180 results about "Nonlinear modelling" patented technology

The invention discloses a cable insulation life prediction method and system based on LSTM accelerated aging mapping, and relates to the technical field of submarine cable insulation life prediction. The existing method has the defects of insufficient multi-stress nonlinear modeling, laboratory and field data separation, difficulty in small sample modeling and the like, and the insulation life of the submarine cable is difficult to accurately predict. The method comprises the following steps: data acquisition: acquiring parameters of insulation electrical performance, physical and chemical performance and mechanical performance under an accelerated aging condition; carrying out data preprocessing: carrying out homodromous processing on the inverse indexes, improving box plot denoising, filling missing data with a space-time K nearest neighbor algorithm, and carrying out normalization; constructing an LSTM model: embedding a dielectric constant differential equation as a physical constraint, and introducing an index weight; and model training and evaluation: adopting five-fold cross validation, and quantizing prediction precision through mean square errors and decision coefficients. According to the technical scheme, the prediction precision is improved, the fault risk caused by insulation aging is reduced, the maintenance cost is reduced, and the reliability of the ocean energy transmission system is improved.

The invention relates to the technical field of intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention discloses an energy-saving control method and system for a water chilling unit, and relates to the technical field of energy-saving control. During operation of the system, a data set is synchronously collected from a water chilling unit system through a multi-channel asynchronous sampling mechanism, time sequence compression and redundancy removal are carried out, an embedded perturbation calculation mechanism is used for calculating and generating a non-dominant control core coefficient, and the non-dominant control core coefficient is used for controlling the energy-saving control of the water chilling unit; a three-dimensional coefficient space is converted and output through an entropy state change structure and is used for constructing a state balance atlas, comprehensively calculating a control state index SEEI, evaluating the current energy state offset degree of a system, determining whether intervention is carried out or not, carrying out rule search and nonlinear modeling based on the control state index SEEI value, generating an adjustment matrix, and carrying out state balance analysis. And starting a data reconstruction micro-strategy of a short-time historical window, receiving an adjustment matrix, converting the adjustment matrix into a device-level instruction, executing an action through an edge controller, feeding back a response error epsilon (t) in real time, and predicting a potential performance degradation trend based on long-time system operation data.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention discloses an oil reservoir production dynamic prediction method fusing discrete gradient information, and belongs to the technical field of oil reservoir development and artificial intelligence crossing, and the method comprises the steps: building a heterogeneous oil reservoir oil-water two-phase flow numerical simulation data set based on a numerical simulation method; designing a double-branch network structure and extracting spatial and physical characteristics of input field data in parallel, wherein the spatial and physical characteristics comprise a main characteristic coding branch and a differential operator branch; designing a backbone network to carry out deep nonlinear modeling; an efficient pressure and saturation field prediction neural network model is constructed based on a double-branch network structure and a backbone network, in a model training stage, spatial region observation points of part of time steps are used to participate in data item loss calculation, and meanwhile, physical control equation residuals are introduced into all time steps and a whole space to serve as physical loss items; a trained efficient pressure and saturation field prediction neural network model is obtained, and high-precision prediction of a full-time-sequence pressure field and a saturation field is achieved.

According to the multi-channel feature image-based improved MobileNetV2 voltage transformer fault diagnosis method, a partial overlapping sliding window technology is used to carry out data enhancement on a secondary side voltage signal so as to construct images capable of representing different features. Through fusion of multi-channel feature images representing structure information, transient change and frequency domain characteristics, combined extraction of voltage signal multi-dimensional features is realized. On the basis, an improved MobileNetV2 diagnosis model is provided, and the nonlinear modeling capability of the network to a complex feature mode is enhanced by introducing a Mish activation function; and a normalization-based attention module (NAM) is introduced to realize dynamic focusing of fault features. Meanwhile, a receptive field of a convolutional layer is expanded by using multi-scale expansion convolution, so that correlation modeling between features is enhanced. According to the framework, the diagnosis precision of the voltage transformer is remarkably improved while the light weight of the model is kept.

