57 results about "Coefficient optimization" patented technology

The invention discloses a simulation optimization method for a contact interface, which comprises the following steps of: initializing an input parameter which comprises a friction coefficient # imgabs0 # between collision body contact interfaces; motion state solving: according to the initialized # imgabs 1 #, solving the mutual friction force # imgabs 2 # between the collision bodies, the friction force impulse # imgabs 4 # caused by # imgabs 3 # and the tangential acceleration # imgabs 5 # caused by friction, and updating the positions and the speeds of the collision bodies by utilizing the friction force impulse # imgabs 6 # and the tangential acceleration # imgabs 7 #; friction coefficient optimization is conducted, specifically, the friction coefficient # imgabs8 # is optimized through a loss function, and the friction coefficient # imgabs9 # is obtained; performing motion state solving and friction coefficient optimization again by using the friction coefficient # imgabs10 # to obtain a friction coefficient # imgabs11 #, performing motion state solving and friction coefficient optimization again by using the friction coefficient # imgabs12 # to obtain a friction coefficient # imgabs13 #, and the like until the loss function is converged to a minimum value. According to the method, simulation precision and calculation efficiency can be considered at the same time.

The invention belongs to the technical field of blade ring structure optimization design of composite materials, and particularly relates to a structure optimization design method of a titanium-based composite material integral blade ring, which comprises the following steps of: analyzing and calculating an equivalent elastic constant of a titanium-based composite material on a micro-scale, and performing secondary development on Abaqus software by using a Python language on a macro-scale to obtain an equivalent elastic constant of the titanium-based composite material integral blade ring; and then compiling a static analysis plug-in of the integral blade ring structure, and finally realizing optimal design of the blade ring structure by utilizing an automatic plug-in. The method has the advantages of being simple, easy to operate, high in efficiency and the like, the static strength reserve coefficient-optimization parameter curve is formed by carrying out automatic finite element simulation of various integral blade ring structural forms, and the further obtained optimization conclusion provides effective guidance and basis for the structural forms of the integral blade ring.

The invention discloses a parameter self-adjusting robust active noise control method, and relates to the technical field of noise control. The method comprises the following steps: acquiring a sound pressure signal in real time, processing the sound pressure signal to form a discrete noise signal as a reference signal, and constructing a dynamically updated reference signal sequence; constructing a filter and generating a weight coefficient thereof, and filtering the reference signal to generate an output signal; processing an output signal of the filter through a secondary path to generate a de-noising signal, collecting a residual signal, feeding back the residual signal to the filter, providing error feedback for self-adaptive iteration of the filter, and driving a weight coefficient of the filter to be optimized and updated; determining an optimal parameter combination for the generalized robust adaptive loss function to realize adaptive optimal determination; and updating a filter weight coefficient vector. According to the method, through organic combination of a self-adaptive parameter adjustment mechanism and a robust control strategy, the fast convergence characteristic, the low steady-state error and the high impact resistance are achieved, and the noise suppression efficiency in the complex environment is remarkably improved.

The invention discloses a bulk cargo intelligent logistics scheduling method and system based on artificial intelligence. The method comprises the steps that firstly, bulk cargo transportation tasks are acquired, and to-be-scheduled task features are extracted through a task feature extraction network; obtaining transport capacity attribute data of to-be-selected transport capacity, and generating to-be-selected transport capacity features through the transport capacity feature extraction network; screening the optimal transport capacity of which the scheduling priority coefficient is higher than a reference value, and extracting the characteristics of the optimal transport capacity; optimizing the optimal transport capacity feature based on the adaptation coefficient of the task feature and the optimal transport capacity feature; and determining a final scheduling transport capacity and distributing tasks through matching of the optimization features and the to-be-selected transport capacity features. According to the design, multi-dimensional feature extraction of tasks and transport capacity is realized by utilizing an artificial intelligence network, the adaptation precision is improved in combination with a dynamic optimization mechanism, the problems of low matching efficiency and poor real-time performance of traditional scheduling are solved, and the intelligent scheduling level of bulk cargo logistics is improved.

