141 results about "Parameter learning" patented technology

The invention discloses a watershed water resource supply and demand risk prediction and evaluation method based on a multilevel Bayesian causal network, and relates to the technical field of water resource supply and demand risk management.The watershed water resource supply and demand risk prediction and evaluation method comprises the steps that a water resource supply and demand risk diagnosis knowledge graph is constructed according to key variables and interrelations input by a user; constructing a multi-level Bayesian causal network structure; estimating conditional probability distribution among the nodes, and performing parameter learning and structure training on the Bayesian causal network; carrying out risk path identification through a reverse Bayesian reasoning method; outputting a posterior probability of water resource supply and demand risk prediction; based on a preset fuzzy character string matching algorithm, typical risk events and risk features are extracted; and according to the posterior probability and the risk characteristics, comprehensively evaluating the water resource supply and demand risk level. The method can improve the systematicness and scientificity of risk identification, is suitable for multi-link and multi-scale risk assessment and scheme comparison and selection in a complex drainage basin, and has high practical value and popularization prospect.

The invention discloses a method and system for predicting juvenile depression based on intestinal flora, and relates to the technical field of bioinformatics and artificial intelligence, and the method comprises the steps: firstly, obtaining an original sequence of a microbiome, carrying out the preprocessing of the original sequence of the microbiome, and obtaining a feature matrix; and screening core flora characteristics with stable trans-folding by adopting characteristic importance evaluation and interpretability analysis based on a gradient boosting decision tree. A mixed weighted graph is constructed based on Spearman correlation and a proximity relationship, and an absolute value of a correlation coefficient is taken as an edge weight and an edge density is adjusted through a threshold adaptive strategy. And finally, through an improved graph attention neural network, based on edge weight attention, layer normalization and random inactivation, enhancing robustness, and adopting adaptive optimization to complete parameter learning. And determining a dynamic classification threshold according to the AUC of the target patient, and outputting a sample discrimination result and confidence. According to the method, the accuracy, stability and biological interpretability of juvenile depression recognition are remarkably improved.

The invention provides a sewage treatment water quality parameter real-time detection system based on deep learning, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting multi-source dynamic data in a sewage treatment process in real time through a distributed sensor network; the feature reconstruction module is used for performing feature space reconstruction on the multi-source dynamic data and generating dynamic correction parameters through time sequence correlation analysis; and the learning prediction module is used for inputting the dynamic correction parameters into a pre-trained multi-task deep learning model, analyzing and evaluating the influence degree of each feature variable through the contribution degree of the parameters, and dynamically adjusting feature importance distribution by adopting a self-adaptive weighting mechanism so as to obtain adjusted feature representation. According to the invention, real-time accurate detection of water quality parameters, timely early warning of standard exceeding risks, energy consumption optimization in a sewage treatment process and stable control of effluent quality are realized.

The invention discloses a scanning radar super-resolution imaging method based on data-driven adaptive parameter learning, which is applied to the technical field of radar imaging and aims to overcome the defects that a traditional super-resolution method is high in parameter sensitivity and an existing network expansion method has a redundant structure and poor adaptability. The method comprises the following steps: firstly, converting a non-differentiable L1 regularization problem into a differentiable L2 problem based on a maximum-minimum principle; secondly, expanding an iteration process into a multi-stage cascade neural network, and performing adaptive learning on regularization parameters in a data driving mode; and finally, layered parameter optimization and super-resolution reconstruction are realized through an end-to-end network architecture. By adopting the method of the invention, the super-resolution imaging performance of the radar is effectively improved.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention discloses a blurred image NeRF modeling method, system and device based on a scattering light path model and a medium. The modeling method comprises the steps of blurred image initial pose calculation, camera pose interpolation, neural radiation field NeRF-based 3D scene modeling, scattering light parameter learning guided by an internal scattering light path model, blurred image synthesis prediction by using a scattering sensing volume rendering method, and parameter joint optimization based on blurred image luminosity loss. The system, the equipment and the medium are used for implementing the method. A light propagation phenomenon is represented as internal scattering of light at a medium or surface intersection point through an internal scattering light path model, a scattering light path direction and a sampling point distance are autonomously learned by means of a feedforward neural network, and image rendering is performed by integrating contributions of sampling points in the directions of a main light path and the scattering light path. The real imaging process of a blurred image in a complex illumination environment can be effectively simulated, the geometric ambiguity problem is effectively avoided, the convergence stability of the neural network is improved, and a finer reduction effect on geometric details of a fine object is achieved.