The invention discloses a magnetostriction liquid level meter temperature compensation method and system based on a support vector machine. The method comprises the steps that the currently captured original pulse number is obtained through a magnetostriction liquid level meter; the current environment temperature is detected in real time through a high-precision temperature sensor in the magnetostrictive liquid level meter; taking the original pulse number and the current environment temperature as input features, sending the input features to a pre-trained support vector machine regression model (SVM), and outputting a displacement error prediction value in the current temperature environment; and correcting the original measurement result according to the error prediction value to obtain a real pulse measurement value after temperature compensation. The system comprises a time-to-digital conversion chip, a temperature chip temperature measurement module, a support vector machine reasoning module, a pulse compensation module and a distance calculation module. According to the method, nonlinear modeling can be carried out on measurement errors caused by environment temperature changes, so that the liquid level measurement result is dynamically corrected, and the overall robustness and precision of a measurement system are improved.

The invention relates to the technical field of biological feature recognition, and particularly provides an anti-migration PPG recognition method based on rate perception and a state space model. The method comprises the following steps: performing physiological feature front-end extraction on an original single-channel PPG signal to obtain a high-dimensional shallow feature sequence; performing double-flow cooperative processing on the high-dimensional shallow-layer feature sequence, and distributing the high-dimensional shallow-layer feature sequence to two parallel branches, namely a control flow branch and a data flow branch; in the control flow branch, an amplitude spectrum and an instantaneous physiological rate curve are obtained; in the data stream branch, acquiring a deep global feature sequence with rate invariance; obtaining multi-scale refinement features based on the high-dimensional shallow feature sequence and the deep global feature sequence; according to the multi-scale refinement features, a final biological feature recognition result is obtained, the method can actively sense the physiological rate change, and efficient nonlinear modeling can be achieved with the extremely low parameter quantity.

The invention discloses an MEKF attitude estimation method based on a physical information neural network, and the method comprises the steps: dynamically predicting an observation matrix of an MEKF through the dynamic modeling capability and constraint embedding characteristics of the physical information neural network, and extracting sensor sequence features through the long-range dependence capturing capability of a time domain convolutional network; and meanwhile, the physical rule constraint is embedded into network training to ensure the physical rationality of a prediction result. Through data-driven nonlinear modeling and a recursive estimation framework of the MEKF, the limitation that the MEKF is high in dependence on noise covariance and poor in dynamic adaptability is effectively overcome, and the precision and the anti-interference capability of attitude estimation in a complex environment are remarkably improved.

The invention relates to the technical field of carbon flux monitoring, and particularly discloses a carbon flux monitoring method and system based on soil drought remote sensing data, and the method comprises the steps: respectively collecting soil humidity remote sensing data and a soil vegetation coverage remote sensing image through a microwave sensor and an optical remote sensing sensor; performing resampling on the soil humidity remote sensing data and the soil vegetation coverage remote sensing image to realize spatial resolution alignment, and performing spatial distribution feature extraction on the soil humidity remote sensing data and the soil vegetation coverage remote sensing image after spatial alignment by adopting a deep learning algorithm; and pixel-level significance enhancement and fusion processing are carried out on the captured soil humidity distribution characteristics and vegetation distribution characteristics, so that nonlinear modeling of a dynamic influence mechanism of the soil drought degree on the vegetation growth state is realized, and intelligent estimation of the carbon flux is realized on the basis. In this way, the nonlinear regulation and control effect of soil drought stress on vegetation respiration and photosynthesis can be effectively described, and the efficiency and coverage range of carbon flux monitoring are improved.

The invention discloses a multi-energy system closed-loop prediction control method, system, equipment and medium, and the method comprises the steps: carrying out the decomposition, dimension reduction and nonlinear modeling of the time sequence characteristics and environmental influence factors of photovoltaic output through a photovoltaic power prediction model, and outputting a future multi-period photovoltaic power prediction sequence; establishing a state equation and an output equation, integrating equipment operation constraints, and constructing a hydrogen-containing energy storage state space model; inputting local load power and a future multi-period photovoltaic power prediction sequence into the hydrogen-containing energy storage state space model, and solving an objective function through a rolling optimization algorithm to obtain a future multi-period optimal scheduling scheme; and applying a first hour control instruction of the optimal scheduling scheme to an actual system, and proportionally superposing the deviation between an actual measurement value and a historical prediction value to a photovoltaic power prediction sequence of a next period through a feedback correction item to realize closed-loop control. According to the method, the photovoltaic local consumption rate can be improved, and the power grid fluctuation rate is reduced.