The invention relates to the technical field of intelligent water affairs, and provides a water supply pipe network leakage monitoring method comprising the following steps: collecting pipe network multi-source data and pre-processing; constructing a pipe network directed graph topological structure, inputting data into a space-time graph convolutional neural network to extract space-time correlation characteristics, and predicting a dynamic reference pressure value and a flow value of each node in combination with environment temperature and historical water consumption; calculating a residual error between an actual monitoring value and a reference value, dynamically determining an adaptive threshold value based on historical residual error statistical characteristics, and judging that leakage occurs when the residual error continuously exceeds a limit; finally, the pipe network hydraulic micro model is called for reverse evolution, the virtual leakage amount of each pipe section is calculated through a leakage coefficient optimization algorithm, and the pipe section with the maximum value is determined as the leakage point. According to the method, deep learning and a hydraulic mechanism are fused, the anti-interference capacity is high, the false alarm rate can be effectively reduced, and early warning and accurate positioning of tiny leakage are achieved.

The invention discloses a method for adjusting dynamic reflection coefficients of double intelligent reflecting surfaces based on deep reinforcement learning. At present, the complexity and dynamic change of the environment are important factors influencing intelligent reflecting surface phase regulation and control, and long-term phase regulation and control are difficult in the dynamic and complex environment. According to the method, on the basis of deep reinforcement learning (DRL), continuous adjustment is carried out through feedback of environment information, an optimal strategy is achieved, and adjustment of the phase and the amplitude of the double-intelligent-reflection-surface unit is carried out. According to the method, a flexible, efficient and automatic method can be provided for a double-intelligent-reflection-surface wireless communication system to achieve reflection coefficient optimization control, especially in the face of constantly changing environments and complex system dynamic states. In this way, the energy efficiency ratio of the wireless network can be improved, the coverage range can be expanded, and the user experience can be improved. In addition, the convergence rate is improved by carrying out distributed training on the method provided by the invention. Simulation results show that the method of the invention is effective and feasible, and the method of the invention has a fast convergence rate.

A combustion mechanism simplification and optimization method based on a two-stage deep neural network algorithm belongs to the field of combustion dynamics simulation and comprises three stages of component simplification based on a first-stage deep neural network, deep reaction simplification and coefficient optimization based on a second-stage deep neural network. Firstly, a deep neural network model with component retention states and initial conditions as input and combustion characteristics as output is constructed, and efficient screening and gradual simplification of mechanism components are achieved. Secondly, deeply simplifying a reaction path, setting a time scale threshold value, eliminating non-key rapid reactions, and constructing a reaction number controlled simplification mechanism; and finally, constructing a deep neural network model taking the key reaction kinetic parameters as input, and performing global optimization of mechanism parameters in combination with a genetic algorithm to improve the prediction precision of the mechanism on key combustion performance indexes. According to the method, a combustion mechanism with high simplification and high fidelity can be realized, and the method is suitable for rapid calculation in complex three-dimensional combustion simulation.

The invention relates to the technical field of grinding processes, and provides a planetary reducer gear shaft machining method capable of reducing machining errors, which comprises the following steps: setting an initial value of a PI coefficient, adjusting grinding parameters of grinding treatment by using a PID control algorithm, and collecting surface roughness before and after the grinding treatment and a vibration value of the grinding treatment; determining the machining effect stability degree of the grinding treatment and the predicted value of the surface roughness degree of the next grinding treatment; a PI coefficient reference value, vibration-grinding precision correlation and PI coefficient parameters of grinding treatment are determined; and according to the PI coefficient reference value of grinding treatment, the PI coefficient value, the vibration-grinding precision correlation and the PI coefficient parameter, a PI coefficient optimization value is determined, and planetary reducer gear shaft machining is achieved according to the PI coefficient optimization value. Cutting machining errors can be reduced, and the precision of the planetary reducer gear shaft is improved.

The invention discloses a meta-heuristic adaptive robust azimuth gyro drift calibration method, and provides a technical scheme for improving the azimuth gyro drift calibration precision on a software level, and the method comprises the following steps: establishing a Kalman filtering model of an augmented measurement equation based on an angular rate; in combination with a variational Bayes-based measurement noise variance matrix adaptive estimation method and an improved sine and cosine heuristic multi-correction coefficient optimization algorithm, drift calibration of multi-channel measurement error decoupling and multi-channel error correction is realized, so that the drift calibration of multi-channel measurement error decoupling and multi-channel error correction can be realized while hardware cost and system complexity are not increased. Therefore, the method is very suitable for large-scale application and popularization.

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.

