27 results about "Coefficient optimization" patented technology

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 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 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.

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 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 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.

The invention provides a four-dimensional ultrasonic signal real-time enhancement method based on anisotropic multiphase decomposition and LTI constraint, and belongs to the field of medical image processing. The method comprises the following steps: constructing a space-time Laplacian pyramid and anisotropic multiphase decomposition, constructing a space-time multiphase filtering network based on dimension decoupling, performing LTI constraint solution based on transposer error minimization, performing cross-scale multiphase feature alignment based on Kronecker product, performing multiphase synthesis inverse rearrangement, and performing image reconstruction. The behavior of the system is described by using the pulse response in the signal processing theory, the shift characteristic introduced by multiphase decomposition is eliminated, the LTI strong constraint is introduced in the filter coefficient optimization stage, and the constraint forces convolution kernel parameters in the space-time multiphase filter network to form a specific complementary structure, so that the performance of the system is improved. According to the invention, different multi-phase components can counteract aliasing terms during synthesis, so that checkerboard artifacts are eliminated macroscopically, low-frequency structure direction information is ensured to accurately cover each high-frequency sub-pixel phase, and no phase deviation exists.

The invention provides a sub-codebook composite codebook generation method and system based on regional similarity clustering, and the method comprises the steps: obtaining a matrix training set based on a channel covariance matrix of a large-scale MIMO system; extracting an upper triangular element of each channel covariance matrix, separating a real part and an imaginary part, and constructing a high-dimensional covariance feature training set; pCA dimension reduction processing is carried out, and a K-Medoids clustering algorithm is combined with contour coefficient optimization and an optimal number of covariance mode clusters are identified; based on the sample density weight, distributing the total code word number to each cluster in the covariance mode cluster according to a proportion, and taking the total code word number as the exclusive code word number of each cluster; obtaining an exclusive sub-codebook corresponding to each cluster; and integrating the exclusive sub-codebooks of all clusters, and constructing a final composite codebook. By extracting the triangular elements on the covariance matrix and separating the real and imaginary parts, the channel covariance mode features are accurately captured, and the adaptive capacity of the codebook to complex propagation scenes with multipath reflection, shielding and the like is effectively improved.

The application relates to the technical field of eddy current sensors and voltage calibration. The eddy current sensor calibration method for small-diameter metal balls 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, combined with an intelligent optimization algorithm, the application successfully establishes an accurate voltage-position relationship model capable of accurately representing the small-diameter metal ball center deviating from the actual effective measurement axis of the sensor, and effectively compensates for the nonlinear measurement error caused by the ball center eccentricity, surface high curvature and sensor axis deviation.

The invention belongs to the technical field of wind power system control, and particularly relates to a reactive current adaptive control method and system of a wind power system, and the method comprises the steps: collecting the multi-dimensional operation data of the wind power system in real time, constructing a multi-dimensional state evaluation model based on an entropy weight method, and dividing the three operation levels of a stable state, a transition state and a disturbance state; aiming at different state levels, constructing a reactive current control coefficient optimization model by adopting a deep Q network (DQN), and outputting an optimal control coefficient by taking minimization of voltage deviation, converter loss and harmonic content as targets; combining the unit capacity ratio and the state adaptation coefficient to construct a collaborative distribution model, and calculating a reactive current reference value of each unit; and dynamic adjustment is realized through closed-loop control. The method solves the problems of single state evaluation, fixed control coefficient, lack of unit collaboration and neglect of equipment loss in the prior art, improves the voltage stability, operation economy and electric energy quality of the wind power system, and is suitable for a high-proportion wind power grid-connected scene.

This invention provides a method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor, relating to the field of permanent magnet motor design optimization technology. The method includes determining the range of values ​​for the parameters to be optimized in the permanent magnet torque motor; determining an optimization objective function based on the torque coefficient equation and torque ripple equation; minimizing the optimization objective function as the objective, using the range of values ​​as constraints, and obtaining the parameters to be optimized by training a surrogate model and performing multi-objective optimization. The apparatus executes the above method. The method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor provided by this invention can accelerate the optimization convergence speed while taking into account torque ripple changes.

