46results about How to "High solution accuracy" patented technology

The invention belongs to the technical field of steering pull rod design, and particularly relates to a steering pull rod mechanism design method based on analytic geometry and a steering pull rod mechanism. The steering pull rod mechanism is simplified into a plane connecting rod mechanism, and four key hinge points are marked; establishing a coordinate system by taking P1 as an original point; the length, the initial angle and the P4 coordinate of the front steering swing arm are obtained; calculating two coordinates of a P2 point when the front steering swing arm rotates to a limit position; generating P2 ''and P2'' ''points by rotating P2 'P4 and P2'' P4 line segments; and connecting the line segments and constructing perpendicular bisectors, and determining the position of a P3 point by solving the intersection point of the perpendicular bisectors. According to the method, the steering pull rod mechanism is designed through analytic geometry and coordinate transformation, the accuracy of hinge point positioning is improved, and structural deviation caused by empirical estimation is reduced; the design complexity is reduced through standardization steps, the development period is shortened, the motion coordination of the steering system is optimized, and the method is suitable for the field of automobile chassis and the like needing high-precision motion control.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The invention provides a visual guidance method and system for a large rocket cabin milling robot, and the method comprises the steps: 1, constructing a visual measurement system which comprises a hybrid robot, an area array structured light camera, a visual target, a target ball base, a product installation platform and a laser tracker, completing the system calibration, enabling the visual target to be installed on the target ball base, and enabling the visual target to be installed on the target ball base; the target ball base is fixed on the product mounting platform, and the laser tracker is used for calibrating the position of the target ball base; 2, collecting an image of a visual target through the area array structured light camera, analyzing the coordinates of the target, and resolving the relative pose of the robot relative to a product mounting platform, so as to determine the position of a theoretical cutting point according to a product design model; and 3, the area array structured light camera is used for collecting the three-dimensional shape of the surface of the to-be-machined product, the normal direction of the to-be-machined surface is calculated, and the direction of the tool is corrected according to the normal direction.

The application discloses an aperture measurement method and device based on distortion compensation and self-adaptive mean blur, and comprises the following steps: S1, performing binaryzation processing on collected images, correcting the distortion, then performing smoothing processing on the images and extracting a target region; S2, performing edge detection on an aperture target in the target region to detect pixels of an aperture edge; S3, detecting the aperture edge pixels to determine contour pixels of the aperture edge, then comparing the contour pixels with a threshold value to remove useless pixels and identify and acquire aperture contour features; and S4, based on the aperture contour features, using a least square method to fit a circle to calculate aperture size parameters. The application can realize non-contact and high-precision measurement of the aperture.

The invention discloses an airborne bistatic radar platform error compensation and target positioning method and system. The method comprises the following steps: constructing a measurement model for a bistatic radar system; the measurement model comprises a platform position motion model, a target position motion model, an observation equation of a platform to a target and a navigation error model of the platform; based on the measurement model, performing linear fitting on each platform position measurement and target position measurement by using a linear fitting model, estimating navigation error parameters of each platform according to fitting results, and correcting platform motion parameters according to estimated values of the navigation error parameters; and based on the corrected platform motion parameters, constructing a linearized target positioning model in combination with multi-platform observation data, and estimating the motion parameters of the target by using a weighted least square method to realize target positioning. Under the condition that no navigation source exists, the positioning precision of the airborne platform is improved, and then the positioning precision of the bistatic airborne radar on the non-cooperative target is improved.

This invention relates to the field of data processing technology and discloses a method, apparatus, and equipment for monitoring surface deformation based on parameter search interval optimization. The method, based on the location information of multiple buildings in a geographic coordinate system and multiple SAR images, determines at least one building-related region and at least one non-building region within a target area. It then determines a first residual elevation search interval for the building-related region based on the roof elevation value of at least one target building within the building-related region; determines a second residual elevation search interval for the non-building region; and performs temporal InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, a preset deformation rate search interval, and multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area. This significantly improves data processing efficiency while ensuring the accuracy of the temporal InSAR phase calculation and obtaining accurate surface deformation rate and elevation fields.