The present invention belongs to the field of laboratory environment control technology, and in particular relates to an artificial intelligence-based intelligent monitoring and regulation method for laboratory temperature and humidity. The method first establishes laboratory temperature and humidity PID control and designs temperature and humidity PID controllers. Next, an objective function is constructed, integrating indicators such as overshoot and rise time and performing normalization processing, and optimizing temperature and humidity regulation performance by adjusting weight coefficients. Then, an improved starfish optimization algorithm (SFOA) is used to optimize the proportional, integral, and differential coefficients of the PID controller through parameter setting and population initialization. Finally, a simulation is performed to obtain a response curve. Compared with traditional PID control technology, the present invention can more efficiently and accurately regulate temperature and humidity, effectively improving the stability of the experimental environment and the reliability of experimental results.

The invention discloses a cascade hydropower optimization scheduling method and device based on regional adjustable resources, and relates to the technical field of power systems, and the method comprises the steps: collecting the operation data of cascade hydropower stations and regional combined adjustable resources; constructing a master-slave game model of the upstream and downstream regions; constructing a three-layer optimization model comprising a compensation coefficient optimization model, an upstream optimization scheduling model and a downstream optimization scheduling model; solving the three-layer optimization model, and dynamically updating a compensation coefficient and upstream and downstream scheduling strategies; and dynamically adjusting a water discharge strategy of the upstream power station and a regulation and control strategy of the combined adjustable resources based on a solving result. According to the cascade hydropower optimization scheduling method based on the regional adjustable resources, the regional adjustable resource joint income models of the upstream region and the downstream region are constructed, the income increasing compensation coefficient is introduced, reasonable allocation of benefits of the upstream region and the downstream region is achieved, and therefore the economic operation efficiency and the power supply stability of the whole drainage basin are improved.

The invention relates to the technical field of eddy current sensors and voltage calibration. The eddy current sensor calibration method for the small-diameter metal ball comprises four parts of coordinate system establishment, calibration data acquisition, calibration sample set construction and calibration equation coefficient optimization. Through innovative coordinate system definition and position parameterization and in combination with an intelligent optimization algorithm, a voltage-position relation model capable of accurately representing the actual effective measurement axis of the small-diameter metal ball center deviation sensor is successfully established; and a nonlinear measurement error caused by sphere center eccentricity, high surface curvature and axis deviation of the sensor is effectively compensated.

According to the traffic equipment data processing method and device provided by the embodiment of the invention, a multi-dimensional data preprocessing mechanism is innovatively constructed, and standardized representation of characteristics such as equipment types and geographic positions is realized through abnormal value identification and one-hot coding. And designing a classification model based on a multi-label random forest, and establishing a feature division and decision tree construction strategy in combination with an information gain criterion and Gini coefficient optimization. And a voting integration mechanism is introduced, and accurate recommendation of illegal configuration is realized through fusion of multi-tree classification results. According to the method, the defects of the traditional technology in the aspects of data processing, classification modeling, result generation and the like are effectively overcome, and the accuracy and reliability of traffic equipment data processing are remarkably improved.

The invention belongs to the technical field of quality prediction, and particularly relates to a cold heading steel ball quality prediction and optimization method based on big data analysis. Firstly, cold heading pressure, steel chemical components and other data are collected; and constructing a quality evaluation system including indexes such as dimensional precision. Multi-target prediction is realized by adopting improved Pareto, a dominating relation is improved, preference coefficients are introduced to regulate and control target function importance, real number coding and specific crossover and mutation operation overcome traditional problems, and a solution space is explored. And an optimal scheme is selected by combining model solution optimization with production requirements. And comparing and analyzing actual and predicted quality data after application, adjusting the model when the error is large, re-evaluating the coefficient, optimizing algorithm parameters and performing optimization. The improved steel ball is improved in dimensional precision, enhanced in plasticity and better in comprehensive performance although the tensile strength is reduced, the defects of traditional production quality control are effectively overcome, and the product quality and the production efficiency are improved.

The invention relates to the technical field of new energy power generation, in particular to a safety boundary constraint-based frequency controller parameter optimization and control coefficient optimization method, which comprises the following steps of: constructing a RoCoF safety boundary by taking system inertia as an influence factor; analyzing influence factors of frequency change of a sending end, and setting a frequency segmentation interval of actions of the wind power plant and the interconnection region; optimizing a control coefficient of the frequency controller, and modulating the optimized coefficient to form an active power modulation instruction; detecting the frequency change of the wind power plant and the frequency controller by setting the value of the frequency segmentation interval; validity verification is carried out through simulation verification, and the influences of different control strategies on the dynamic characteristics of the system frequency are compared. The method has the beneficial effects that the response capability of the wind power plant to the frequency change of the power grid is enhanced by optimizing the control coefficient of the wind power plant; by dynamically adjusting the control coefficient, the wind power plant can provide more accurate active power support according to the real-time frequency state of the power grid.

