56 results about "Continuous parameter" patented technology

The invention provides a micro-lens array turning servo control method and device and a medium, and relates to the technical field of micro-lens array turning servo control, and the method comprises the steps: dispersing a continuous track curve corresponding to a micro-lens array turning process into a plurality of track points in a parameter domain; applying cubic interpolation in a time domain to construct a continuous parameter velocity profile; integrating the parameter velocity profile to obtain a parameter trajectory u (t); generating an optimal trajectory P (u (t)) which corresponds to the continuous trajectory curve and meets kinematics constraints; deriving the P (u (t)) to obtain expected speeds and expected accelerations of an X axis, a Z axis and a C axis of a machine tool execution mechanism; taking the expected speed and the expected acceleration as feed-forward input, substituting the feed-forward input into an output calculation formula of a servo controller, and obtaining servo output to drive X-axis, Z-axis and C-axis movement; the problem that precision and efficiency are difficult to consider in the prior art can be effectively solved.

The invention provides an automobile injection mold data real-time monitoring method and system, and relates to the technical field of data processing.The method comprises the steps that increment changes of all parameters in adjacent sampling time are compared according to native data, and parameter point locations with the changes exceeding a preset response threshold value are marked; extracting continuous parameter records in the time interval in which the abnormal change occurs, and generating abnormal interval analysis data according to the change trend of the continuous parameter records; performing grading judgment on the abnormal interval analysis data, dividing each abnormal interval into different grades, and generating multi-dimensional abnormal description data; searching a historical normal parameter interval, performing multi-dimensional feature matching and comparison on the historical normal parameter interval, calculating a parameter adjustment amount, and generating a dynamic regulation and control suggestion; according to the invention, the autonomy and accuracy of real-time monitoring of the data of the automobile injection mold are improved.

The application discloses an end-to-end cooperative target feature extraction and matching method based on a reference frame. In view of the problems that target feature extraction is easy to be disturbed and cross-view matching is poor in robustness in a complex industrial environment, a unified end-to-end neural network is constructed: background disturbance is inhibited through a semantic shunt backbone network, sub-pixel level ellipse center positioning and morphological parameter regression are realized by using a dense offset field and a continuous parameter field; multi-modal features are extracted based on a dynamic region of interest, local visual information and geometric parameters are fused; and a multi-modal Transformer matching module with a fault-tolerant mechanism is introduced. The application realizes 100% feature detection rate and matching rate under a test set of complex light, large viewing angle, local occlusion and other working conditions, and can be widely applied to feature point extraction and matching in high-precision real-time posture measurement of industrial robots such as aviation hole making.

The invention relates to the technical field of composite material forming and manufacturing, and particularly discloses a multi-stage multi-target process parameter optimization method under strong coupling in a composite material fiber placement process, which comprises the following steps: training a neural network prediction model according to fiber placement machine process parameter experimental data and formed part quality index experimental data; inputting process parameter sampling data of the fiber placement machine into the trained neural network prediction model so as to output quality index prediction data of the formed part; outputting a plurality of groups of optimal solutions according to the process parameter sampling data and the quality index prediction data through an NSGA-II algorithm; a target optimal solution is screened out from the multiple sets of optimal solutions through a TOPSIS algorithm, and technological parameter sampling data in the target optimal solution serve as final technological parameters; and verifying the final process parameters. Cooperative regulation and control of continuous parameters and discrete parameters in the fiber placement process can be achieved, defect control and mechanical property improvement of the formed part are both considered, and process support is provided for batch production of high-performance composite material components.

