34results about How to "Fast solution" patented technology

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention discloses a wind-light storage configuration method considering transient voltage constraint and inertia constraint, and the method comprises the steps: obtaining a wind-light typical output scene and a direct-current power transmission plan power through employing a k-means clustering method based on wind-light output and receiving end load data, employing a bilevel programming model for wind-light-storage capacity configuration, and obtaining a wind-light storage capacity configuration model; the upper-layer model takes the minimization of the total investment cost as an optimization target, and constructs a transient overvoltage constraint, an inertia support constraint and a capacity constraint based on the short-circuit capacity; and the lower layer model takes the operation cost of the thermal power generating unit and the new energy power abandoning cost as targets, considers the power balance constraint, the operation constraint and the like of the wind-light-storage channel direct current power transmission delivery system, introduces the power transmission confidence probability constraint, and realizes the measurement of the delivery reliability of the wind-light storage channel. An improved grey wolf optimization algorithm is adopted, a Logistic-Tent mapping opposite learning mechanism is added, a mutation operator, a crossover operator and a selection operator are introduced in the differential mutation process of the algorithm, the population number and category richness in the algorithm are improved, and the solving speed is increased.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

This invention discloses a method for testing the absolute efficiency of a hydroelectric turbine based on the surge method in a surge chamber. Applied to hydroelectric power stations equipped with surge chambers, the method measures key parameters during normal operation and after load shedding when surges occur in the surge chamber. These key parameters are then substituted into the turbine flow equation corresponding to the surge chamber type to obtain the turbine flow rate during normal operation. The initial load of the turbine during normal operation is changed, and the above steps are repeated. The volute flow coefficient is calibrated based on the turbine flow rate and the differential pressure in the corresponding key parameters during normal operation. The turbine flow rate under the current operating condition is measured using the differential pressure flow measurement method. The turbine working head is calculated based on the current flow rate, and the turbine output formula is used to back-calculate the turbine absolute efficiency. This invention can conveniently and accurately obtain the turbine flow rate under various operating conditions, thereby achieving accurate calculation of the turbine's absolute efficiency.

This invention discloses a SKU shelving method and storage medium for a mobile robot warehousing system. The method includes: acquiring data information from the mobile robot warehousing system; constructing an SKU shelving decision model based on the acquired data information; constructing a calculation method for an SKU shelving strategy based on the data information and the SKU shelving decision model; calculating the SKU shelving result of the mobile robot warehousing system according to the calculation method, and implementing the method. The SKU shelving method strategy for a mobile robot warehousing system provided by this invention proposes an SKU shelving method from a globally optimal perspective. Compared with existing technologies, it can more rationally allocate shelving locations for SKUs, reduce the number of shelves required to complete all orders, and improve picking efficiency. This invention has good practicality and operability, and the calculation method is simple, providing a new and efficient strategy for solving the SKU shelving problem in mobile robot warehousing systems.

The application discloses a kind of based on uncertainty complement's multi-wind farm aggregated day-ahead output decision and deviation control method, comprising: step 1, generating wind power data, and carrying out uncertainty fitting;Step 2, the aggregated day-ahead output collaborative optimization model of multi-wind farm grouping is established;Step 3, the aggregated day-ahead output collaborative optimization model established in step 2 is solved and generates in-group settlement and responsibility signal.The application utilizes the uncertainty complement between wind farm under the premise of not depending on external flexibility resource, and is combined with the integrated collaborative optimization of conditional risk value and mixed integer programming, realizes the closed-loop process of "grouping-aggregated day-ahead output decision-deviation calculation and apportionment-settlement".

This invention relates to the field of satellite telemetry, tracking, and command (TT&C) technology, and provides a method, system, device, and medium for satellite IoT TT&C mission planning. The method includes: S1: obtaining the set of observable arc segments for each ground station; S2: dividing the mission planning time period into m time segments; S3: first solving the mission planning result for the first time segment; S4: removing observable arc segments in the current time segment that intersect with the previous time segment, completing the mission planning for the current time segment; S5: taking the current time segment as the previous time segment and the next time segment as the current time segment, repeating step S4 until the mission planning for the second to the mth time segments is completed; S6: combining the mission plans of the m time segments to form the satellite IoT TT&C mission plan. This scheme can plan all actions within the planning time period at once, designing a reasonable planning scheme and reducing the complexity of the mission planning algorithm.

The application discloses a trajectory planning method, device and equipment of an automatic driving vehicle and a vehicle, and belongs to the technical field of computers. The method comprises the following steps: acquiring self-vehicle driving data, a first self-vehicle planning trajectory, an obstacle prediction trajectory and road boundary data; generating a grid map of a specified area based on the self-vehicle driving data; constructing an occupancy grid map by using a first construction strategy based on the obstacle prediction trajectory, the road boundary data and the grid map; constructing a space-time corridor by using a second construction strategy based on the first self-vehicle planning trajectory and the occupancy grid map; and obtaining an optimized second self-vehicle planning trajectory by using a trajectory optimization algorithm based on the space-time corridor, the self-vehicle driving data, the first self-vehicle planning trajectory and the occupancy grid map.