The application discloses a kind of based on depth reinforcement learning's double intelligent reflector dynamic reflection coefficient adjustment method.Current complexity and dynamic change of environment are important factors to influence intelligent reflector phase control, in dynamic and complex environment, long-term phase control is difficult thing.The method of the present application is based on depth reinforcement learning (DRL), constantly adjust through the feedback of environmental information, reach an optimal strategy, to carry out double intelligent reflector unit phase and amplitude adjustment.The method of the present application can provide a flexible, efficient, automatic method for double intelligent reflector wireless communication system to realize reflection coefficient optimization control, especially when facing changing environment and complex system dynamics.In this way, the energy efficiency of wireless network can be improved, the coverage range is expanded and the user experience is improved.In addition, the convergence rate is improved by distributing training for the method of the present application.Simulation results show that the method of the present application is effective and feasible, and the method of the present application has a faster convergence speed.

This invention discloses a dynamic automatic correction system for the nozzle area of ​​a steam turbine regulating stage, comprising: a detection module for real-time acquisition of operating parameters of the steam turbine regulating stage, including the pressure difference across the regulating valve, main steam flow rate, reheat steam temperature, unit power, and speed; and a control module, which is communicatively connected to the detection module and incorporates a load rate calculation model, a dynamic correction algorithm, and secondary correction logic. This invention ensures that the regulating valve opening remains within the high-efficiency range by dynamically correcting the nozzle area, combined with secondary correction logic. This verifies that throttling losses are reduced under low load conditions, directly reducing energy consumption and improving the unit's operational economy. Simultaneously, through high-frequency data acquisition, dynamic coefficient optimization, and flexible correction cycles, it achieves a "second-level" correction response. The specification clearly states that it can quickly meet the peak-shaving needs of the power grid and solve the problem of nozzle area decoupling from operating conditions under load fluctuations.

The application relates to the grinding process technical field and discloses a planetary reducer gear shaft machining method capable of reducing machining errors, which comprises the following steps: setting an initial value of a PI coefficient, adjusting grinding parameters of grinding treatment by using a PID control algorithm, collecting surface roughness before and after grinding treatment and vibration values of the grinding treatment; determining a processing effect stability degree of the grinding treatment and a predicted value of surface roughness of the next grinding treatment; determining a PI coefficient reference value of the grinding treatment, vibration-grinding precision correlation and a PI coefficient parameter; determining a PI coefficient optimization value according to the PI coefficient reference value of the grinding treatment, the value of the PI coefficient, the vibration-grinding precision correlation and the PI coefficient parameter; and realizing the planetary reducer gear shaft machining according to the PI coefficient optimization value. The application can reduce cutting machining errors and improve the accuracy of the planetary reducer gear shaft.

The invention discloses a thermal power generating unit primary frequency modulation control method based on economical efficiency and stability optimization, which belongs to the technical field of power system control, and is technically characterized by comprising the following steps: step 1, detecting power grid frequency fluctuation, and when the power grid frequency fluctuation exceeds a set normal range, executing step 2; controlling each device to perform primary frequency modulation according to the magnitude of power grid frequency fluctuation and the SOC state of the energy storage device; 2, carrying out dynamic droop coefficient optimization on the energy storage device, and carrying out system economy calculation and stability calculation to obtain an optimal value of a target function; and step 3, setting a dynamic weight adjustment mechanism which is used for dynamically adjusting the weights of economy and stability according to the magnitude of the power grid frequency fluctuation, setting constraint conditions and determining an optimal frequency modulation strategy by using a particle swarm optimization algorithm, and has the advantages of being capable of adjusting and calculating the weights and response strategies in real time according to the power grid frequency fluctuation and improving the frequency modulation accuracy. And the economical efficiency and the stability of the system are effectively improved.

The invention discloses a three-dimensional anisotropic medium time-space domain seismic forward modeling method and device, a medium and a product, and relates to the technical field of geophysical exploration forward modeling simulation, and the method comprises the steps: presetting an improved time high-order difference operator based on a preset improved space high-order difference operator; respectively discretizing a time derivative and a space derivative in the three-dimensional VTI dielectric acoustic wave equation; based on the three-dimensional VTI dielectric acoustic wave equation, simplifying the time discrete function and the space discrete function to obtain a simplified time-space domain frequency dispersion relational expression; solving the simplified time-space domain dispersion relational expression based on a target optimization algorithm to obtain an optimized time item high-order difference coefficient and an optimized space item high-order difference coefficient; and based on the optimized time item high-order difference coefficient, the optimized space item high-order difference coefficient, the time discrete function and the space discrete function, performing extrapolation solution on the three-dimensional VTI dielectric acoustic wave equation. According to the invention, the numerical precision in anisotropic wave field simulation can be effectively improved.