The invention provides a coverage-driven OSC2.0 format scene generation method and device, and the method comprises the steps: analyzing an OSC2.0 abstract scene file, extracting parameters and constraint conditions, and discretizing continuous parameters according to a preset step length; defining a coverage index based on a k-wise combination test idea, and determining a to-be-covered parameter value and a combination set P; calculating a potential value of each parameter combination com and a vulnerability hole set, preferentially selecting the com with the most vulnerabilities, and determining a parameter value of the com; updating the vulnerability set for the remaining unassigned parameters, selecting the parameter with the most vulnerabilities, determining the value of the parameter in combination with the k-wise combination coverage improvement effect, adding the value into the current com, repeating until the remaining parameters have no vulnerabilities, and completing assignment according to constraints to generate an OSC1.0 concrete scene; and adding the concrete scene into the test set, updating the coverage degree and the vulnerability set, and stopping generation until the coverage degree reaches the standard or no vulnerability exists. According to the application, the OSC2.0 and the OSC1.0 can be efficiently connected, the scale of the test set is controlled, and the coverage integrity is guaranteed.

The application discloses a database parameter tuning method based on parameter type customization optimization, and belongs to the field of database configuration optimization. The method comprises the following steps: dividing a database parameter space into a continuous parameter subspace and a discrete parameter subspace according to parameter types, and projecting the continuous parameter subspace and the discrete parameter subspace into a low-dimensional space; using a GP model and an SMAC model as proxy models to search for continuous parameter configurations and discrete parameter configurations in the low-dimensional continuous parameter subspace and the low-dimensional discrete parameter subspace respectively; realizing the interaction between the GP model and the SMAC model through iterative optimization and a context-based communication mechanism; and mapping the parameter configuration results back to the database parameter space for performance evaluation. The application can effectively improve the performance and efficiency of database parameter tuning, and allows users to search for database parameter configurations with higher performance in a shorter time.

The invention discloses a three-dimensional geological modeling and stress analysis system based on comprehensive geophysical prospecting, and relates to the field of geological geophysical prospecting analysis. The system comprises a vector acquisition unit, a mode clustering unit, a coordinate acquisition unit, a mode screening unit, a sequence construction unit and a sequence extraction unit, and is used for extracting Q geological state evolution sequences corresponding to Q stable geological clusters from N geological state evolution sequences; the sequence embedding unit is used for embedding the Q geological state evolution sequences into a pre-constructed three-dimensional geological model to generate a high-quality geological unit for stress analysis; the internal parameters of the finally generated high-quality geological units are in smooth transition along the depth direction, and the transverse adjacent units have good transverse transition due to a stable mode of underground space aggregation, so that the internal consistency of the three-dimensional geological model is remarkably improved, and sample data with stable structure and continuous parameters are provided for stress analysis.

The embodiment of the invention provides a space bounding box calculation method for a three-dimensional model. The method comprises the steps that continuous parameterized expression of the surface of a target three-dimensional model on a parameter domain is determined; according to the continuous parameterized expression, analyzing a plurality of profile curves obtained by projecting the target three-dimensional model on a plurality of mutually orthogonal coordinate planes in a three-dimensional coordinate system; according to the parameterized expressions of the plurality of profile curves, determining two-dimensional bounding boxes respectively corresponding to the plurality of profile curves on the corresponding coordinate planes; and performing extreme value selection operation on the boundary value of the two-dimensional bounding box in the coordinate axis direction to generate a three-dimensional space bounding box of the target three-dimensional model. A projection profile curve is directly analyzed through continuous parameterization expression, the geometric boundary of the model is locked in mathematics essence, the defect that extreme points are missed in discrete sampling is avoided, and the calculation precision is improved; and three-dimensional extremum search is converted into two-dimensional profile curve analysis and two-dimensional bounding box construction on a plurality of orthogonal coordinate planes, so that the calculation complexity is reduced.

The invention relates to the technical field of water treatment equipment multi-dimensional management, and discloses a water treatment equipment multi-dimensional management system based on an intelligent water affair cloud platform, and the system comprises the steps: calculating a dynamic alpha factor in real time through on-site off-gas detection data and a clear water reference mass transfer coefficient, and carrying out the real-time calculation of a dynamic alpha factor based on the monotonous relation between a bubble stagnation cap and mass transfer resistance; and performing inversion to obtain continuous parameters for quantifying the gas-liquid interface state. And further calculating the real-time sensitivity of the parameter to the air volume of the air blower, constructing an air volume and mass transfer nonlinear model, and solving the target air volume with the minimum energy consumption under the condition of meeting the technological oxygen demand constraint condition. And finally, the target air volume serves as a control instruction to be issued, meanwhile, interface recovery time is output according to the time change rate of the gas-liquid interface parameters, and dynamic energy saving and interface state collaborative management of the water treatment aeration system is achieved.