The invention provides a loudspeaker mask defect identification method and system based on visual inspection, and relates to the technical field of machine visual inspection, and the method comprises the steps: collecting a surface image of a loudspeaker mask through an industrial camera, calculating a median gray value in a pixel neighborhood to effectively filter out impulse noise, carrying out size standardization processing, and obtaining an image of the surface of the loudspeaker mask; a standardized image is obtained; according to the standardized image, a convolutional neural network is adopted, and a parameter learning rate is dynamically adjusted through an adaptive moment estimation mechanism, so that multi-scale feature mapping of mask textures is extracted; according to the multi-scale feature map, candidate defect regions are generated through a region suggestion network; and for a candidate defect area proposal, using a U-Net network and a learning rate to quickly approach an optimal solution, attenuating the learning rate according to an exponential law along with the increase of the number of iterations to stably converge to fine local optimum, and performing pixel-level fine segmentation on a defect boundary to obtain a defect mask image. According to the invention, the detection efficiency of the loudspeaker mask is improved.

The invention discloses a light-weight cabin efficient self-adaption machining method based on cutting parameter learning, solves the cutting parameter self-adaption problem in light-weight cabin machining, and belongs to the technical field of spacecraft structural part manufacturing or numerical control self-adaption machining. The method aims at improving the machining quality and the machining efficiency, minimizing the cutting force and maximizing the material removal rate, a mathematical model of two targets and cutting parameters is established, an orthogonal process test is designed to analyze the response characteristics of the two targets to the cutting parameters, the influence of the cutting parameters to the cutting force in the actual cutting process is analyzed, and the machining precision is improved. Optimized operation data and a tool database are formed; and learning of adaptive parameters is realized by applying an ACM adaptive optimization technology, and adaptive processing is carried out.

An apparatus of a vehicle comprises a memory storing at least one instruction and a processor configured to execute the at least one instruction. The at least one instruction may be configured to cause, when executed by the processor, the apparatus to: via a tuning parameter learning model for image correction, generate, based on received video data, a tuning parameter for adjusting image signal processing (ISP) for correcting the received video data; correct, based on the tuning parameter, the received video data; identify, via a video recognition model, at least one object in at least one image corresponding to the corrected video data; and control, based on the identified at least one object, autonomous driving of the vehicle.

The invention relates to the technical field of power grid reactive voltage operation control, in particular to a power grid reactive voltage strategy regulation effect tracing reasoning method, device and equipment and a medium, and the method comprises the steps: constructing a regulation effect quantitative evaluation index system from safety, economy and schedulability; comprehensive weighting with subjective and objective combination is adopted, and key index weights are corrected; comprehensively evaluating each level of indexes of the typical day by using an ideal approach solution to form an operation state result; extracting an abnormal index based on a result, and constructing an associated factor set in combination with a regulation and control strategy and operation characteristics; screening strong correlation factors through correlation and causality analysis; k mean discretization is carried out on the time sequence of the abnormal indexes and the time sequence of the abnormal indexes, and Bayesian network parameter learning is completed by adopting maximum likelihood estimation; key causes are identified based on network reverse reasoning, and the method can accurately evaluate the regulation and control effect, locate the effect key factors and provide decision support for power grid dispatching optimization.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention discloses a radar interference effect evaluation method based on a constraint learning dynamic Bayesian network, is applied to the field of radar interference evaluation, and aims at solving the problem that the accuracy of interference effect evaluation is reduced due to radar detection data missing in a complex electromagnetic environment. Meanwhile, parameter constraints of five types of evaluation indexes and interference effect grades are defined; secondly, constructing a constraint learning dynamic Bayesian network, and learning a conditional probability and a transition probability under a data missing condition; then, proposing a prior constraint expectation maximization algorithm, converting parameter learning into an optimization problem with constraint by combining convex optimization, and overcoming the defects of a traditional expectation maximization algorithm; secondly, a cloud model is introduced to quantify discrete probability distribution into a continuous interference degree value; finally, simulation shows that the method can effectively improve parameter learning stability and evaluation accuracy under the conditions of suppressing and deception jamming and single index deficiency, and provides a reliable scheme for radar jamming effect evaluation in a complex environment.

