooth window can be automatically adjusted without manual intervention, balance is achieved between noise suppression and detail reservation, the problem that a traditional fixed window method is insufficient in precision in a non-uniform deformation area is solved, high-precision full-field strain detection of a noise-containing displacement field is achieved, and the detection precision is improved. The method is suitable for derivative calculation of digital image related systems and various noisy continuous signals.

The application relates to the technical field of high-performance computing, and particularly discloses a method for predicting the performance of an application program and a computing device. The method comprises the following steps: constructing a performance characteristic data set based on the running parameters of an application program in each job of a cluster system; determining similar characteristic data from the performance characteristic data set based on the running parameters of the application program on a single computing node of the cluster system; classifying the similar characteristic data according to the number of computing nodes, generating simulation characteristic data by using an application performance prediction model based on the number of similar characteristic data in each classification; generating an application data set by using the similar characteristic data and the simulation characteristic data; fitting the relationship between the number of computing nodes and the running time by using a polynomial regression algorithm based on the application data set; and predicting the running time of the application program on each computing node of the cluster system by using the fitted relationship. According to the application, accurate computing power use selection suggestions can be provided for the application program.

The present application relates to the field of strain detection, and particularly to a self-adaptive strain detection method, system and medium. The method comprises: inputting grid point coordinates, noisy displacement data and a maximum smoothing half window; calculating an overall noise level sigma through local linear regression analysis; extracting a local strain curvature characteristic quantity through cubic polynomial regression; establishing a noise and curvature balance relationship based on sigma and the curvature characteristic quantity, and calculating an adaptive smoothing half window; obtaining strain estimation values of each grid point through linear polynomial regression, and finally obtaining overall strain distribution. The method can automatically adjust the optimal smoothing window without manual intervention, balances between noise suppression and detail preservation, solves the problem of insufficient accuracy of traditional fixed window methods in non-uniform deformation areas, realizes high-precision full-field strain detection of noisy displacement fields, and is suitable for derivative calculation of digital image correlation systems and various noisy continuous signals.

The application discloses a digital elevation model elevation error correction method based on a particle swarm optimization random forest, and belongs to the technical field of remote sensing. The particle swarm optimization random forest method is used for correction, so that the precision of the corrected digital elevation model is further improved. SRTM is selected as the digital elevation model used in the test, ICESat-2 strong light beam ground surface photon data is used as the reference elevation control point used in the test, Globeland30 is used as the global ground surface cover data used in the test, airborne LIDAR DTM published by NEON is used as the verification data used in the test. The method proposed in the application and the correction method based on polynomial regression are tested, the root mean square error is used as the verification index, the elevation error of SRTM can be effectively reduced, the elevation error of the corrected SRTM is reduced by 42% to 46% compared with the elevation error before correction, and the correction precision is better than that of the correction method based on polynomial regression.

The invention belongs to the technical field of simulation optimization, and particularly relates to a centrifugal pump impeller multi-physics field coupling simulation optimization method which comprises the following steps: constructing a geometric model of a centrifugal pump by adopting three-dimensional drawing software according to an actual structure of the centrifugal pump; finite element modeling is conducted, and a finite element analysis model of the centrifugal pump is obtained; hexahedral grids are divided for the impeller, a shape variable space of the impeller is built, and the impeller deformation serves as a design variable; solving the finite element analysis model after grid division through a solver; an experimental design sample is generated by adopting a Latin hypercube sampling method, a global response surface model is constructed through quadratic polynomial regression fitting, an optimal impeller deformation amount is iteratively solved by adopting a global response surface optimization algorithm, a finite element analysis model is adjusted, and an optimization model is formed. According to the method, fluid-solid coupling simulation, a global response surface method and an automatic iteration process are fused, and the efficiency of the centrifugal pump impeller is maximized and optimized on the basis of accurately matching actual working conditions.

The embodiment of the invention discloses an on-line detection method and device for the loss of a distribution transformer, equipment and a storage medium. The method comprises the steps that the current load rate and the current load current of the transformer are acquired; determining a target order total loss model according to the current load rate and a preset corresponding relation between the load rate and the order of the total loss model; and determining the current total loss of the transformer by using the current load current and the target order total loss model. The polynomial function relation between the loss and the load current is obtained through theoretical derivation, and the loss of the transformer is calculated through the polynomial regression algorithm. In order to compensate the influence of the load on loss calculation, a polynomial dynamic order adjustment mechanism is introduced, and the loss calculation accuracy is further improved. Meanwhile, on-line loss detection only needs to detect primary and secondary side voltage and current effective values during normal operation of the transformer through a sensor, power failure is not needed, power supply reliability is greatly improved, and waste of manpower and material resources is avoided.