The application discloses a three-dimensional anisotropic medium space-time domain seismic forward method, device, medium and product, relates to the technical field of forward simulation of geophysical exploration, and comprises the following steps: based on a preset improved time high-order difference operator and a preset improved space high-order difference operator, respectively discretizing time derivatives and space derivatives in a three-dimensional VTI medium acoustic wave equation; based on the three-dimensional VTI medium acoustic wave equation, simplifying time-discrete functions and space-discrete functions to obtain a simplified space-time domain dispersion relation; based on a target optimization algorithm, solving the simplified space-time domain dispersion relation to obtain optimized time item high-order difference coefficients and optimized space item high-order difference coefficients; and based on the optimized time item high-order difference coefficients, the optimized space item high-order difference coefficients, the time-discrete functions and the space-discrete functions, extrapolating and solving the three-dimensional VTI medium acoustic wave equation. The application can effectively improve the numerical precision in anisotropic wave field simulation.

The invention relates to a method for optimizing the confidence coefficient of an aircraft simulation model, and provides a method for improving the confidence coefficient of a simulation model and solving the problem of data delay by combining a simulation system and aircraft transmission data in order to solve the problems that the confidence coefficient of the aircraft simulation model is not high and the online data transmission of an aircraft is delayed. Simulation output data corrected by the off-line network has the characteristic insufficiency phenomenon on real data, and the on-line network can carry out secondary correction on an off-line correction result by combining delayed transmission real data, so that the method disclosed by the invention combines two parts, namely an off-line correction network and an on-line correction network. A random forest algorithm and an MDA method are adopted to extract off-line network and on-line network features. The offline network adopts a bidirectional long short-term memory neural network (BiLSTM) network and a time attention mechanism to preliminarily correct simulation data errors; and carrying out secondary correction on the error between the offline correction result and the real data through an online algorithm FTRL.

The invention relates to a multi-user and rate maximization method based on Bayesian optimization. The method comprises the following steps: firstly, constructing a new user utility function, and constructing an inner-layer precoding optimization problem and an outer-layer scaling coefficient optimization problem by taking error vector amplitude minimization of a multi-user system as a target; and secondly, in an inner layer optimization problem, fixing a scaling coefficient, updating a precoding matrix by using a gradient descent method, and solving the inner layer problem through a weighted minimum mean square error frame. And finally, by using Bayesian optimization, minimizing the error vector amplitude sum of all users as a target optimization scaling coefficient, and determining a final optimal precoding matrix. According to the method, the system performance under the condition of a limited symbol set is improved, so that the algorithm shows better transmission quality in various channel environments and has higher practicability and popularization value, and a low-complexity and high-performance solution is provided for multi-user multiple-input multiple-output precoding in a wireless communication network.

The application provides a method for generating an in-situ stress field inversion of a grid, based on the superiority of GAN data enhancement, deep learning of the distribution of lateral stress coefficients, GAN analysis of lateral stress coefficients, fine tuning of ancient lateral stress coefficients by the introduced generative adversarial network, optimization of the present in-situ stress field, and meeting the needs of engineering analysis. The advantages of uniform design can greatly reduce the number of tests and keep the training samples uniformly dispersed. After each uniform design test, GAN training can be performed, the data of the test points are input into the GAN, and the optimized lateral stress coefficients can be obtained, the optimized ancient in-situ stress field can be obtained by calculation, and the present in-situ stress field can be obtained after nonlinear elastic-plastic excavation simulation.

The invention provides a machine pump bearing weak fault diagnosis method and system based on time-frequency self-adaptive segmentation. The method comprises the steps that collected vibration signals are converted and processed to obtain corresponding time-frequency distribution characteristics; quantitatively representing the adjacent correlation distribution relation of the time-frequency distribution characteristics, and realizing frequency domain interval division to obtain multiple parts of time-frequency distribution characteristics; determining fault characteristic frequency ratio indexes corresponding to time-frequency distribution characteristics of different parts by using a principal component analysis technology, and selecting sensitive components; and performing coefficient optimization processing by adopting a differential search algorithm, calculating to obtain a fault feature component, and extracting and diagnosing weak fault features of the pump bearing based on the fault feature component. By adopting the scheme, the problems of single theory, complex calculation and insufficient accuracy in the prior art can be solved, the weak fault diagnosis of equipment is realized by comprehensively utilizing the time-frequency adaptive segmentation technology and the frequency band coefficient optimization algorithm, the timeliness is good, the operation result is finer, and the recognition and diagnosis of the weak fault of the bearing are timely and reliably realized.