The present invention discloses a method for processing equivalent electromagnetic parameters of reinforced concrete for analyzing the time-domain coupling characteristics of electromagnetic pulse environments. The method first obtains the S parameters of a reinforced concrete wall structure; then, the relationship between the S parameters and the reflection coefficient and transmission coefficient is calculated; then, the relationship between the relative permittivity and relative magnetic permeability and the reflection coefficient and transmission coefficient is calculated; then, the intermediate variables, the reflection coefficient and transmission coefficient, are eliminated to obtain a relationship expressing the relative permittivity and relative magnetic permeability using the S parameters; finally, the obtained equivalent electromagnetic parameters are fitted into a specific, frequency-continuous mathematical polynomial form. The present invention overcomes the multi-valued nature of the equivalent electromagnetic parameter calculation results through imaginary part compensation, and achieves frequency-domain continuous parameter calculation in the frequency band of interest through polynomial fitting. This method can effectively reduce the computational resource consumption of complex reinforced concrete building models, and provides a method for calculating the time-domain electromagnetic coupling response of large-scale buildings to external electromagnetic pulse environments.

The application discloses a multiple-input multiple-output antenna decoupling performance prediction method based on deep learning, belongs to the technical field of computer-aided electromagnetic system design and prediction, is used for antenna decoupling performance prediction, and comprises the following steps: determining key size parameters of an antenna to be optimized; performing parameter scanning on the variation range of the key size parameters to obtain full-band S parameter simulation data and form an original data set; the data amount of the original data set is expanded by using an SMOTE algorithm to generate an enhanced data set; an antenna decoupling performance prediction model is constructed and trained, the antenna to be optimized is input, and corresponding full-band S parameter curve final prediction results are output. The application effectively expands high-dimensional data by introducing the SMOTE algorithm, solves the problem that small sample data in the electromagnetic simulation field cannot support deep learning model training, constructs a DNN model to establish a nonlinear mapping of a continuous S parameter curve, and can comprehensively evaluate the S parameters of the antenna, instead of being limited to discrete frequency points.

This invention proposes a safety assessment method for delaminated composite materials based on the principle of damage non-propagation, belonging to the fields of composite material delaminated damage modeling and composite structure safety assessment. The method includes: constructing ultimate load envelopes that meet the damage non-propagation requirement under different damage sizes; for readily available binary observation data in practical engineering, i.e., whether damage propagates under specific damage sizes and load conditions, connecting discrete observations with a continuous parameter space through a latent variable model; using the Probit link function to map the predicted difference between the load and the ultimate load into a damage propagation probability; and constructing a likelihood model using a Bernoulli likelihood function; and optimizing the ultimate load envelope within a Bayesian framework. This invention can effectively support safety assessment and maintenance decisions for composite material structures in aerospace and other fields.

The invention discloses a parameterized CAD sequence generation method based on a knowledge-guided Bayesian flow network. The parameterized CAD sequence generation method comprises the following steps: constructing a quantitative constraint data set of a parameterized CAD sequence; a Bayesian flow network containing a main channel and an auxiliary channel is built, the main channel achieves original Bayesian flow modeling, the auxiliary channel introduces knowledge guidance of deviation of intermediate sequence geometric attributes and constraints in iteration generation, the starting frequency of the auxiliary channel is regulated and controlled through an annealing routing mechanism, and geometric accuracy and semantic consistency of generated sequences are optimized; training the network by using the data set to obtain a generative model; and inputting constraint conditions to generate a target sequence. The method is suitable for parameterized CAD sequence generation tasks containing quantitative geometric constraints, mixed representation of discrete commands and continuous parameters can be effectively modeled, and the method can be widely applied to scenes such as automatic industrial design and reverse modeling.