The invention provides a Mars mineral abundance inversion method and system. The Mars mineral abundance inversion method comprises the steps of firstly obtaining target Mars hyperspectral reflectivity data; and then generating simulated Mars hyperspectral image data based on the target Mars hyperspectral reflectivity data. Secondly, training the constructed mineral abundance inversion deep learning model based on the simulated Mars hyperspectral image data, and determining the trained mineral abundance inversion deep learning model; thirdly, acquiring to-be-processed Mars hyperspectral image data, and performing preprocessing; and finally, performing mineral abundance inversion on the preprocessed to-be-processed Mars hyperspectral image data through the trained mineral abundance inversion deep learning model to obtain a mineral abundance inversion result. Therefore, the weight distribution of each wave band of the hyperspectral image can be enhanced through the mineral abundance inversion deep learning model, and the non-linear modeling capability and the wave band characteristic response capability of abundance inversion are improved, so that the accuracy and the efficiency of Mars mineral abundance inversion can be effectively improved.

The invention provides a multi-dimensional data preferential analysis method and device based on a gradient boosting decision tree model, and relates to the technical field of data mining, in the method, a target gradient boosting decision tree model is adopted, the nonlinear modeling problem of strategy optimization in multi-dimensional data is solved, and the multi-dimensional data is optimized according to the target weight determined in the training process. According to the method, key features can be accurately identified, the processing efficiency and precision are high, compared with a traditional linear regression or single decision tree method, the accuracy and real-time performance of strategy optimization can be improved, high interpretability and robustness are achieved, an optimization strategy can be adjusted in a self-adaptive mode in a changeable environment, and the method is suitable for popularization and application. The method is widely applied to multiple fields of industrial optimization, intelligent decision making and the like.

The invention discloses a photovoltaic power generation power prediction and power system scheduling method and a system for realizing the method. The method comprises four steps of data acquisition and preprocessing, similar day selection and training set construction, GA (Genetic Algorithm)-fuzzy RBF (Radial Basis Function) neural network modeling and power scheduling plan generation. The method comprises the following steps: selecting a meteorological variable with high correlation degree through a Spearman rank correlation coefficient, and constructing a time sequence feature; similar days are utilized to construct training samples, and the generalization ability of the model is improved; a fuzzy RBF network optimized by GA is adopted to enhance the nonlinear modeling precision; and inputting a prediction result into the power system simulation model, and generating an optimal scheduling instruction by adopting dynamic programming. According to the method, the photovoltaic power prediction accuracy and scheduling efficiency can be remarkably improved, the power grid stability is enhanced, and the new energy consumption capability is promoted.

The invention discloses a split type three-box girder nonlinear vortex vibration steady-state amplitude intelligent prediction method, belongs to the field of bridge vortex-induced vibration control, and aims to solve the problem of low accuracy of three-box girder vortex vibration nonlinear modeling and amplitude prediction. The method comprises the following steps: acquiring displacement time sequence data through a reduced scale model wind tunnel test, preprocessing to obtain displacement, speed and acceleration dimensionless data, and dividing a data set; constructing a candidate function library containing high-order polynomial terms of the primary function; performing nonlinear system sparse recognition by taking acceleration data as a target item and combining an improved algorithm to obtain a preliminary feature set; a final feature set is obtained through energy-statistics-self-adaption three-stage screening; and finally, constructing a control equation to predict the steady-state amplitude. The method can accurately predict amplitude, coincide test and real bridge observation results, and is less in computing resource occupation and high in numerical value precision.