The invention relates to the field of air transportation, in particular to a reasonable flight combination scheme filter based on a travel-distance polynomial regression relationship, which comprises the following steps: S1, acquiring historical sales data from an open market; step S2, cleaning and preprocessing the collected data; s3, utilizing a polynomial regression method to fit the relationship between the total flight travel and the linear distance; s4, in the route combination process of the virtual intermodal transport, calculating the linear distance between a given departure place and a given destination; s5, the flight combination scheme with the total journey exceeding the flight total journey range is regarded as unreasonable and filtered out. The method is completely based on objective market data, and limitation of subjective judgment and artificial experience is avoided; the automatic filtering and optimization of the flight combination scheme are realized, and the intelligent level of the virtual combined transport system is improved; the method does not depend on specific airlines or airlines, and has high universality and expandability.

The invention discloses a high-speed railway RCF crack size characterization method based on an ACFM technology By signal. The method comprises the steps that a finite element model of a high-speed railway steel rail is established; adopting a control variable method, taking one of the crack length, the crack pocket depth and the crack inclination angle as a variable and taking the other two of the crack length, the crack pocket depth and the crack inclination angle as a fixed quantity, carrying out ACFM detection simulation, detecting the crack to obtain a By signal, and extracting characteristic parameters of the By signal; obtaining a quantitative relationship between the By signal characteristic parameter and the crack geometric parameter; based on the quantitative relationship, establishing a By signal-crack size mapping model through a polynomial regression algorithm or a support vector regression algorithm; inputting a to-be-measured By signal into the By signal-crack size mapping model, and outputting three quantitative results of the crack length, the crack pocket depth and the crack inclination angle. The method not only provides a theoretical basis for developing a more comprehensive ACFM quantization algorithm, but also lays a technical foundation for online intelligent monitoring and maintenance of the high-speed railway steel rail.

The present application provides a kind of carbon emission trend prediction method based on polynomial regression and neural network, comprising: S11, respectively constructs the carbon emission model and flow chart model of whole life cycle;S12, data cleaning and pretreatment are carried out to carbon emission model and flow chart model, and high-quality data are output;S13, high-quality data are updated by dynamic updating mechanism;S14, a carbon emission prediction model is established using polynomial regression analysis method, carbon emission data are input into the carbon emission prediction model, and carbon emission trend prediction result is output.The present application can improve the accuracy and timeliness of high-carbon emission data, shorten the evaluation period, reduce the cost, and provide a strong basis for environmental regulation and protection decision.

The present application relates to the technical field of ecological hydrology and water resources management, and discloses a method for determining suitable area of lake based on ecological water use efficiency, comprising: obtaining multi-source remote sensing monitoring data of target lake area in historical years, extracting lake area, normalized vegetation index, vegetation area and land use type area in each year; calculating ecological environment quality index based on land use type area; calculating ecological water use efficiency; drawing a scatter plot of lake area and ecological water use efficiency in multiple years, conducting quadratic polynomial regression fitting, and constructing a response function between lake area and ecological water use efficiency; determining the optimal lake suitable area threshold by using the first derivative extreme value method, calculating the maximum ecological water use efficiency value, and determining the lake suitable area interval to implement lake ecological management; the present application realizes quantitative evaluation of ecological benefits of lakes in arid regions and accurate determination of suitable area.

The invention discloses a powder grinding multi-target cooperative control method and system, and belongs to the field of industrial process automation control, and the method comprises the steps: extracting powder concentrator load and mill main motor power time sequence data, separating internal model components based on variational mode decomposition, and combining with residence time distribution to generate a working condition quality mapping matrix; performing least square polynomial regression and numerical control comparison on the working condition quality mapping matrix to construct candidate control parameters; calculating an internal model component instantaneous frequency variance to construct a dynamic weight, selecting a candidate vector with the minimum weighted sum, and executing amplitude truncation to generate a control instruction; collecting new data to calculate a prediction residual vector, and executing least square iteration update to generate a correction regression equation; and eliminating abnormal lines of the working condition quality mapping matrix by utilizing a quantile boundary on the prediction residual error sequence, adding new data and executing secondary calibration. According to the method, data-driven closed-loop control is adopted, and the adaptive capacity and control precision of the model to nonlinear working conditions can be improved.