The invention relates to a dual-wavelength digital holographic imaging method based on unwrapping coefficient optimization, and the method comprises the steps: retrieving an unwrapping coefficient of a wrapped phase obtained through the measurement of a first wavelength, and obtaining the wrapped phase of a second wavelength through the phase guidance of the unwrapped first wavelength; and with the minimum value of the square of the difference between the induced phase of the second wavelength and the measured wrapped phase of the second wavelength as a target, obtaining an unwrapping coefficient of the first wavelength meeting the condition through a least square method, and correctly reconstructing an unwrapped phase with an expanded measurement range. According to the method, the searching process of the dual-wavelength phase unwrapping coefficient is optimized, the optimal unwrapping coefficient is quickly determined, and high-precision and large-range phase reconstruction is realized. Meanwhile, the signal-to-noise ratio and the calculation efficiency are remarkably improved, and the method is suitable for efficient measurement of the microstructure three-dimensional morphology.

The invention discloses a laser interferometer high-precision filter coefficient generation method based on an FPGA, and belongs to the technical field of digital signal processing. The method comprises the following steps: designing an ideal floating point FIR filter coefficient according to a measurement requirement and carrying out initial quantization; constructing a cost function containing passband ripples and stopband attenuation weights; under the constraint of keeping linear phase symmetry, performing bit-by-bit perturbation and iterative search on integer coefficients, and searching an optimal integer coefficient set which minimizes a cost function; and generating a fixed-point coefficient file adaptive to the FPGA hardware resources. The invention provides a coefficient optimization strategy in a discrete space aiming at the problem that the performance of a filter is reduced due to the limited word length effect of an FPGA (Field Programmable Gate Array) in a laser interference measurement system. On the premise that FPGA hardware resource consumption is not increased, quantization noise is remarkably reduced, the stopband suppression degree and the signal-to-noise ratio of the filter are improved, and therefore the final measurement precision of the laser interferometer is improved.

The application is suitable for the technical field of copper wire, and particularly relates to a die matching coefficient optimization method for multi-pass drawing of bare copper wire, which comprises the following steps: a coefficient offset and a coefficient reference difference are respectively determined through a die matching reference parameter and real-time production parameters and the die matching reference parameter and a preset die matching target parameter, so as to obtain a coefficient actual difference of each pass of the bare copper wire; the intermediate quantity of the die matching reference parameter is introduced, instead of directly obtaining the coefficient actual difference through the real-time production parameters and the preset die matching target parameter, so as to improve the accuracy of the coefficient actual difference; and finally, a die matching coefficient optimization scheme of the bare copper wire is obtained through the coefficient actual difference and a preset extension threshold value. Through the above method, the problem that the die matching coefficient optimization scheme obtained by directly using the calculation logic of the target die matching coefficient minus the actual die matching coefficient has deviation and cannot accurately achieve the die matching target can be solved.

The application provides an affine power flow calculation method for a domain contraction type electrical comprehensive energy system, establishes an affine model of an electrical network, a gas network and a coupling element considering the uncertainty of distributed power output fluctuation and load change; around the network basic operating point under the determined performance flow, firstly, the state quantity is completely predicted based on network sensitivity analysis, and a compression coefficient optimization model is introduced to compress the domain of the predicted state quantity, so as to reduce the conservativeness of the state quantity solution. In addition, an electrical affine power flow column iteration method is provided, the power matching at the coupling unit is used to realize the power flow interaction in the calculation, the calculation sequence of the network in the complex coupling system does not need to be combed, and efficient and simple electrical-gas multi-power flow solving is realized.

