21results about How to "Reduced solution time" patented technology

The invention discloses a full-connection Isin machine and an updating method thereof. The full-connection Isin machine comprises a spinning unit array, a global control module, an input driving module and an output driving module, the spinning unit array comprises a plurality of spinning units, any two spinning units interact with each other, a memristor array formed by memristor units is arranged in each spinning unit, and the output driving module is connected with the input driving module. The memristor array receives spin values from other spin units and stores weights equivalent to interaction; the driving module selectively activates and drives the memristor arrays in the plurality of spin units to perform spin updating operation; the output driving module enables all the spinning units to output spinning values; the global control module carries out global control on the input driving module and the output driving module so as to realize parallel control on all the spinning units, and each spinning unit receives spinning values from all the other spinning units and inputs the updated spinning values into all the other spinning units when each spinning update is carried out.

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 application relates to a coaxial heat exchange well heat exchange efficiency prediction method in oil exploitation, which comprises the following steps: a coaxial heat exchange well coupling heat transfer physical model is established; according to the engineering structure and the geological conditions of a target block, a standardized heat transfer model directly used for mathematical solution is established; according to the temperature control equation in the oil pipe and the annular space, the wellbore temperature field partial differential equation is calculated by using Laplace transformation, the fluid temperature at a position of the wellbore is calculated, and the wellhead outlet temperature is obtained; the analytical solution of the formation temperature changing with the radial distance and the time in the target block is obtained, the range of the formation temperature drop in the radial direction after the coaxial heat exchange well runs for a predetermined time is evaluated; and the coaxial heat exchange well heat exchange efficiency is calculated and predicted according to the obtained wellhead outlet temperature and the known well inlet temperature. The application can solve the problems of long solution time in numerical simulation and inaccurate calculation results in the analytical solution based on the steady-state assumption, improve the oil production efficiency and reduce the energy consumption.

The application discloses a simulation method and device of a multi-physical field coupling resonator, a storage medium and a terminal, wherein the method comprises the following steps: acquiring a resonator model to be solved; identifying a plurality of physical fields in the resonator model to be solved; performing cubic grid division on the resonator model to be solved, and dividing the resonator model to be solved after the grid division into a plurality of grid areas; and acquiring system matrices of all the grid areas by using a finite element method; wherein a degree of freedom vector in the system matrices comprises physical field components of the plurality of physical fields in the resonator model to be solved; the system matrices of all the grid areas are sequentially spliced based on a coupling mode of the same physical field components in the matrices, so as to acquire the system matrices of the resonator model to be solved; and the system matrices of the resonator model to be solved are solved, so as to acquire a target solving physical quantity. The application realizes the simulation combination of the plurality of physical fields in the resonator, reduces the solving time, and also reduces the memory required for calculating the matrices.

The application provides a security-constrained economic dispatch method based on a dual Halpern iteration, and belongs to the technical field of power system optimization dispatch and artificial intelligence computing infrastructure. The method comprises the following steps: constructing an original model of a security-constrained economic dispatch problem SCED; modeling the renewable energy output uncertainty by using a Gaussian mixture model, converting the chance constraint in the original model into a deterministic linear constraint, and thus converting the original model into a convex quadratic programming problem; constructing a limited Wolfe dual model for the convex quadratic programming problem, solving the dual model by using a Halpern iteration, and obtaining a dispatch optimization result. The application can solve the problems of low calculation efficiency, large memory overhead, low GPU utilization rate and difficulty in stable solution in a super-large-scale scenario in large-scale SCED, and can quickly generate an executable dispatch scheme, so that the calculation efficiency and engineering applicability of online dispatch and rolling optimization are improved.

This invention discloses a high-precision RTK positioning method based on an improved adaptive genetic algorithm-based satellite selection strategy, belonging to the field of satellite navigation and positioning technology. A subset of satellites with a specified number of satellites is selected to minimize the loss of the Position Accuracy Factor (PDOP) and satellite geometric volume. The satellite selection problem is transformed into a combinatorial optimization problem. A fitness function is designed with the reciprocal of PDOP and geometric volume as its core, and adaptive crossover and mutation probabilities are introduced. The genetic operation parameters are dynamically adjusted according to the population evolutionary state to balance global search and local convergence capabilities. Iterative evolution is performed under the condition of satisfying subset size constraints. When the convergence condition is met, the subset of satellites with locally optimal PDOP values ​​is output for subsequent RTK ambiguity resolution and positioning. This invention can quickly converge to a near-globally optimal satellite geometry configuration in polynomial time while also considering satellite quality, significantly reducing computational complexity and improving the real-time performance and reliability of RTK positioning.