The invention provides an acquisition terminal clock precision calibration method and device and a medium, and relates to the technical field of clock calibration, and the method comprises the following steps: obtaining terminal temperature data and clock error data at each temperature; through a DBSCAN clustering algorithm, noise points are calculated, recognized and removed based on the optimized Manhattan distance, and an effective clock error value corresponding to each temperature is obtained; the cloud server trains a polynomial regression type clock error prediction model and a calibration value prediction model in stages, converts the models into rknn formats and sends the rknn formats to the terminal; and the terminal predicts a clock error and a corresponding calibration value based on the current temperature through NPU acceleration model reasoning so as to calibrate the clock chip. According to the invention, temperature change can be responded in real time, the clock calibration value matched with the current temperature is dynamically output, and the clock calibration precision is effectively improved.

The application discloses a metal dust explosion risk comprehensive index early warning method and system, and particularly relates to the technical field of metal dust; the real-time dust environment data collected on the operation site and the standard dust data are standardized, a feature vector is generated and similarity is calculated, and a potential abnormal state is identified; further, a multi-dimensional risk assessment model is constructed by extracting processing temperature deviation features and charged concentration fluctuation features, a metal dust explosion risk comprehensive index is calculated by using a polynomial regression algorithm, and is compared with a gradient risk threshold, so that multi-level early warning signal triggering and linkage response control are realized; the application has self-adaptive identification capability for new or unknown dust states, significantly improves the accuracy and timeliness of risk early warning, and enhances the intrinsic safety guarantee capability of the system under complex working conditions.

The invention discloses a quick evaluation method and system for the crispness of sauced cucumbers based on hyperspectral imaging, and the method comprises the steps: describing the sensory crispness score of to-be-detected sauced cucumbers, and collecting the texture indexes of the to-be-detected sauced cucumbers; modeling indexes are determined as hardness and elasticity through correlation analysis, and a quadratic polynomial regression equation among the brittleness, the hardness and the elasticity is established; for each index, acquiring spectral data of the sauced cucumber to be detected through a hyperspectral imaging method, performing preprocessing and characteristic wavelength screening on the spectral data, and then establishing a machine learning prediction model; and according to a hardness prediction value and an elasticity prediction value obtained by prediction of the machine learning model, inputting the hardness prediction value and the elasticity prediction value into the quadratic polynomial regression equation to obtain a brittleness evaluation result. Compared with a traditional sensory evaluation method, an instrument analysis method and other existing methods, the method has the advantages of high efficiency, safety, no damage, cost saving and the like.

The invention discloses a method for forecasting the ultimate strength of a multi-layer egg-shaped pressure-resistant shell. The method comprises the following steps: multiplying a plastic attenuation factor, a defect attenuation factor and a linear buckling formula to establish an ultimate strength forecasting formula of the multi-layer egg-shaped pressure-resistant shell; determining the relationship between the ultimate strength of the multi-layer egg-shaped pressure-resistant shell and the linear buckling load, the plastic attenuation factor and the defect attenuation factor; determining influence factors and value ranges of the plastic attenuation factor and the defect attenuation factor, and designing a response surface test; establishing a three-dimensional finite element model of the multi-layer egg-shaped pressure-resistant shell; fitting based on finite element numerical simulation data to obtain a polynomial regression equation of a plastic attenuation factor and a defect attenuation factor; designing an orthogonal test according to the influence factors of the plastic attenuation factor and the defect attenuation factor, and verifying an ultimate strength forecasting formula; and calculating the ultimate strength of the multi-layer egg-shaped pressure-resistant shell according to actually measured geometry, materials and defect parameters. According to the method, the ultimate strength of the multi-layer egg-shaped pressure-resistant shell can be efficiently and accurately predicted.