The invention relates to the technical field of vehicle thermal management, in particular to a prediction model construction method and device for vehicle thermal management, a control method and device and a vehicle, and the construction method comprises the steps: obtaining a historical sample data set of a vehicle thermal management system; preprocessing the historical sample data set; an initial prediction model is established based on a Transform network, the input of the initial prediction model is state data, the output of the initial prediction model is control data, and a loss function comprises a mean square error term and a physical constraint term; and taking the minimum loss function value as an optimization target, and performing iterative training optimization on the initial prediction model through the preprocessed historical sample data to obtain a prediction model of vehicle thermal management. According to the method, on the basis of strong nonlinear modeling capability of the Transform network, model parameters are optimized by introducing physical constraint terms, so that a model prediction result is ensured to be consistent with an actual physical rule, and a prediction error caused by violation of the physical rule is avoided, thereby improving the prediction precision.

The invention provides an engineering drawing steel bar bulk sample annotation text detection method, system and device based on deep learning, and a storage medium. The method comprises the following steps: S1, preprocessing an engineering drawing image and labeling a steel bar bulk sample annotation text region data set; s2, constructing a staged multi-scale feature map extraction backbone network framework; s3, introducing compression excitation into each scale feature map; s4, adopting an ACON adaptive activation function in nonlinear modeling; s5, constructing a multi-scale feature pyramid structure based on the RSE-FPN, and superposing scales to reinforce fusion; and S6, outputting the approximate binary image to realize accurate prediction of the bounding box. According to the method, the OCR detection precision is remarkably improved, small character and complex background interference is effectively overcome, and missing detection and false detection are reduced; the model is lightweight to facilitate efficient deployment of equipment; the output boundary is compact and accurate, the semantics is reasonable, and subsequent recognition and analysis are facilitated; the method has excellent generalization ability, adapts to steel bar large sample drawings of different formats, definitions and styles, and is high in engineering practicability.

The invention discloses a crystal size distribution soft measurement method integrated with mechanism constraints. The method comprises the following steps: 1) data acquisition and integration; 2) modeling training and model testing: constructing a crystal size distribution soft measurement model integrated with mechanism constraint, training a training data set, injecting crystallization process dynamics knowledge into the Kolmogorov-Arnod network in a loss function form, improving the nonlinear modeling capability and credibility of the model, and performing model testing on the Kolmogorov-Arnod network; and verifying test set data by using the trained model. According to the method, the knowledge of the crystallization dynamic process is injected into the Kolmogorov-Arnod network in the form of the loss function, so that the prediction capability of the model on the crystal size distribution in the crystallization process and the credibility of the model are effectively improved.

The invention relates to the technical field of precision timing instrument testing, in particular to an atomic clock aging prediction method based on feature fusion and quantum constraint learning. According to the atomic clock aging prediction method based on multi-modal feature fusion and quantum constraint learning provided by the invention, a multi-modal tensor fusion means is adopted, and a time domain microwave probe signal, a space domain atomic cloud density distribution signal and a frequency domain lambda stripe signal are collected at the same time for feature extraction, so that the completeness of the features is improved; during nonlinear modeling, a Bloch equation constraint is adopted to ensure that a network model accords with a quantum physics rule. According to the method, the atomic clock aging feature expression effect can be improved through an artificial intelligence means, the defect of overfitting caused by purely depending on training data is effectively avoided, the atomic clock aging prediction model better conforms to the physical law, the atomic clock aging prediction precision is improved, and the prediction model complexity is optimized.

The invention discloses a method for evaluating soil pollution hazards based on ecological risk indexes in the technical field of ecological protection, which comprises the following steps: sampling soil in a target area to obtain sampled soil; the method comprises the following steps: collecting the concentration value of pollutants in sampled soil, and determining the basic toxicity coefficient of the pollutants by utilizing a toxicological experiment; constructing a basic toxicity coefficient dynamic correction model to obtain a dynamic toxicity response coefficient of the pollutant concentration value; and constructing an environment factor interaction model. According to the method, the multi-source data is composed of the concentration value and the pH value of the inorganic pollutant and the concentration value of the organic matter, and through fusion and nonlinear modeling of the multi-source data, the problems of weight solidification and neglect of spatial heterogeneity in a traditional method can be solved; the evaluation scientificity, accuracy and operability of the soil pollution risk in the target area can be improved, and the influence of the toxicity of the pollutants on the soil pollution hazard can be effectively evaluated by fusing the synergistic and antagonistic effects among the pollutants.