This invention discloses a normalizing control method and production method for high magnetic induction oriented silicon steel, which involves normalizing and annealing hot-rolled coils at different temperature stages: the first normalizing temperature is 1135~1140℃, and the normalizing time is 55~70s; the second normalizing temperature is 1130~1135℃, the dew point is controlled to be positive, and the normalizing time is 20~30s; the third normalizing temperature is 930~940℃, the dew point is controlled to be positive, and the normalizing time is 55~75s; the fourth cooling temperature is 860~870℃, and the cooling time is 8~15s. This invention, through an improved normalizing process, achieves more complete nitride solidification at normalizing temperatures above 1130°C under a fixed furnace atmosphere dew point, while simultaneously reducing nitride solidification time: the first normalizing stage is reduced to below 70 seconds, with a minimum of 55 seconds; the second normalizing stage is reduced to below 30 seconds. During the cooling process at approximately 930-940°C, finer AlN precipitation is more uniform, and the AlN precipitation time after 930-940°C is reduced to below 75 seconds, with a minimum of 55 seconds. This invention significantly shortens the normalizing time, improves normalizing production efficiency, and increases production capacity by controlling the uniformity of the grain size on the surface of the normalized hot-rolled plate.

The application provides an unmanned vehicle trajectory planning method based on energy optimization, comprising: updating complex irregular obstacle information of surroundings in the unmanned vehicle based on point cloud information; calculating posterior probability of a grid according to point cloud information fed back each time, and constructing an occupancy grid map in real time; searching for path points for avoiding obstacles by using an A* algorithm based on vehicle kinematics according to terrain and environment information constructed by a sensor; obtaining a series of relatively dense path points by using a path backtracking method, and performing curve fitting by using a uniform B-spline curve without control points; constructing an Euclidean distance map to further construct an unconstrained optimization problem about minimum energy consumption, driving speed, driving acceleration and obstacle avoidance distance, and solving the optimization problem by using an open source solver to obtain an optimal trajectory with optimal energy consumption, satisfying dynamic feasibility and being able to avoid obstacles; and performing time redistribution on the optimal trajectory, so that the speed and acceleration values corresponding to the trajectory points do not exceed a threshold value.

The invention discloses a transmission and distribution cooperation-based mountainous area power grid source-grid load-storage progressive coordination planning method, system and device, and a medium, and the method comprises the following steps: generating an original operation scene through a sampling method according to historical load data and historical output data, and reducing the original operation scene into a typical operation scene; solving the source network load storage coordinated optimization model by adopting a decomposition algorithm, and obtaining an optimal investment decision of each planning stage through iteration; and performing load flow calculation on the typical operation scene based on the power grid frame parameters and the optimal investment decision, and calculating a new energy consumption rate. According to the invention, by constructing a full-voltage class coordinated optimization framework and a progressive rolling planning mechanism, the technical problems of bidirectional power flow, poor planning collaboration, insufficient uncertainty processing capability and the like caused by the access of high-proportion new energy to the mountain power grid are effectively solved.

The present application provides a kind of based on M-HODLR solving electromagnetic scattering method of metal medium composite structure, to solve the problem of large memory consumption and low accuracy of electromagnetic scattering result when solving electromagnetic scattering of metal medium composite structure.The implementation steps of the present application are: the surface of metal medium composite structure is completely meshed;Define the base function on each mesh element after meshing and sort the base function;Fill the total impedance matrix;Total impedance matrix is blocked using M-HODLR method;Each sub-matrix block after blocking is compressed using enhanced ACA method;Solve the matrix equation of the generated metal medium composite structure and calculate the electromagnetic scattering result.The present application has the advantages of significantly reducing the calculation memory, reducing the solving time and the high accuracy of the electromagnetic scattering result of the metal medium composite structure calculated.