The application discloses a geometric invariant three-dimensional model classification algorithm based on a smooth quadratic loss, which comprises the following steps: S1, obtaining three-dimensional models of various product parts, and performing mesh sampling on continuous parameter surfaces and edges in each three-dimensional model to obtain discretized parameter surfaces and edges; S2, performing random rotation on each discretized edge and parameter surface, and using the product part type corresponding to the three-dimensional model, the discretized parameter surface and edge before and after the random rotation as a data set; S3, training a deep classification model by using the data set, wherein a loss function of the deep classification model comprises a smooth quadratic loss function based on model output before and after rotation and a cross-entropy loss function; and S4, performing mesh sampling on continuous parameter surfaces and edges of an entity three-dimensional model of a product part to be identified, and then inputting the trained deep classification model to obtain the type of the product part to be identified.

The invention discloses a genetic algorithm-based tight gas reservoir horizontal well fracturing section automatic division method. The method comprises the following steps of S1, determining a reasonable fracturing section division principle; s2, performing quantitative expression on the key parameters of the fractured section of the tight gas reservoir horizontal well; s3, acquiring continuous parameter values of the key parameters of the target layer section of the horizontal well according to the logging curve data, and respectively constructing a porosity vector, a Young modulus vector, a Poisson's ratio vector and a minimum horizontal principal stress vector of each fracturing section; s4, taking the standard deviation minimization of the attribute values of the fracturing sections as an optimization target, and establishing a mathematical model for dividing the fracturing sections of the single well; and S5, fracturing section division is converted into an optimization problem, and a genetic algorithm (GA) is adopted for efficient solving. According to the method, automatic division of the tight gas reservoir horizontal well fracturing sections can be rapidly and efficiently achieved, balanced crack initiation of hydraulic fractures is facilitated, the fracturing effect is improved, and tight gas reservoir development is optimized.

The embodiment of the invention provides a maximum segmentation solving method based on primal dual graph neural network learning optimization and related equipment, and the method comprises the steps: firstly, obtaining an image maximum segmentation model which comprises an adjacent objective function and a one-hot vector parameter; then, simplex relaxation conversion is carried out on one-hot vector parameters in the image maximum segmentation model, a relaxation continuous optimization model is obtained, and the relaxation continuous optimization model comprises continuous parameters; thirdly, performing iterative solution on the relaxation continuous optimization model based on an adjacent objective function, continuous parameters and a dual hybrid gradient neural network to obtain a relaxation feasible solution; and finally, sampling is carried out based on the probability distribution of the relaxation feasible solution, a target feasible solution of the image maximum segmentation model is obtained, the target feasible solution is used for segmenting image data corresponding to the image maximum segmentation model into multiple pieces of subset image data with the maximum total weight, and the solving efficiency of the maximum segmentation model is greatly improved.

The invention discloses a soil environment detection method based on a gun-shaped detection gun, which is used for rapid field detection of a soil environment, and is characterized in that a multi-sensor probe array is integrated through gun configuration equipment, a probe can be inserted into soil by pulling a trigger once by a user, and in the insertion process, the equipment utilizes a probe insertion kinetic model to perform self-adaptive adjustment, so that the detection accuracy is improved. The method is used for overcoming resistance of different soil firmness and synchronously acquiring parameters such as soil humidity, pH value, conductivity, temperature and nitrogen phosphorus and potassium nutrients, and the core of the method is that depth profile reconstruction and intelligent decision are realized through algorithm processing: measured data of discrete depth points are reconstructed into a continuous parameter vertical distribution curve; according to the method, multi-source data is subjected to fusion and uncertainty quantification, a comprehensive report containing multi-parameter values, profile curves, soil quality indexes and risk levels is finally output, the detection time is shortened from several hours to about 30 seconds, and the efficiency, depth and intelligent level of field detection are greatly improved.