The invention discloses a sounding rocket empennage aerodynamic optimization method and system based on a machine learning proxy model, and provides a proxy model optimization framework combining an electric eel foraging optimization (EEFO) algorithm and a kernel extreme learning machine (KELM) to solve the problems of high cost and low efficiency caused by traditional aerodynamic and structure optimization separation. CFD simulation data is obtained through a Latin hypercube method to train a KELM model, model parameters are optimized through an EEFO algorithm, and a multi-target optimization model is constructed to balance a resistance coefficient, the empennage volume and a lift coefficient. Optimization results show that the method can significantly reduce the resistance, moderately control the lift force loss and improve the lift-drag ratio, and an efficient optimization scheme is provided for the design of the sounding rocket empennage.

The invention relates to the technical field of safe broadcasting of an intelligent studio, in particular to a studio delay system, which comprises a multi-modal data acquisition module for synchronously acquiring audio and video, heart rate, network and interaction data in the studio under a unified time reference and packaging the data into synchronous data frames with timestamps; the risk and income quantitative evaluation module is used for calculating a comprehensive risk index based on the content, the personnel state and the system risk quantitative value, and calculating a participation income index in combination with the interaction rate and the sentiment analysis; the dynamic delay instruction generation module is used for deciding a target delay duration according to the risk and income index, and generating a control instruction after smooth transition; the instruction execution monitoring and optimizing module monitors the adjustment process in real time, evaluates the effect after completion, and optimizes the risk weight according to the effectiveness coefficient; according to the system, dynamic adaptive adjustment of time delay is realized, live broadcast safety, real-time performance and interactive experience are improved, and intelligence and reliability of the system are enhanced through a closed-loop optimization mechanism.

The invention relates to the field of wireless communication, in particular to an STAR-IRS enhanced symbiotic radio secret communication device and method, and the method comprises the steps: building an STAR-RIS assisted symbiotic wireless communication system model; constructing an optimization problem according to an ES protocol of the STAR-RIS, a system power constraint, a minimum user decoding SNR constraint and a reflection and transmission phase shift constraint of the STAR-IRS so as to maximize the safety rate of a reflection side; an alternating optimization algorithm is proposed to solve a system optimization problem, and the problem is decomposed into two sub-problems of BS beam forming optimization and STAR-RIS phase shift coefficient optimization. An active beam forming optimization problem of the BS is converted into an SDP problem to be solved by using SCA and SDR technologies; and then, adopting a PCCP method to effectively optimize the STAR-RIS phase shift, and popularizing the STAR-RIS phase shift to an MS scheme based on a penalty method.

The application provides a valve coefficient optimization calculation method, device and equipment and a storage medium. The method comprises the following steps: obtaining initial valve coefficients of valves in a dust removal system, an optimization strategy and a dust removal pipe network simulation model corresponding to the dust removal system, wherein the optimization strategy at least comprises a target function and a constraint condition; calculating the initial valve coefficients of the valves by using the target function and the constraint condition to obtain global optimization valve coefficients of the valves; inputting the global optimization valve coefficients into the dust removal pipe network simulation model for simulation calculation to obtain a simulation result set of the valves; constructing a machine learning model and training the machine learning model according to the simulation result set, so as to adjust valve openings of the valves by using the trained machine learning model. The initial valve coefficients are optimized, the accuracy of simulation calculation is improved, and the regulation and control effect of the dust removal system is ensured.

The invention relates to a tolerance analysis method and device based on a product assembly dimension chain. The method comprises the steps that an assembly tolerance graph and an assembly directed graph are acquired; generating an assembly dimension chain according to the assembly tolerance diagram; constructing a Jacobian spinor model based on the assembly dimension chain, and solving a tolerance contribution degree; performing semantic reasoning on the assembly dimension chain to obtain a tolerance transfer coefficient; performing optimization solution through a cuckoo search algorithm according to the assembly dimension chain, the tolerance contribution degree and the tolerance transfer coefficient to obtain a tolerance distribution scheme; and performing virtual assembly and tolerance analysis based on digital twinning according to the assembly directed graph and the tolerance distribution scheme to obtain an assembly success rate, and optimizing and solving the tolerance distribution scheme by introducing a cuckoo search algorithm in combination with a tolerance contribution degree and a tolerance transfer coefficient obtained by semantic reasoning. And then virtual assembly and tolerance analysis based on digital twinning are carried out to determine the assembly success rate, so that the accuracy and efficiency of tolerance analysis can be effectively improved, and the assembly efficiency and success rate are further improved.