The application relates to a kind of honeycomb aluminum structure to space debris ultra-high speed collision protection quantitative evaluation method, belong to numerical simulation technical field.Based on the actual ultra-high speed collision situation, establish the simulation model of ultra-high speed collision, respectively extract the backplane and the back effect plate of honeycomb aluminum structure after ultra-high speed collision Perforation damage image, and extract the coordinate information of SPH particle of fragment cloud produced after space debris collides with honeycomb aluminum structure;The extracted perforation damage image is sequentially grayed, binarized, then the damage area is identified and the area size is counted, and the identification and size statistics of the minimum circumscribed circle or the minimum circumscribed fitting ellipse are realized, and the damage degree and range are quantitatively evaluated based on the above statistical results;Based on the coordinate information of the extracted SPH particle, the density clustering algorithm is used to realize the identification and mass statistics of the center dangerous fragment of the fragment cloud formed after collision, which realizes the quantitative evaluation of protection efficiency, and greatly saves the research and development cost.

The application discloses a high-precision network solving method based on mass-volume flow double coupling, relates to the technical field of mine ventilation, and adopts Kruskal algorithm to select a minimum wind resistance tree; mass air quantity of all branches is represented by the mass air quantity of a residual tree chord, and a wind pressure balance equation set is established; the mass air quantity of the residual tree chord is calculated, and a correction value of each residual tree chord mass air quantity is calculated; the mass air quantity of all branches is iteratively corrected, and when a convergence condition is reached, high-precision mass air quantity of all branches is obtained; and then high-precision volume air quantity of all branches is calculated. The application takes mass flow as a calculation reference, the calculation of the mass flow is not disturbed by air density change, the air flow dynamics in the mine ventilation network can be more accurately simulated, the volume flow solving error is significantly reduced, and the calculation precision and efficiency are improved.

This invention discloses a multi-robot path planning method and system based on hierarchical control. The method acquires model information and basic parameters of each robot in the system, and constructs a realistic map of the working environment using the SLAM algorithm. The grids of the constructed realistic map are merged, transforming a fine-grained grid map into a coarse-grained one. Considering the overall movement direction and target position of the robots, a global path input for the navigation system is obtained. This global path input is published to each robot, and robots can subscribe to other robots' paths to capture their global path information. Each robot perceives local environmental information in a local map and obtains information about obstacles around other robots. Based on the global path results from the MAPF algorithm, a local path planning method is used to generate local paths, enabling avoidance between robots. This provides a reliable solution for efficient collaboration in multi-robot systems.

The application discloses a kind of multi-edge device task assignment methods based on dynamic resource perception, purpose is to solve the imbalance between inference delay and inference energy consumption problem.The multi-edge device task assignment system based on task splitting containing delay prediction model is constructed, the delay prediction model is trained, the inference time of different task fragments running on the resource-limited, highly heterogeneous edge device cluster is predicted, the resource condition of edge device is monitored in real time, and the memory capacity of edge device is introduced as constraint, the delay and energy consumption model of multi-device collaborative computing based on task splitting is constructed, to minimize the total weighted cost of task assignment system as optimization goal, construct collaborative inference joint optimization problem, convert the optimization problem into hierarchical directed graph shortest path search problem, determine optimal task allocation and routing, realize task splitting and multi-device dynamic efficient execution, so that inference delay and inference energy consumption are balanced, greatly shorten task inference time.

A kind of quadrotor unmanned aerial vehicle obstacle avoidance method based on digital twin system, comprising the following steps: step 1: establishing unmanned aerial vehicle digital twin system;Step 2: design feedforward-feedback control strategy based on model predictive control in physical space;Step 3: design and deploy offline trajectory planning framework in virtual space;The present application improves the quality of unmanned aerial vehicle trajectory planning and motion control accuracy based on the establishment of unmanned aerial vehicle digital twin system through the design of trajectory planning framework and feedforward-feedback control strategy.

This invention discloses a real-time foreign exchange risk quantification method based on multi-model fusion optimization. The method includes: acquiring raw foreign exchange data and preprocessing it to generate a standardized feature dataset; based on the standardized feature dataset, controlling a Markov chain model and a VAR model to perform independent parallel computations to obtain the initial exchange rate state probability distribution and the initial value at risk quantification result, respectively; acquiring sudden market event data, and performing Bayesian dynamic probability correction on the initial exchange rate state probability distribution and the initial value at risk quantification result to obtain the corrected prediction result. The beneficial effects are: through Kubernetes resource isolation and Apache Spark parallel scheduling, the Markov chain and VAR model can operate independently and in parallel, reducing the time taken for traditional serial computation from ≥15 minutes to ≤2 minutes, improving computational efficiency by 87%; the Markowitz effective front solution time is shortened from 3 minutes to less than 500ms.