The invention discloses a roadway rockburst risk level total-space prediction method and system, and relates to the technical field of rockburst disaster risk prediction. The method comprises the steps that rock mass mechanical parameters collected through a plurality of discrete monitoring points in a roadway target area are obtained; performing spatial interpolation processing on the rock mass mechanical parameters, and constructing a continuous parameter field covering the target area; inputting the continuous parameter field into a pre-trained rockburst prediction model; the rockburst prediction model performs feature extraction on the continuous parameter field through a Transform module, inputs the extracted features into an Adaboost module for integrated classification, and outputs a rockburst risk level prediction result; and based on the rockburst risk level prediction result, generating a rockburst risk level three-dimensional visualization graph of the target area. According to the method, the full-space continuous prediction and three-dimensional visual expression of the rockburst risk can be realized, the fragmentation limitation of traditional local monitoring is overcome, and the accuracy and decision-making efficiency of disaster early warning are remarkably improved.

The invention discloses a network element data acquisition method and device. The method comprises the following steps: receiving network element information of target network element equipment; a prediction model is adopted to analyze network element information of the target network element equipment to obtain a data acquisition strategy of the target network element equipment, the prediction model is obtained based on fusion feature training, fusion features are determined according to discrete parameter features and continuous parameter features, and the discrete parameter features comprise a manufacturer strategy inheritance relation graph, a manufacturer strategy inheritance relation graph and a manufacturer strategy inheritance relation graph; the manufacturer strategy inheritance relation graph is used for representing an acquisition strategy inheritance relation between network element devices of different manufacturers, and the continuous parameter characteristics are used for representing a time sequence dependency relation between parameters; and collecting data of the target network element equipment according to the data collection strategy of the target network element equipment. The method provided by the invention at least solves the technical problem of low acquisition success rate caused by low matching degree between the network element data acquisition strategy and the network element in the related technology.

The application discloses a method for estimating parameters of a water acoustic double-spreading channel based on Newton orthogonal matching pursuit, which comprises the following steps: first, frame synchronization is realized by using the cross-correlation peak of an LFM signal and a received signal; second, the coarse estimation value of a Doppler coefficient is obtained on a discrete time-frequency grid by using the autocorrelation characteristics of an m sequence pilot and a blurring function method, the multipath time delay of a channel is detected by using the matching filter peak value of a frequency offset compensated signal, and a coarse estimation result of the channel is obtained; third, the Newton orthogonal matching pursuit algorithm is used to perform Newton iteration optimization in a continuous parameter space, and the super-resolution off-grid estimation of the Doppler coefficient of a first data block is realized; and finally, an iterative block tracking strategy is proposed, the compensation of a latter block is initialized by using the estimation value of a former block, the time-varying Doppler coefficient is updated by using the feedback of an equalized signal, and the dynamic tracking of the time-varying Doppler coefficient is realized. The application significantly reduces the root mean square error of Doppler estimation and has high calculation efficiency.

The application relates to the technical field of electronic signal processing, and discloses an anti-interference design method based on multi-element mixed electromagnetic interference, which establishes a mixed model of a useful signal static sparse dictionary and a parameterized atomic generating function of an interference signal; initial estimation of interference parameters is obtained by utilizing discrete anchor point dictionary matching; a joint optimization objective function containing a data fidelity term and a sparse constraint term is constructed; initial estimation values are used for initialization, and alternating iteration solving is executed based on a trust region constraint; useful signal sparse coefficients, interference parameter sets and amplitudes are alternately updated by minimizing the objective function, wherein the interference parameters are updated in a multi-element continuous parameter space containing time delay and frequency shift based on a trust region method to adaptively match waveform deformation; and finally, the useful signal is reconstructed according to the converged sparse coefficients and the static dictionary and is output. The application solves the problem of mixed interference separation under multi-system coexistence, and improves the parameter estimation precision and the reliability of algorithm convergence.