The invention discloses a Raman spectrum noise reduction method based on an MSC-U-Net algorithm, and the method comprises the steps: S1, measuring Raman spectrums of a plurality of substances with different concentrations under the same condition, and respectively measuring a plurality of spectrums for each concentration of each substance, so as to obtain an original spectrum data set containing noise; s2, performing normalization preprocessing on all the collected spectrums, dividing a data set into a training set, a verification set and a test set, and constructing a data set required by a noise reduction model; s3, averaging the collected original spectrums according to a plurality of spectrums of the same substance and the same concentration to obtain ideal spectrums without noise; and S4, constructing a Transform-Attention U-Net model, inputting the training set into the model to carry out parameter learning, and carrying out iterative optimization according to a loss function to obtain a spectral noise reduction model. According to the method, noise signals in the Raman spectrum can be effectively removed, meanwhile, effective signals are not lost, and the signal-to-noise ratio of the Raman spectrum is increased.

The invention provides a radar signal small sample modulation identification method and system based on meta-learning, and relates to the technical field of radar signal modulation identification, and the method comprises the steps: collecting a multi-polarization radar echo signal of a target object, and carrying out the preprocessing of the multi-polarization radar echo signal to generate a multi-polarization feature sequence and a modulation feature set; analyzing signal channel quality, performing multi-channel fusion, and obtaining target enhanced polarization characteristics through airspace interference suppression; performing multi-modal feature alignment on the modulation feature set and the modulation feature set, generating multi-modal feature representation through joint mapping and similarity measurement, and reducing intra-class difference; and finally, on the basis of a meta-learning framework, constructing a layered optimization framework to carry out parameter learning and meta-parameter adjustment, realizing adaptation and generalization of a radar signal modulation identification rule under a small sample by means of normalized loss mechanism standard training, and carrying out multi-polarization signal processing, multi-modal feature fusion and meta-learning optimization. Effective adaptation and generalization of the radar signal modulation recognition rule are realized under the small sample condition, and the recognition performance is improved.

The invention discloses a vehicle crawling control method, device and equipment and a storage medium, and the method comprises the steps: firstly detecting a driving mode selected by a driver, and then carrying out the matching from a pre-established crawling moment control logic set according to the driving mode, and executing a corresponding target control logic. On the basis, the system continuously collects operation parameters of the driver in the mode, and dynamically updates and optimizes the currently executed target crawling moment control logic by using the parameters, so that the self-adaptive adjustment of the control logic is realized. According to the method, by introducing a driving mode recognition and parameter learning mechanism, control over the crawling moment can adapt to different driving styles and scene requirements, and the economical efficiency, the NVH performance and the driving responsiveness of the whole vehicle are effectively improved. The method can be widely applied to the technical field of vehicles.

The invention provides a rocket aircraft flow field correction method based on multi-fidelity data fusion, and the method comprises the following steps: 1, collecting low-fidelity data, and carrying out the construction of a low-fidelity data set: simulating the change process of an unsteady flow field around an object in a finite time step through a Reynolds time-average simulation method or a large vortex simulation method; 2, data correlation analysis is carried out, wherein a linear relation model yH = rho (x) yL + delta (x) of a non-viscous flow field yL and a viscous flow field yH of the rocket aircraft is established; step 3, neural network architecture design: constructing a low-fidelity data approximation network NNL; 4, performing hyper-parameter learning and optimization; defining a loss function; 5, performing data acquisition and preprocessing: performing high-fidelity viscous flow field data acquisition, and preprocessing the acquired high-fidelity and low-fidelity flow field data; and step 6, model training and verification.