The invention discloses a thermal power plant auxiliary machine equipment health state monitoring method and system based on polynomial regression, and relates to the technical field of signal data integration technology and equipment state monitoring, and the method comprises the steps: carrying out the data collection of auxiliary machine equipment based on an Internet of Things platform, mining equipment core characteristic parameters through employing a multiple linear regression algorithm, and carrying out the monitoring of the health state of the auxiliary machine equipment; constructing a typical operation characteristic parameter set; the collected data are preprocessed, an auxiliary machine equipment health state monitoring AI model is constructed based on a polynomial regression algorithm, and the health degree is evaluated; real-time data are input into an AI model for prediction, an AI + mechanism early warning model is built through a logical operation assembly and mechanism modeling, and state judgment and graded early warning are achieved. According to the method, the abnormal state of the auxiliary machine equipment can be timely and accurately detected and dealt with, the safety and stability of the power production process are improved, the engineering practicability is high, the response is faster, the implementation cost is remarkably reduced, and the labor intensity and the working pressure are relieved.

The application discloses a kind of powder grinding multi-objective collaborative control method and system, belong to industrial process automation control field, it includes extracting powder concentrator load and mill main motor power time series data, based on variation mode decomposition separates internal mode component and generates working condition quality mapping matrix in combination with residence time distribution;Least square polynomial regression and numerical domination comparison are executed to working condition quality mapping matrix to construct candidate control parameter;The instantaneous frequency variance of internal mode component is calculated to construct dynamic weight, select the candidate vector of minimum weighted sum and execute amplitude truncation to generate control instruction;New data is collected to calculate prediction residual vector, and least square iteration update is executed to generate revised regression equation;The abnormal row of working condition quality mapping matrix is removed using the upper quantile boundary of prediction residual sequence, new data is added and secondary calibration is executed.The application uses data-driven closed-loop control, and the adaptability and control precision of model to nonlinear working condition can be improved.

The invention relates to a vision measurement technology, and discloses a basketball projection detection method, equipment system and storage medium, and the method comprises the steps: obtaining an RGB video stream and a depth map in a process that a basketball flies towards a basket based on a depth camera; carrying out basketball target detection according to the RGB video stream, and aligning the depth map with a corresponding image frame in the RGB video stream; based on the depth map, obtaining point cloud data of a basketball target in each image frame; fitting the point cloud data of the basketball target in each image frame to generate a three-dimensional model of a sphere, and calculating the coordinates of the center of sphere; fitting the center coordinates of the continuous image frames to generate a basketball flight path; and based on the basketball flight path after coordinate conversion, combining polynomial regression and extended Kalman filtering to predict a projection result. According to the method, the falling point coordinates and the incident angle of the basketball are accurately calculated, more detailed and accurate shooting result information is provided, and the requirement for accurate analysis of basketball shooting is met.

The application provides a self-adaptive radar signal sorting method and system based on an RFSoC heterogeneous platform. The method works in cooperation with a PS end and a PL end. First, a normalization constant is calculated based on input PDW data, and parallel normalization is performed. Subsequently, a three-part search is used to determine the most dominant energy field radiation factor online, false alarm pulses are removed, and an index mapping is established. The effective pulses are re-normalized, the pulse pair Euclidean distance matrix is calculated, and the nearest neighbor sorting is performed, and then the local density neighborhood scale is determined. The PS end calculates the local density and the relative distance, generates the decision graph basic data, and automatically selects the clustering center through a second-order polynomial regression. Based on the clustering center, pulse assignment and cluster merging are performed, and finally the sorting result of the original batch is output through the index mapping. The application has the characteristics of heterogeneous architecture cooperative design and algorithm hardware friendliness, realizes self-adaptive sorting, and maintains the operation precision.

This invention relates to the field of short-term meteorological climate prediction, specifically disclosing a seasonal adaptive integration and dynamic anomaly correction temperature prediction system and method. The system includes a data acquisition module, a modeling and calculation module, and an evaluation and visualization module. The method includes: S1, fusing multi-source data to construct time-coded, lag, and cross-feature features, and generating a dynamic climate benchmark with variable weights; S2, based on the Stacking integration framework, using Ridge+LightGBM in winter, SVR and multinomial regression in summer, weighting during the transition season, and residual calibration in winter; S3, three-level anomaly evaluation, combined with benchmark calculation and visualization, with fine-tuning when the matching rate is low. The seasonal adaptive integration and dynamic anomaly correction temperature prediction system and method proposed in this invention solves the problems of difficulty in nonlinear capture, benchmark rigidity, and poor seasonal adaptation, effectively improving the accuracy of seasonal temperature prediction.