The application discloses a NiZn high-frequency inductor distributed capacitance fast calculation method based on a PSO-BPNN algorithm, and comprises the following steps: a two-dimensional axisymmetric electric field finite element model of a magnetic core inductance of a NiZn high-frequency inductor is established, and sensitivity analysis is performed to screen out key influence factors; a hybrid sampling strategy of discrete parameter layering and continuous parameter Latin hypercube sampling is adopted to generate multi-parameter samples meeting structure constraints, form a distributed capacitance data set, and train a NiZn high-frequency inductor distributed capacitance fast prediction model based on a particle swarm optimization back propagation neural network; and the key influence factors of the NiZn high-frequency inductor to be calculated are input into the trained prediction model, and corresponding total distributed capacitance, winding capacitance and magnetic core capacitance are output. The application effectively reduces the simulation calculation cost by combining the hybrid sampling strategy with the finite element simulation to construct a high-precision sample data set and by relying on the PSO-BPNN algorithm to establish a parameter intelligent prediction model, and meets the engineering requirements of high-frequency inductor fast design and parameter optimization.

The invention discloses a flame image recognition and feature extraction method and system, and belongs to the field of engine testing. The method comprises the following steps: performing background noise reduction and boundary enhancement preprocessing on an engine combustion flame image; a preliminary mask is generated by adopting an adaptive threshold method, and fine segmentation is realized in combination with a U-Net network; tracking flame movement by using a background subtraction method and an optical flow method, and analyzing the propagation speed and direction; three-dimensional reconstruction is realized based on MonoDepth2 monocular depth estimation and binocular vision parallax calculation, and a flame space position and an external cube are determined; constructing a convolutional neural network model by taking ResNet-50 as a backbone network, and respectively predicting continuity parameters and discrete parameters through regression branches and classification branches; and fusing the flame area, the speed, the color histogram and the edge features, and finishing final recognition through an SVM (Support Vector Machine) classifier. According to the invention, automatic flame identification, three-dimensional positioning and multi-feature parameter extraction are realized, and manual intervention is reduced.

The invention discloses a digital twin reconstruction method and system for power substation equipment. The method comprises the following steps: establishing an in-station unified coordinate system, a three-dimensional point cloud of a timestamp sequence, infrared thermal imaging, hyperspectral imaging and visible light image data; obtaining the surface reflectivity based on the hyperspectral imaging data, and calculating the band-pass effective emissivity according to the band-pass response of the infrared camera; according to the band-pass effective emissivity in combination with the infrared thermal imaging data, performing inversion on the temperature of the target equipment to obtain a correction temperature; according to the three-dimensional point cloud data, geometric constraints of target equipment are described by continuous parameters, and an equipment microparameterization template is constructed; establishing a projection relation from the three-dimensional point cloud to the two-dimensional image according to the internal reference and the external reference of the camera; according to the projection relation, on the basis of the microparameterized template, correcting the temperature, constructing a joint objective function and carrying out joint optimization; and instantiating the three-dimensional model of the target equipment by using the optimized continuous parameters, binding multi-modal data, and outputting a target equipment level digital twin instance.

The invention provides a simulation configuration generation method and system based on a physical-numerical constraint feasible region. The method comprises the following steps: constructing a unified simulation parameter vector according to a continuous parameter set and a discrete parameter set; calculating a key dimensionless physical quantity according to the continuous parameter set; constructing a physical consistency constraint set and a numerical stability constraint set, and constructing a feasible region according to the physical consistency constraint set and the numerical stability constraint set; generating an initial candidate parameter vector from a natural language or a semi-structured engineering demand input by a user through semantic analysis and parameter extraction; judging whether the initial candidate parameter vector belongs to a feasible region or not; if yes, generating a simulation configuration file; if not, executing a hierarchical projection correction strategy; intelligent generation of simulation configuration is achieved, the success rate of one-time starting is remarkably increased, the number of manual parameter adjustment times is reduced, dependence of simulation analysis on personal experience of engineers is reduced, and efficient and reliable technical support is provided for numerical simulation of complex engineering problems.