This disclosure relates to the field of remote sensing and mapping technology, and provides an adaptive distributed scatterer interferometry method, apparatus, and medium for mountainous areas. The method includes: acquiring radar image data and digital elevation model (DEM) data of the target monitoring mountainous area; calculating the radar vegetation index of each pixel in the dual-polarization synthetic aperture radar (DAP) image sequence and classifying it into multiple vegetation coverage levels to obtain a vegetation coverage grading atlas; identifying statistically homogeneous pixels in each image scene under the spatial constraints of vegetation coverage levels according to the grading atlas to determine a candidate set of distributed scatterers; further, using the grading atlas, performing adaptive coherence threshold filtering on each candidate point to obtain multiple distributed scatterers; and determining the long-term surface deformation results of the mountainous area based on the multiple distributed scatterers, the DAP image sequence, and the DEM data. This embodiment effectively improves the accuracy and reliability of distributed scatterer extraction and is suitable for deformation monitoring in complex mountainous areas.

The application discloses a VCSEL cold cavity three-dimensional vector mode solving method based on a transmission matrix-finite element hybrid. The method firstly constructs a complete epitaxial structure transmission matrix model containing an oxidation limiting layer to search for a resonance wavelength, and calculates DBR surface admittance at the resonance wavelength; then only the core active region is subjected to three-dimensional structured grid division and Nédélec edge element vector finite element discretization, and the DBR surface admittance is embedded into the weak form of Maxwell equation as an impedance boundary condition; a generalized non-hermitian eigenvalue problem is solved to obtain a cold cavity mode; finally, LP mode automatic classification is realized through a three-level algorithm of aperture restriction factor screening, azimuth angle Fourier decomposition and radial node counting. The application equivalently processes the DBR by the transmission matrix method, reduces the FEM calculation domain by one to two orders of magnitude, greatly improves the calculation efficiency while maintaining the three-dimensional vector precision, and correctly describes the influence of the longitudinal position of the oxide on the resonance condition.

The invention relates to the technical field of power system safety, and provides a power distribution system safety operation optimization method and system based on low inertia. The invention discloses a power distribution system safe operation optimization method based on low inertia. The method comprises the following steps: constructing an evaluation model based on a minimum inertia demand by taking minimization of the total operation cost of a system as an optimization target; under a set constraint condition, solving the evaluation model based on the minimum inertia demand by using a grey wolf algorithm to obtain a minimum inertia demand value at each moment; in the process of searching the minimum inertia demand value at each moment, the grey wolf algorithm adopts a convergence factor to determine a search area; the convergence factor is represented as the fractional order power of the ratio of the difference value of the maximum number of iterations and the current number of iterations to the maximum number of iterations. Response reliability of the system to sudden disturbance can be improved.

The invention discloses an operation state regulation and optimization method for a power distribution network containing a distributed power supply and a flexible load, and the method comprises the following steps: obtaining a topological structure and operation parameters of the power distribution network, and constructing a power distribution network operation state regulation model according to the topological structure and the operation parameters; on the basis of a power distribution network operation state regulation and control model, a traditional knowledge acquisition sharing algorithm is improved, and a spiral updating strategy is introduced to guide a search individual to approach a current candidate optimal solution along a spiral trajectory; and performing iterative solution on the operation state regulation and control model by using an improved knowledge acquisition sharing algorithm to obtain operation optimization configuration of the power distribution network. According to the method, the global search capability and the local convergence precision of the algorithm are remarkably improved, so that the operation optimization configuration can be quickly and accurately obtained, and the stability, the adaptability and the overall operation performance of the power distribution network when the power distribution network deals with renewable energy output fluctuation and load change are enhanced.

The present application relates to a kind of nuclear power process twin system simulation model parameter optimization method based on PSO-TD3 hybrid framework, comprising: based on demand, construct nuclear power process twin system simulation model, determine the optimization parameter contained in nuclear power process twin system simulation model;Loss function for measuring the combination performance of current each parameter of nuclear power process twin system simulation model is constructed, with the value of loss function minimization as optimization goal;With particle, each parameter of nuclear power process twin system simulation model is represented, based on particle swarm optimization algorithm, the PSO update function for updating the speed and position of particle is constructed;With the state parameter of PSO update function, TD3 state vector is constructed, and the output action of TD3 is mapped as the weight parameter of PSO update function;Based on optimal particle group, the parameter of nuclear power process twin system simulation model is updated and optimized.For solving the problem that nuclear power simulation model parameter space dimension is high, nonlinear is strong, traditional static optimization algorithm is easy to fall into local optimum.