The invention relates to a Beidou / GNSS + 5G unmanned aerial vehicle cooperative positioning method based on a multi-mode neural network, and belongs to the technical field of unmanned aerial vehicle navigation and intelligent logistics. According to the invention, through nonlinear modeling, multi-source feature adaptive extraction and autonomous learning capabilities of the multi-modal neural network, Beidou, 5G, inertial navigation and visual environment perception data are deeply fused, and in combination with scene adaptive switching and cluster collaborative optimization, high-precision and high-robustness positioning support is provided for the low-altitude logistics unmanned aerial vehicle in a complex environment. According to the invention, through multi-source data deep fusion, scene adaptive switching and cluster collaborative optimization, the positioning precision and reliability of the low-altitude logistics unmanned aerial vehicle in a complex environment are effectively improved.

The invention relates to the technical field of intelligent financial evaluation, and discloses a financial asset rating method and device based on a gradient boosting tree, equipment and a medium, and the method comprises the steps: preprocessing financial data; financial rating features related to financial asset rating in the preprocessed financial data are determined based on statistical analysis, domain knowledge and an automatic feature generation method, and the financial rating features are extracted; on the basis of a gradient boosting tree algorithm, a financial asset rating model is constructed according to financial rating characteristics, and the financial asset rating model is trained and optimized through a parameter tuning and model integration technology; and obtaining a prediction result and a result explanation of the financial asset rating model, recording the result explanation and generating an understandable rating report. The method can be applied to the development of business systems such as financial science and technology, medical health, old-age care and the like, and the accuracy and reliability of financial asset rating are remarkably improved through nonlinear modeling and high-dimensional data processing capacity.

The invention discloses an automobile queue control method based on pipeline distributed model predictive control, and belongs to the field of intelligent automobiles and intelligent traffic. The method comprises the following steps: constructing a vehicle discrete dynamic model; establishing a queue cooperative control framework through vehicle-vehicle communication; introducing a wheel slip rate into the queue performance index function, and setting a longitudinal slip rate as a constraint; on the basis of nominal model predictive control, a nominal optimal control input sequence and an optimal state track are generated through decision making; and then the state of the disturbed vehicle is corrected in real time through an auxiliary control law, so that the disturbed vehicle is restrained near the optimal track. In the queue car-following control process, an interference suppression mechanism and tire nonlinear modeling are introduced into the system, and the limitation that the node vehicle stability and the full-queue control performance are not sufficiently considered in a traditional method is broken through. Even under the limiting working conditions of high-speed driving, low adhesion and the like, the stability and the good all-working-condition control performance of the automobile queue can be guaranteed.

The invention discloses an earthquake collapse building dielectric constant inversion method based on an improved neural network, and aims to improve the inversion precision of ground penetrating radar (GPR) data and the recognition capability of an internal structure. The method comprises the following steps: firstly, acquiring a radar data set for simulating a collapsed building structure, and preprocessing the radar data set, including background interference removal, median filtering and time gain enhancement, so as to improve the signal-to-noise ratio; on the basis, a UKAN neural network introducing an attention mechanism and depth separable convolution is constructed, and a KAN module is combined to strengthen the nonlinear modeling capability. The GPR image is inverted through the model, dielectric constant distribution in a collapse structure is reconstructed, and automatic recognition of key areas such as survival gaps is achieved. The method can provide efficient and accurate structural analysis and rescue decision support in a complex post-disaster environment, and has practical application value.

The invention relates to the technical field of intelligent detection, and discloses a T-SVAE feature extraction strategy and a method for improving near infrared spectrum soil rapidly available potassium measurement precision by using the T-SVAE feature extraction strategy, and the method comprises the following steps: step 1, collecting soil surface samples of different plots, obtaining near infrared spectrum data of the soil samples by using a Fourier transform near infrared spectrometer, and calculating the near infrared spectrum data of the soil samples; determining the actual content of quick-acting potassium in the soil sample by adopting a national standard method; the method comprises the following steps: 1, acquiring near infrared spectrum data, 2, preprocessing the acquired near infrared spectrum data, and removing impurity signals caused by instrument fluctuation, environmental interference and sample physical form difference, and 3, constructing a Transform and supervision constraint fused variational self-encoding model (T-SVAE). By constructing a variational self-encoding model fusing Transform and supervision constraint, the problem of feature blindness caused by high-dimensional data difficulty, nonlinear modeling limitation and unsupervised learning in near infrared spectrum data processing of a traditional feature extraction method is effectively solved.