The invention discloses a quota management optimization method and system for medium and low voltage power protection projects in a distribution network, and relates to the technical field of quota management of medium and low voltage distribution networks, and the method comprises the following steps: calculating an initial quota base price of a target project sample according to a reference quota; based on the initial quota base price, the target project scene feature vector and historical project data, generating a corrected quota price through scene dynamic parameter learning; based on the target project scene feature vector and historical project data, generating a deviation estimation value through a scene deviation estimation model; determining a deviation compensation quota price according to the corrected quota price and the deviation estimation value; and taking the initial quota base price as a reference, carrying out weighted fusion on the increment between the enterprise quota price and the deviation compensation quota price according to confidence, and generating a final optimized quota price. The method is used for solving the problems that the difference between the quota price and the actual settlement price is too large in the quota management of the low-voltage power protection project, and the adaptability of the traditional quota to the complex construction scene is insufficient.

To provide a learning program, a learning method and an information processing unit that can learn a complicated probability distribution.SOLUTION: A computer is caused to execute: first processing to generate a probability distribution model by learning a probability distribution having less peaks than a target probability distribution; and second processing to generate a new probability distribution model by learning a probability distribution closer to the target probability distribution than the probability distribution having been learnt by the use of parameters of the probability distribution model having been generated.SELECTED DRAWING: Figure 4

The application discloses a lithology identification active learning method and system based on BPMN, and the system comprises a BPMN process front-end module, a data set labeling module, a lithology identification active learning task module, a dynamic form module, a camunda process engine module, a model deployment module and a storage module. The application automatically trains a target on a server side based on a BPMN2.0 specification to realize a lithology identification function. The lithology identification active learning method based on the BPMN is used to increase the calculation of the credibility, the labeling candidate set extraction module and the labeling module on the original machine learning steps, improve the parameter learning, the model training and the complexity through the active learning mode of the machine, improve the model effect, and improve the work efficiency.

The present application relates to the technical field of computer-aided design, in particular to a residential entrance facade generation method driven by enterprise knowledge base data and an interactive terminal, comprising the following steps: extracting facade key parameter classification and collection, setting threshold to screen samples to generate a training set, coding parameter card to identify difference sorting, and extracting path ratio to generate a parameter combination set; in the present application, through standardized extraction and attribute classification of residential entrance facade project parameters, structure and component feature collection is realized, sample comparability is improved, representative training samples are screened by setting construction threshold and proportion rules, parameter learning effect is strengthened, efficient index structure is constructed through field coding and parameter set, data retrieval and combination efficiency is improved, logical connection between details and overall structure is enhanced, difference sorting and offset ratio analysis mechanism realize rapid fitting and matching of input parameters, shorten the conception cycle, and improve design response speed, result controllability and scheme reuse value.

The invention discloses an autonomous mimicry control and anti-interference method for an intelligent shuttle vehicle of a refrigeration house. According to the method, an environment space-time gradient field is constructed through a distributed optical fiber sensing network, and heat flow disturbance is inverted to update a dynamic environment model; driving current harmonics are synchronously analyzed to identify a ground phase change state, and a digital twin model is utilized to compensate a mechanism hysteresis effect; a reflection-adaptation-learning three-layer decision-making framework is adopted, wherein a reflection layer responds to sudden risks at a millisecond level; adjusting motion parameters at the adaptive layer second level; learning minute-level planning of a mimicry navigation path; and finally, fusing instructions of each layer through model prediction control, and outputting a cooperative control signal. According to the method, the shuttle vehicle can actively sense, predict and adapt to the non-uniform dynamic environment of the refrigeration house, and the operation safety, the positioning precision and the operation efficiency under the low-temperature, wet and slippery and multi-disturbance working conditions are improved.