The application belongs to the technical field of workshop scheduling, and particularly discloses a kind of inconsistent batch quantity flow flexible job shop scheduling method and system, it includes: for inconsistent batch quantity flow flexible job shop scheduling problem, with sub-batch quantity as one of decision variables to construct mixed integer linear programming model;Intelligent optimization algorithm is used to solve mixed integer linear programming model iteratively, in the iteration process, in the intermediate solution generated in each iteration, select part of elite solution, fix the machine allocation of each sub-batch in elite solution and the value of processing order variable of each sub-batch on machine, with the sub-batch quantity in elite solution as optimization variable, construct batch sub-problem model;Sub-problem model is solved, so that elite solution is optimized, and the intermediate solution after processing continues to the next round of iteration.The application can efficiently solve inconsistent batch quantity flow flexible job shop scheduling problem, and realize the improvement of processing efficiency and equipment utilization.

The present application relates to reservoir scheduling technical field, disclose a kind of cascade reservoir group scheduling trajectory dynamic optimization method, comprising the following steps: collecting scheduling basic data;Optimization function and constraint condition of cascade reservoir group scheduling are constructed and calculation parameter is set;Using artificial experience decision or conventional method, obtain the initial trajectory of each reservoir each stage;Start iteration, execute several times dynamic programming method and establish historical trajectory library;Continue iteration, execute multi-type flight mode dynamic optimization, randomly select lev flight, spiral flight or navigation flight mode combination historical trajectory optimization current trajectory, iteration until meeting termination condition output optimal scheduling trajectory.The present application also discloses a kind of cascade reservoir group scheduling trajectory dynamic optimization.The present application cascade reservoir group scheduling trajectory dynamic optimization method and system, break through the dimension disaster defect that dynamic programming exists in solving complex reservoir group scheduling trajectory optimization problem, present significant convergence ability and calculation precision.

This invention discloses a method for constructing a convex optimization model for train energy-saving operation and control oriented towards complex traction characteristics. The method includes the following steps: collecting train operation data; constructing a basic convex optimization model for train energy-saving operation and control; constructing an inherent characteristic model of the traction system with natural characteristic zones and hybrid braking; constructing the relationship between efficiency, traction / braking force, and speed, introducing auxiliary variables, establishing the relationship between efficiency and train operation energy consumption, and constructing a convex model for train energy-saving operation and control based on the dynamic efficiency map of the motor; using positive and negative fitting faces to fit data to positive and negative surfaces, converting the dynamic efficiency map of the motor into a convex polynomial, and establishing the connection between dynamic efficiency and energy consumption; and obtaining real energy consumption data from power loss analysis of various components at the system level. This invention constructs a more accurate, highly adaptable, and robust train energy-saving operation and control model, reduces the model complexity at the traction drive system level, and improves the solution efficiency of complex models.

The application relates to a Beidou regional network earth rotation parameter solving method and system based on random model refinement. The method comprises the following steps: acquiring a historical high-precision earth rotation parameter sequence, statistically processing high-order differences, verifying the stability of the statistically obtained high-order differences, providing GNSS observation method equations, providing inter-satellite link observation method equations, providing virtual observation equations based on the high-order differences, and restraining the variation of earth rotation parameters between days, and jointly solving satellite orbits and earth rotation parameters. The method provided by the application can improve the solving precision of the regional network ERP by adding Ka-band inter-satellite two-way observation and refining the random model to provide prior constraints.