This invention provides a method and system for optimizing urban rail transit train timetables considering passenger flow allocation, belonging to the field of urban rail transit operation management technology. This invention establishes a timetable optimization model considering both long / short routes and express / local train modes, taking into account constraints such as train intervals, train service frequencies, and train origin and destination points. With the objective of minimizing total passenger travel time and company vehicle-kilometer costs, a multi-objective timetable optimization model based on long / short routes and express / local train modes is constructed. A passenger flow allocation model is established based on passenger choice behavior analysis under express / local train operation conditions, considering the influencing factors of passenger route selection under express / local train operation and analyzing passenger route selection behavior. The invention also discusses in detail the possible transfers and choice behaviors that passengers may exhibit during their travels and establishes a passenger flow allocation model.

The present application relates to lithium battery production and intelligent production control technology field, specifically to a kind of lithium battery module production task dynamic optimization production scheduling method, comprising: obtaining initial global production scheduling plan containing standard production rhythm and each detection node real-time feedback accessory detection state data;Identify local detection abnormal events and calculate corresponding disturbance isolation radius;Candidate rescheduling scheme is constructed in the local production area covered by disturbance isolation radius, and the production scheduling plan fluctuation index relative to initial global production scheduling plan is calculated;When fluctuation index is less than preset fluctuation threshold, establish optimal rescheduling scheme and generate, issue local rescheduling instruction;Local rhythm recovery delay is monitored, and local task delay and global production rhythm dynamic isolation are realized;The present application considers solution timeliness and line stability.

The application relates to a load balancing-based sparse matrix LU decomposition method and device, which comprises the following steps: loading a sparse matrix A in circuit simulation and forming a basic update level; performing ALAP-based scheduling on the generated basic update level to generate an ALAP post-scheduling update level; performing numerical decomposition on the ALAP post-scheduling update level based on a Left-Looking algorithm from the lowest priority level, wherein all columns in the same level are calculated in parallel, and the columns in different levels are calculated according to the level priority; and switching to a Right-Looking algorithm for numerical decomposition when the size of the remaining level is one column after the Left-Looking algorithm-based decomposition. The application can improve the average utilization rate of hardware computing resources, reduce the LU decomposition time, shorten the sparse matrix solving time, and further accelerate the overall simulation speed.

This invention relates to a vehicle positioning method, apparatus, and computer based on Informer networks. The method includes: determining whether the vehicle's satellite positioning function is normal; clustering the vehicle's traffic environment image data using a Gaussian mixture model to obtain multiple traffic environment cluster datasets; sorting the vehicle's motion feature data under each traffic condition according to the vehicle's travel time sequence to obtain a motion feature time series dataset; training Informer neural network models using the motion feature time series datasets corresponding to multiple traffic conditions; determining the Informer neural network prediction model using a bulldozer distance analysis method when the vehicle's satellite positioning function is abnormal; and predicting the vehicle's trajectory using the Informer neural network prediction model. This invention uses Informer networks to predict vehicle trajectories, effectively solving the problem of how to accurately predict vehicle positions when GPS fails for extended periods.

The invention discloses a high-precision network resolving method based on mass-volume flow double coupling, and relates to the technical field of mine ventilation, and the method comprises the following steps: selecting a minimum wind resistance tree by adopting a Kruskal algorithm; the mass air volume of all branches is expressed by the residual tree chord mass air volume, and an air pressure balance equation set is established; calculating residual tree chord mass air volume, and calculating a correction value of each residual tree chord mass air volume; iterative correction is conducted on the mass air volumes of all the branches, and when the convergence condition is met, the high-precision mass air volumes of all the branches are obtained; and high-precision volume air volumes of all branches are calculated. According to the method, the mass flow is adopted as the calculation reference, and the calculation of the mass flow is not interfered by the air density change, so that the airflow dynamic state in the mine ventilation network can be more accurately simulated, the volume flow calculation error is obviously reduced, and the calculation precision and efficiency are improved.