The invention relates to the field of lunar remote sensing detection, in particular to a lunar mineral multi-source data collaborative inversion method based on deep learning, and the method comprises the steps: constructing a multi-source data set; training a mineral prediction model based on the multi-source data set, and evaluating the mineral prediction model by adopting a cross validation method; inputting the oxide remote sensing data into the mineral prediction model to generate an initial mineral abundance prediction map; lithology unit distribution is extracted, space matching is carried out on the initial mineral abundance prediction map, a credible mineral content interval is set for each type of lithology, a priori normal distribution weight is constructed, and weighted correction is carried out on the initial mineral abundance prediction map; and performing multi-scale verification on the calibrated mineral abundance prediction map by combining actually measured mineral data and mineral content remote sensing data of a landing point of the lunar probe. According to the method, the problems of non-linear modeling deficiency and poor regional generalization in traditional mineral prediction are solved, and the mineral abundance prediction precision of scarce data areas such as the back surface of the moon is remarkably improved.

The invention relates to the field of artificial intelligence and computer vision, in particular to an unmanned aerial vehicle multi-target tracking method based on a CNN-Transform-Mama network and a space-time Mama motion model, and the method comprises the steps: obtaining a video data set of multi-target tracking collected by an unmanned aerial vehicle; constructing a CNN-Transform-Mama network as a target detector to obtain bounding boxes and spatial position information of a plurality of targets on the ground in a single-frame image; constructing a multi-target space-time trajectory based on the space position information in the continuous space-time; a space-time Mama motion model is constructed to complete nonlinear modeling of a multi-target space-time trajectory, and multi-target tracking is achieved; and constructing a joint loss function training model, and evaluating the performance of the model through precision evaluation. According to the invention, by aggregating the advantages of the CNN, the Transform and the Mama network, the local-global-long-range dependency features are effectively fused, and the positioning precision of the multi-target position is improved; and the precision and the robustness of multi-target tracking are improved through nonlinear modeling of the space-time trajectory, so that the adaptive capacity to a complex scene is further improved.

The invention relates to the technical field of hysteresis nonlinear modeling and displacement compensation, and discloses a driver displacement compensation method based on weighting function equidensity segmentation, and the method comprises the steps: building a test system, outputting a drive signal, and collecting displacement data; acquiring a hysteretic curve, and equally dividing an input interval to construct a Preisach surface; a Preisach positive model is identified by adopting a non-negative least square method; performing equal-density geometric segmentation on the Preisach surface, and re-dividing the weight of the Preisach surface; constructing an inverse model, and performing step iteration to adjust the current to approach the expected displacement; hysteresis nonlinearity is compensated in real time; the system comprises an inverse model module, an upper computer module, a power supply control module, a programmable DC power supply module, a GMA driver module and a displacement acquisition module. According to the method, the Preisach model is optimized by adopting an isodensity geometric segmentation technology, and the spatial resolution of the model is refined, so that the original rough hysteretic curve is described more accurately.

The invention provides an intelligent control method, device and equipment for a steam crosslinking room and a medium, and relates to the technical field of intelligent control, and the method comprises the steps: collecting temperature, steam and humidity parameters in real time to form a multi-parameter observation vector with synchronous time; establishing a dynamic heat conduction model for describing the nonlinear coupling relation of the temperature, the steam flow and the thermal inertia; pID parameter output control quantity is adaptively adjusted based on the temperature deviation and the error change rate, a future temperature track is predicted by using the model, and an objective function is optimized under constraint conditions to obtain an optimal control increment; and combining the temperature deviation energy integral, the steam energy consumption and the temperature fluctuation variance to construct a multi-target optimization function to dynamically adjust the weight and output a target control signal. According to the method, the problems that a traditional control mode lacks nonlinear modeling and predictive compensation capability, temperature fluctuation is easily caused, a thermal field is uneven, and material performance is unstable can be solved.