The invention provides an atmospheric parameter inversion method and system for a high-speed orbiter. The method comprises the following steps: acquiring pressure data of a plurality of measuring points arranged on the surface of the high-speed orbiter; inputting the pressure data into a pre-established mapping relation model to obtain and output corresponding atmospheric parameters; wherein the mapping relation model is a nonlinear model obtained by training by adopting a neural network fused with extended Kalman filtering according to the corresponding relation between the measuring point pressure data of the high-speed orbiter in the flight envelope and the atmospheric parameters. On the basis of the theoretical basis of the one-to-one correspondence single-valued mapping relation between the surface pressure data and the corresponding atmosphere data, the design method for inversing the atmosphere data based on surface pressure reverse modeling is provided, and the technical problem that the surrounding environment parameters of the high-speed orbiter cannot be obtained in real time through a traditional design method is solved; meanwhile, the attack angle and the sideslip angle of the orbiter are solved, and the technical problem of obtaining the high-precision attitude angle of the high-speed aircraft is solved; the applicability is high, and the calculation precision is high.

This invention relates to a method for energy saving and pressure pulsation suppression in the hydraulic system of engineering vehicles, comprising the following steps: S1: Real-time acquisition of the operating parameters of the hydraulic system; S2: Inputting the operating parameters into a pre-constructed joint prediction model of energy consumption and pulsation to obtain predicted energy consumption and pressure pulsation values ​​in the future time domain; S3: Using the minimum weighted sum of the predicted energy consumption and pressure pulsation values ​​as the objective, a rolling optimization algorithm is used to solve for the optimal control parameter sequence; S4: Applying the first control parameter in the optimal control parameter sequence to the adjustable element of the hydraulic system. This invention can save energy and reduce consumption, accurately regulate and adapt to different operating conditions.

The invention relates to the technical field of virtual power plants, in particular to a virtual power plant energy scheduling method and system based on distributed optimization, and the method comprises the steps: constructing a distributed energy node state sensing network, collecting the operation state data and load prediction data of each distributed energy unit in real time, and transmitting the data to a distributed optimization scheduling center; based on an improved distributed model predictive control algorithm, establishing a multi-target energy scheduling optimization model, and decomposing a global optimization problem into local sub-problems of each node; parallel iterative solution of each sub-problem is realized through a distributed collaborative solution mechanism, and a scheduling scheme is dynamically corrected; and based on the real-time operation deviation and the external environment change, adaptively adjusting a scheduling strategy and issuing the scheduling strategy to each energy unit for execution. According to the invention, distributed energy resources are integrated through a distributed optimization architecture, efficient collaborative scheduling of virtual power plant energy is realized, the energy utilization rate is improved, the operation cost is reduced, and the system operation stability and flexibility are enhanced.

This invention relates to the field of attitude stabilization and active vibration control technology for tunneling machines, and discloses a magnetorheological active anti-instability and vibration suppression attitude stabilization system for a steep inclined shaft tunneling machine. The system includes an anti-roll bar assembly, a magnetorheological actuator, an attitude sensing unit, a vibration sensing unit, a drive unit, and a main controller. The main controller performs frequency domain decoupling filtering on the three-dimensional attitude signal and the three-axis vibration acceleration signal, separating the low-frequency reference attitude offset and the high-frequency vibration acceleration characteristic quantity, and calculating the anti-instability foundation support current and the vibration suppression damping compensation current respectively. It then calculates the control weights based on contact pressure data and weightedly fuses the two currents to generate a comprehensive target drive current. The drive unit adjusts the excitation current according to the comprehensive target drive current to change the apparent yield stress of the magnetorheological fluid. This invention reduces control interference in attitude stabilization support and high-frequency vibration reduction, and improves operational stability under complex conditions of steep instability.

The application provides a SAR adaptive intensity tracking method based on feature detection and nonlinear mapping, comprising the following steps: step 1, data preprocessing; step 2, dividing a main image into a plurality of dense evaluation grids; step 3, obtaining a matching window size of each dense evaluation grid; and step 4, obtaining ground surface offset information of a target area. The application improves the accuracy of feature extraction and the rationality of matching window division, can automatically allocate a relatively small size matching window in a feature-rich area to retain deformation details, allocate a relatively large size matching window in a feature-sparse area to guarantee matching robustness, effectively improves the calculation accuracy of the ground surface offset of the target area, and significantly improves the spatial integrity of the result, and has stronger practical value.