The application discloses an ISP debugging method, an ISP debugging device, an image processing system, a terminal and a computer readable storage medium. The ISP debugging method comprises the following steps: inputting a to-be-processed original image into a parameter learning prediction network; determining an image processing prediction parameter corresponding to an image processor according to the to-be-processed original image by using the parameter learning prediction network; and outputting the image processing prediction parameter to the image processor, so that the image processor performs image processing on the to-be-processed original image according to the image processing prediction parameter to obtain a target output image. In the application, the image processing prediction parameter corresponding to the image processor is determined according to the to-be-processed original image by using the parameter learning prediction network, the huge computing performance of the parameter learning prediction network can be fully utilized, and the optimal image processing prediction parameter of the to-be-processed original image can be found without too much human participation in application.

This application provides a method and system for radar signal modulation recognition based on meta-learning, relating to the field of radar signal modulation recognition technology. This application collects multi-polarized radar echo signals of a target object, preprocesses them to generate multi-polarization feature sequences and modulation feature sets; then, after analyzing the signal channel quality, multi-channel fusion is performed, and target enhanced polarization features are obtained through spatial interference suppression; subsequently, these features are aligned with the modulation feature set for multi-modal feature integration, and multi-modal feature representations are generated through joint mapping and similarity measurement, reducing intra-class differences; finally, based on a meta-learning framework, a hierarchical optimization architecture is constructed for parameter learning and meta-parameter adjustment, and normalization loss mechanism is used to standardize training, achieving adaptation and generalization of radar signal modulation recognition rules under small sample conditions. Through multi-polarization signal processing, multi-modal feature fusion, and meta-learning optimization, effective adaptation and generalization of radar signal modulation recognition rules can be achieved under small sample conditions, improving recognition performance.

The invention discloses an intelligent automobile fault identification method based on a minimum grey box, and the method comprises the steps: obtaining intelligent automobile fault injection data with fault classification labels, constructing a Bayesian network through employing a structure learning method, forming a directed acyclic graph which reflects a variable causal dependence relation, mapping the directed acyclic graph into a fault tree model, and carrying out the recognition of the fault tree model. The method comprises the following steps: establishing a corresponding structure of a fault event and a logic relationship, determining a minimum cut subset through a fault tree model, screening in combination with a Markov blanket, extracting a minimum and sufficient key variable set, forming a minimum grey box, performing parameter learning by taking a directed acyclic graph as structural input and in combination with fault injection data, and constructing a dynamic Bayesian network. And inputting the minimum grey box variable set into the network to realize the detection of the fault state of the intelligent automobile and the judgment of the fault type. According to the method, through minimum grey box construction and dynamic Bayesian network reasoning, the problems of large data redundancy, strong black box performance and insufficient causal interpretation in the prior art are solved.

The application discloses a multi-behavior recommendation method and device based on contrast clustering learning and a medium. The method first uses a graph convolution network to learn user and item embeddings of each behavior, and then designs three types of tasks to improve the embedding quality: a) behavior-level embedding, an adaptive parameter learning strategy is used to obtain embedding weights of each behavior of each user, and a weighted method is used to aggregate embedding expressions of all behaviors of each user; b) instance-level embedding, a contrast learning method is used to optimize user embeddings and item embeddings under different behaviors; c) clustering-level embedding, a contrast clustering learning method is used to explore potential clustering information between user embeddings or item embeddings to obtain more comprehensive user embedding expressions and item embedding expressions, and to alleviate the problem of data sparsity. Finally, the three tasks are jointly learned by weighting. The application is simple and effective, and through comparison with other methods and testing on known data sets, the application has good performance.