The invention relates to a method (100) for producing a gas analysis device (10) having at least one separating device (12) and a plurality of pneumatic modules (20). The method (100) comprises a first step (110) in which at least one target parameter (62) for the gas analysis device (10) to be produced is preset and a plurality of basic aerodynamic structures (35) are provided. In a second step (120), a plurality of continuous and discrete parameters (42, 44) of the basic aerodynamic structure (35) are provided. A basic configuration (50) is generated from the basic aerodynamic structure (35), said basic configuration being determined by changing (55) the discrete parameter (42). In a third step (130) of the method (100), candidate aerodynamic structures (60) are determined from each basic configuration (50), at least one consecutive parameter (44) of each basic configuration (50) being varied by means of an optimization algorithm (72). Furthermore, in a fourth step (140), a candidate pneumatic structure (60) is selected as a function of the desired value (64) of the target parameter (62) and output to the user and / or data interface. In addition, the gas analysis device (10) is produced on the basis of the selected candidate pneumatic structure (66). The invention also relates to a corresponding computer program product (70), a gas analysis device (10), a simulation method (200) and a simulation program product (80).

This invention discloses a method and system for 3D reconstruction and neural rendering of large-scale scenes. The method includes the following steps: S1, constructing a 3D scene representation model based on anchor points; S2, training the 3D scene representation model based on multi-view images, the training process including the following sub-steps: S21, periodically calculating the generation efficiency score of each anchor point and removing anchor points with generation efficiency scores lower than a dynamic threshold; S22, using a global codebook to perform vector quantization on the parameter set of the anchor points, mapping the continuous parameter set to discrete codebook indices; S3, rendering using the trained 3D scene representation model, adjusting the opacity of each Gaussian primitive generated by the anchor point according to the viewing distance from the anchor point to the camera through a modulation function to reduce the rendering computation of distant areas, thereby significantly reducing the model storage size and improving the rendering speed without losing visual quality.

The application provides a radar jamming decision and parameter optimization method and device based on hierarchical reinforcement learning, which comprises the following steps: constructing a radar countermeasure model; according to the radar countermeasure model, establishing state-action samples of radar working modes and jamming styles; according to the collected samples, an outer Q-Learning network obtains an optimal jamming strategy; an environment of an inner DDPG network is constructed, and the jamming strategy obtained by the outer layer is mapped to the inner DDPG network; an inner DDPG network model is constructed, the optimal jamming strategy is pulse parameter optimized by selecting actions on a continuous parameter space; an interference effect evaluation algorithm based on AHP-TOPSIS is used to evaluate the interference effect, and the interference effect evaluation result is used as environment feedback to update the radar countermeasure model; the application is suitable for cognitive jamming decision and parameter optimization in radar electronic warfare, the interference effect is evaluated by using the evaluation algorithm based on AHP-TOPSIS, and then the jamming decision and parameter optimization are carried out by using the model based on hierarchical reinforcement learning.

The invention relates to the technical field of wastewater treatment modeling and intelligent optimization, and discloses a wastewater treatment model parameter calibration method based on fuzzy logic and reinforcement learning, and the method mainly comprises the following steps: firstly, obtaining a state defined by an error between model prediction and actual observation; secondly, selecting a discrete action according to the state by utilizing a reinforcement learning module; thirdly, converting the discrete action into an accurate and continuous parameter adjustment action through a fuzzy logic module; and finally, updating model parameters by applying the adjustment action, and updating the reinforcement learning module according to environment feedback, thereby forming a closed-loop iterative optimization process. According to the method, the global search capability of reinforcement learning is combined with the fine reasoning capability of fuzzy logic, so that efficient, automatic and multi-target collaborative calibration of the parameters of the wastewater treatment model is realized, and the accuracy and practicability of the model are remarkably improved.