The invention discloses a robust energy-saving method for auxiliary simultaneous information and energy transfer of an active intelligent surface. The method comprises the following steps: establishing a nonlinear energy collection model and a statistical channel error model of an active intelligent surface link; constructing a robust optimization problem including an outage probability constraint and an active intelligent surface power constraint by taking minimization of base station transmitting power as a target; an improved optimization problem is established; an alternative optimization framework is adopted, and the improved optimization problem is decomposed into two sub-problems of base station transmitted beam forming optimization and active intelligent surface coefficient optimization; and alternately updating the two sub-optimization problems until the objective function converges to obtain a solution of the optimization problem. According to the method, random channel state errors and nonlinear circuit characteristics in an actual system are comprehensively considered, on the premise that the achievable rate of an information decoder, the outage probability of the receiving power of an energy receiver and the active intelligent surface power are met, the transmitting power of a base station is remarkably reduced, the network energy efficiency is improved, and good industrial practicability is achieved.

This invention provides a method for constructing a droop coefficient optimization model and an online adjustment method for the droop coefficient, relating to the field of power electronics technology. The method for constructing the droop coefficient optimization model includes: calculating the operating point and corresponding droop coefficient under different operating conditions based on the parameter constraints of the power grid interconnection system; obtaining the impedance of the power grid interconnection system and calculating the corresponding weakest pole based on the impedance and droop coefficient; training an initial static optimization model and an initial stability judgment model using the impedance, operating point, droop coefficient, and weakest pole as a dataset to obtain a static optimization model for the droop coefficient and a stability judgment model; and constructing an initial impedance model and adjusting its parameters using the Kalman filter algorithm to obtain an impedance identification model. The beneficial effects of this invention are: expanding the applicability of the droop coefficient optimization model, and enabling online optimization and adjustment of the droop coefficient under various conditions, thereby achieving stable operation of the power grid interconnection system.

The invention discloses a gaseous ecological network diffusion coefficient optimization method and system, and relates to the technical field of fluid mechanics, and the method comprises the steps: S1, constructing an early warning model; s2, acquiring data information in real time; s3, the turbulence suppression module performs turbulence suppression processing by using the data information; s4, analyzing the flux, and triggering an alarm according to the flux and the current Reynolds number; s5, optimizing a critical Reynolds number Recritnew and a damping length scale by using a loss function, and updating a diffusion coefficient; s6, the steps S2-S5 are cyclically repeated; a diffusion coefficient is updated through a fluid mechanics regularization theory, a'turbulence suppression-biological damping-network feedback 'ternary coupling mechanism is established, and accurate diffusion simulation and dynamic early warning under a high Reynolds number working condition are realized.

The invention relates to the field of electric energy metering, and particularly discloses a variable fractional delay filter based on a symmetric structure and a weighted least square method, and the filter comprises a signal parameter pre-calculation unit which is used for providing a delay parameter for the filter; the symmetric structure FIR coefficient construction unit is used for calculating a symmetric FIR coefficient framework; the WLS coefficient optimization unit is used for calculating a fixed-point optimization coefficient matrix; the phase shift execution unit is used for realizing integer delay of the current signal through a cache length and realizing fractional delay compensation of the voltage signal through a VFD filter, and the integer delay and the fractional delay compensation cooperate to complete'integer + fractional 'joint phase shift; the self-adaptive calibration and frequency tracking unit is used for calculating an angular difference between the voltage and the current; and adding the calculated angular difference with an original phase shift angle to obtain an updated phase shift angle, and when it is detected that the absolute value of the difference value between the current fundamental frequency and the historical fundamental frequency is larger than a fundamental frequency fluctuation threshold value, synchronously updating the time delay integer part, the decimal delay and the current path signal cache length.