The application relates to the technical field of motor control, and particularly discloses a resolver software decoding method based on an MCU and a device thereof, the decoding device comprising an MCU, an amplification circuit, two conditioning circuits, two multipliers and two low-pass filters; the MCU comprises a timer, a DAC module, a first ADC module and a second ADC module; the DAC module is connected with a primary side winding of a resolver, a first input end of a first multiplier and a first input end of a second multiplier through the amplification circuit; two secondary side windings of the resolver are connected with a second input end of the first multiplier and a second input end of the second multiplier; and an output end of the first multiplier is connected with the first ADC module through a first filter and a first conditioning circuit, and an output end of the second multiplier is connected with the second ADC module through a second filter and a second conditioning circuit. The application can reduce the cost of a resolver shaft angle calculation system and realize high-precision shaft angle calculation.

The self-calibrating polarization state measurement device and method based on the trust-domain reflection method belongs to the field of precision instrument manufacturing and measurement technology. The device includes an incident optical fiber, a collimator, a magneto-optical crystal module, a quarter-wave plate, an analyzer, an output optical fiber, and a packaging shell. The method obtains 16 output optical powers by changing the rotation angle of the four magneto-optical crystal modules. It uses an optical power meter to measure and processes the data using a trust-domain reflection optimization mathematical model and algorithm. It can realize the self-calibration of the polarization state measurement device and analyze the polarization state of the input light, and can overcome the influence of temperature, wavelength, assembly and measurement errors, and noise on the measurement accuracy.

This invention discloses a method and system for identifying photovoltaic cell model parameters based on an improved flower pollination algorithm, belonging to the field of photovoltaic cell modeling. It includes: receiving settings for the type of the photovoltaic cell model and the parameters of the flower pollination algorithm; determining the composition of parameters to be identified based on the set photovoltaic cell model type, using these as the positions of individual pollen individuals, and initializing the pollen population; iterating the population using the improved flower pollination algorithm, and outputting the global optimal solution and the corresponding pollen positions; during population iteration, when updating the pollen individual positions globally for pollination, the parameter controlling the pollination intensity uses a Gaussian distributed random number; when updating the pollen individual positions locally for pollination, the positions of two pollen individuals in the population that are different from the current pollen come from different clusters. This invention uses a Gaussian distribution to represent long-distance pollination in the flower pollination process and a clustering evolution strategy to represent short-distance pollination, effectively improving convergence speed and solution accuracy.

This invention belongs to the technical field of robotic arm motion planning and discloses a robotic arm motion planning method and system based on an error-driven adaptive gradient model. The method includes: S1 establishing the joint velocity at two adjacent moments with respect to the adaptive coefficient λ based on a Lyapunov function. n The gradient relationship is obtained; S2. The joint velocity at time n+1 is calculated using the gradient relationship, and the error between the calculated joint velocity at time n+1 and the adaptive coefficient is updated using the central finite difference method; S3. n = n+1; return to step S2 until the joint velocities at all times are obtained; S4. The joint position is calculated using the relationship between the joint velocity and position; the velocity of the end effector is calculated using the robotic arm motion planning model and the joint velocities at each time, thereby realizing the motion planning of the robotic arm. This invention solves the problem of low computational efficiency in robotic arm motion planning.

The invention discloses a nuclear reactor few-group diffusion coefficient solving method based on anisotropic neutron transport, which comprises the following steps: firstly, generating a material area multi-group macroscopic cross section by a multi-group cross section library according to the geometric dimension and material information of a nuclear reactor fuel assembly; secondly, on the basis of the multi-group macroscopic cross section of the material area, adopting anisotropic P1 neutron transport calculation to obtain the neutron flux density of the flat source area, and normalizing according to volume weight to obtain a component neutron energy spectrum; then, establishing a multi-group diffusion coefficient solving linear equation set, and solving to obtain a multi-group diffusion coefficient; further, carrying out transport correction by utilizing a multi-group diffusion coefficient to obtain a transport cross section and a corrected zero-order self-scattering cross section; finally, isotropic neutron transport calculation of transport correction is carried out, leakage correction and component homogenization are carried out after the neutron flux density of a flat source region is obtained, and a few-group diffusion coefficient is generated. According to the method, the error caused by approximate processing is reduced while the requirement for component calculation efficiency is met, and the method has engineering application value.