The invention discloses a small sample mine earthquake risk identification method based on a physical information convolution-attention neural network, and belongs to the field of mine dynamic disaster prevention and control. According to the method, firstly, a correlation model of deformation localization and mine earthquakes is established, and mine earthquake occurrence conditions are defined from the angle of mechanical instability; secondly, micro-seismic signals are processed through a bilinear time-frequency feature extraction module, and cross-domain feature representation is optimized in combination with an interactive iterative learning strategy; secondly, learning spatial distribution of key physical parameters based on a physical information neural network so as to regularize a stress-strain response mechanism; and fusing the time-frequency features and the physical parameters by using a convolution-attention neural network, realizing mine earthquake risk identification, designing a physical-data mixed loss function, and integrating a feature extraction module and a physical parameter learning module through a collaborative optimization strategy to jointly optimize the network performance. According to the method, the mine earthquake risk can be identified and key physical parameters can be revealed under the small sample condition, and an effective technical path is provided for prediction and early warning of mine disasters.

The invention discloses an unmanned ship turning control parameter learning method and device. The method comprises the steps of obtaining sample data of unmanned ship turning control; forming a sample set, dividing the sample set into a training set and a verification set, and constructing a turning comprehensive performance index; building a network architecture of a BP neural network model; representing a turning control parameter space by using a legal value range of the turning control parameter; configuring a training strategy for the BP neural network model based on the training set and completing training; the trained BP neural network model serves as a turning performance agent model, and a turning control parameter combination enabling the turning comprehensive performance index to be optimal is searched in the turning control parameter space according to the prediction result of the turning comprehensive performance index in the turning control parameter space. According to the method, comprehensive performance indexes including turning time, track errors and rolling angle safety penalty terms are constructed, a BP neural network model is established, and an improved genetic algorithm is adopted, so that efficient, accurate and safe automatic learning and optimization of unmanned ship turning control parameters are realized.

The invention relates to the technical field of disease detection, in particular to an early cardiovascular and cerebrovascular disease detection method based on sleep data. The method comprises the following steps: acquiring sensor data in a night sleep process of a user, and preprocessing the data to obtain intermediate features; constructing a data set based on historical data and establishing a probability distribution hypothesis, and performing parameter learning on a probability density function by using methods such as maximum likelihood estimation and the like to form a training model; extracting intermediate features from newly collected sleep data, inputting the intermediate features into the model, calculating the probability of occurrence of the intermediate features in the distribution, and judging whether the user has the risk of cardiovascular and cerebrovascular diseases according to the probability; meanwhile, the new features are stored in an individual database, and an individualized probability distribution function is constructed; and integrating the existing model and the individualized model, calculating the risk probability of the user, and outputting early warning information when the risk probability meets a preset condition. According to the invention, early risk prompting of cardiovascular and cerebrovascular diseases can be realized by using night sleep data.

The invention particularly relates to a cluster error code correction parameter learning method and system, and relates to the technical field of distributed cluster communication and error code correction. A dynamic neighborhood topology construction module; a local transfer rule engine module; an asynchronous random updating engine module; and an entropy monitoring and stability detection module. According to the method, cellular neighbors are defined according to physical and logic two dimensions, and cluster dynamic changes such as node online and offline, link faults and replica migration are accurately adapted; and physical parameter microsecond acquisition is realized through direct reading of a hardware register, communication and calculation overhead is greatly reduced through discrete coding and lightweight custom messages, and the core requirement of lightweight interaction of the cluster is met.

The invention relates to the technical field of computer aided design, in particular to a residential area entrance facade generation method driven by enterprise knowledge base data and an interactive terminal.The method comprises the following steps that facade key parameters are extracted to be classified and collected, threshold value screening samples are set to generate a training set, and coding parameter card recognition difference sorting is carried out; according to the method, through standardized extraction and attribute classification of residence entrance facade project parameters, structure and component feature collection is achieved, the comparability between samples is improved, a construction threshold value and a proportion rule are set, representative training samples are screened, and the parameter learning effect is enhanced; an efficient index structure is constructed through field coding and a parameter set, the data retrieval and combination efficiency is improved, logic connection between details and an overall structure is enhanced, rapid fitting matching of input parameters is achieved through a difference sorting and offset ratio analysis mechanism, the conception period is shortened, and the design response speed, the result controllability and the scheme reuse value are improved.