The invention discloses a dual-active bridge topology circuit and a constraint coefficient optimization modulation method thereof. The circuit comprises a direct current side circuit, an alternating current side circuit, a series inductor and a transformer, the direct current side circuit adopts a full-bridge structure and is electrically connected with the primary side of the transformer; the series inductor comprises a transformer leakage inductor and an external inductor and is electrically connected with the secondary side of the transformer; and the alternating current side circuit adopts a matrix half-bridge structure and is electrically connected with the secondary side of the transformer. According to the method, the correlation model of the constraint coefficient and the total loss of the system is systematically established, so that efficiency optimization in a full-working-condition and full-phase-angle range is realized.

The invention relates to the technical field of energy management, and discloses a smart energy optimization control method and system for a port, and the method comprises the steps: collecting the energy of a target port area, and obtaining a historical energy consumption data set of the energy; according to the historical energy consumption data set, constructing a nonlinear regression model for predicting a future energy consumption value of the energy; constructing a confidence interval based on the historical energy consumption data set; comparing the future energy consumption value with a confidence interval, and determining an abnormal energy consumption value; calculating an optimization coefficient for each data point of the abnormal energy consumption value, and determining an abnormal point corresponding to the abnormal energy consumption value and an abnormal degree of the abnormal point according to the optimization coefficient; and executing optimization control on the abnormal point according to the abnormal degree of the abnormal point. The future energy consumption demand is predicted more accurately, and the accuracy of anomaly detection is improved. According to the method, abnormal energy consumption is marked, and abnormal points are subdivided into early warning subintervals, normal subintervals and warning subintervals through coefficient optimization and subinterval division, so that the severity of anomalies can be distinguished.

The invention discloses a KAN-based network flow data zero-loss category forgetting method. The method comprises the steps of data processing, data division and forgetting category determination. Model training; formulating: converting the KAN into a formula; target calculation: calculating an optimized target; optimizing the target formula, and updating the coefficient of the target formula through a gradient descent method to realize zero loss forgetting; zero-loss forgetting testing is carried out, and whether zero-loss efficient forgetting is achieved or not is tested. According to the method, the memory of the intrusion detection model on the network flow data can be removed, the model is helped to forget knowledge related to polluted data in specific scenes such as poisoning attacks, the window period of the model is effectively shortened, and rapid recovery of the model is ensured. Specifically, zero-loss forgetting is carried out based on a KAN model, polluted network flow data is taken as a forgetting class, accurate class forgetting is realized by converting the KAN model into a specific formula, coefficient optimization is carried out by using a gradient descent algorithm aiming at a target formula, namely a formula corresponding to the forgetting class, and the forgetting class of the forgetting class is optimized while forgetting. The accuracy of the intrusion detection model in data retention is not affected, and zero loss forgetting in experiments is realized. The engineering implementation of the invention strictly follows the strict requirements of lightweight, real-time performance and accuracy of a network security defense system, and provides theoretical support and a technical scheme for constructing a credible intelligent detection system.

The invention belongs to the technical field of electric vehicle chassis control, and particularly relates to a distributed electric drive vehicle multi-target torque distribution control strategy based on DBO. The strategy adopts a hierarchical control framework: in an upper motion control layer, total driving torque is calculated through a PID (Proportion Integration Differentiation) controller, and additional yawing moment required for maintaining the stability of the vehicle is calculated based on a sliding mode control algorithm; in the lower torque distribution layer, a multi-objective optimization function including the tire load rate, the torque deviation and the motor energy consumption is constructed, four-wheel torque distribution is decoupled into a two-side front and rear axle distribution coefficient optimization problem, and a dung beetle optimization algorithm is introduced to perform optimization solution under the condition that road adhesion and motor characteristic constraints are met; and in the execution layer, the calculated final torque instruction is sent to the hub motor. The method can solve the problem that a traditional distribution strategy is difficult to give consideration to energy consumption and stability, and remarkably reduces the motor energy consumption and improves the driving smoothness while guaranteeing the vehicle driving safety.