This invention discloses a low-carbon economic dispatch method and apparatus for a hydrogen-electricity combined system, belonging to the field of electrical control engineering technology. The method aims to minimize system operating costs under carbon tax considerations, using discretized linear dynamic energy flow equations as constraints for the operation of the hydrogen-electricity network. It constructs a low-carbon economic dispatch model for the hydrogen-electricity combined system, which can both characterize the dynamic characteristics of the hydrogen-natural gas transportation process and meet the requirements of optimization problem solving. It can accurately reflect the operating status of the hydrogen-natural gas system by tracking changes in gas composition during transportation. While preserving the model's characteristics, it reduces the computational cost of real-time solving of the nonlinear dynamic energy flow model. The method uses the low-carbon economic dispatch model of the hydrogen-electricity combined system for dispatching, ensuring that the combined system always operates within safe boundaries, improving the system's renewable energy absorption capacity, and reducing system operating costs and carbon emissions.

The application discloses a method for evaluating ship riding wave probability in irregular waves based on AIS data, which comprises the following steps: 1, establishing a probability density function of ship density based on ship AIS data; 2, selecting a representative route and multiple test points at equal intervals, and obtaining the probability of occurrence of a specific random sea state in a target sea area according to a wave scatter diagram of a sea area near the test points; 3, calculating the riding wave probability of a ship in random waves; 4, calculating the riding wave probability of a ship in a specific irregular wave of a test point of the representative route in random waves; 5, calculating the riding wave probability of the ship under each random sea state of the test point, and calculating the riding wave probability of the ship of other test points according to the same steps. The application solves the related problem of solving the riding wave motion of a ship in irregular waves, can predict the probability of the riding wave motion of the ship in irregular waves, the calculation result can directly obtain the riding wave motion probability of the ship in irregular waves, the solving precision is high, the efficiency is high, and the application is suitable for various ship types.

The invention discloses a vibration solving method for a gear system driven by a physical information neural network. The vibration solving method comprises the following steps: constructing a neural network architecture fused with a Fourier feature mapping layer; defining a physical loss function formed by a system kinetic equation; and verifying a network generation result by taking a Runge-Kutta method iteration result as a truth value to realize solution and verification of the dynamic model. According to the method, a two-degree-of-freedom spring damping system and a four-degree-of-freedom gear meshing system are used as research objects, Fourier feature mapping is introduced, physical constraints are successfully embedded into a network, a definite physical significance is given to the model, the model is not a black box any more, the problem of frequency spectrum deviation existing in a neural network is solved, and the method is suitable for being applied to the field of modeling. The high-frequency dynamic characteristics of the system can be learned and represented more efficiently, the model solving precision is higher, the complex vibration mechanism of the system can be simulated more accurately, and high-precision solving of the nonlinear and high-frequency dynamic system is achieved.

The application discloses a wind farm virtual inertia optimization distribution method based on an improved particle swarm algorithm, and steps are as follows: firstly, the critical inertia of a power grid for maintaining frequency dynamic stability is solved according to a power grid frequency safety constraint index, and a power grid inertia compensation target is obtained in combination with an actual inertia of the power grid. Then, the minimum system maximum frequency deviation is taken as an optimization target, the wind farm virtual inertia compensation target is taken as an optimization object, the power grid inertia level constraint, the wind turbine virtual inertia support capability constraint and the system frequency stability are taken as constraint conditions, and an optimization distribution model is established. Then, the model is solved by using the improved particle swarm algorithm, and the virtual inertia optimization distribution scheme of each wind farm is obtained. Finally, the correctness and effectiveness of the method are verified in an IEEE-39 node example system. Under the premise of ensuring that the power grid inertia is at a safe level and the wind turbine runs stably, the optimal distribution scheme of the virtual inertia of each wind farm can be obtained, and the algorithm has strong convergence and high solution accuracy.