The application discloses a kind of power power balance calculation method and system based on machine learning, belong to electric power system optimization scheduling technical field, will deep neural network and optimization calculation be combined, adopt the strategy of integer variable reduction, the operation state of most thermal power units is determined in advance by Seq2Seq-Attention model, the input of model is the relaxation solution of operating state and scene curve data, and the output is the operating state of thermal power unit, so as to reach the effect of improving the calculation speed of power power balance problem, the acceleration effect evaluation index designed and the acceleration effect verification method based on variable time scale scene generation are used to verify the acceleration effect of the fast calculation method, which has high precision and strong practicability.

This invention discloses a large-scale power grid simulation method, program product, and storage medium based on algebraic multigrid optimization. The method includes: parsing the netlist of a large-scale power grid to establish a power grid topology matrix; extracting global feature vectors based on the netlist and the power grid topology matrix; compressing the power grid topology matrix to obtain a compressed feature matrix; constructing and training a regression model, which is used to predict the solution time based on the compressed feature matrix, global feature vectors, and coarsening parameters; using the minimum output of the regression model as the objective function, and employing simulated annealing to find the optimal coarsening parameters; establishing a power grid simulation system Ax = b; and solving the power grid simulation system using the algebraic multigrid method with the optimal coarsening parameters to obtain the power grid unknown vectors, thus realizing large-scale power grid simulation. This invention improves solution efficiency and reduces memory consumption while ensuring accuracy.

The invention relates to the technical field of computer networks, and discloses a multistage CDN (Content Delivery Network) traffic scheduling method based on linear constraint, which comprises the following steps of: receiving an access request analyzed by a local DNS (Domain Name System) to form a scheduling instance to be optimized; carrying out complexity discrimination on the scheduling instance, and determining an applicable scheduling category of the scheduling instance; establishing a performance upper limit model related to server hardware resources; functional linear constraints are introduced, and Big-M parameters related to the constraints are finely set; and server cluster information, server information, quality constraint information and region hijacking information are obtained, joint solving is performed on the performance upper limit model and the functional constraint, a target cache node IP address meeting all constraint conditions is determined and fed back to a local DNS, and response and scheduling of an access request are completed. According to the invention, through a multi-stage scheduling framework and refined Big-M parameter setting, the computational efficiency and resource utilization precision of CDN scheduling in a complex network environment are improved.

The application discloses a kind of low-carbon scheduling methods of integrated energy system considering energy-carbon coupling, which is based on carbon emission flow theory, combined with the strong fitting ability of neural network, proposes the method of carbon flow constraint learning, converts the complex mapping relationship between power flow and carbon flow into mixed integer linear constraint, realizes the effective embedding of carbon flow constraint in optimization model. At the same time, in order to reduce the structural complexity of neural network, the sparse training strategy is introduced, the model parameter size is effectively compressed, and the ReLU activation function is linearized by the improved big-M method, the feasible region is gradually tightened by introducing the cut plane constraint, so as to significantly improve the solving efficiency of optimization model. Finally, the carbon flow constraint model is embedded in the integrated energy system optimization scheduling problem, the carbon emission reduction consciousness of load side is stimulated, the demand response behavior of load side based on its own carbon signal is guided, low-carbon energy adjustment is promoted, and low-carbon scheduling under energy-carbon cooperation is realized, to reduce the overall carbon emission level of the system.

This invention discloses a method for enhancing the security of heterogeneous microservices based on simulated annealing. The method is as follows: S1, Variant Selection: Ignoring nodes, variants are selected only if the constraints are met; S2, Node Deployment: A greedy algorithm is used to deploy the selected variants to nodes one by one according to the runtime dependency chain, maximizing the security of the deployed system while satisfying the constraints. This invention can adapt to different needs by adjusting the parameters of the simulated annealing algorithm. When the system complexity is low, the initial temperature and maximum number of iterations can be appropriately reduced to obtain a faster solution speed; when the system is more complex, a larger initial temperature and number of iterations are used to obtain a better solution. This invention is applicable to situations where there are multiple functionally equivalent variants of the same microservice. By modeling nodes, microservices, and microservice dependencies, and combining the deployment method of this invention, a deployment scheme is generated.

This invention relates to the field of power grid data repair technology, specifically a method for calculating the voltage of nodes with missing distribution network measurements. By analyzing the power deviation problem in distribution network measurement configuration, a mechanism model for power deviation allocation is studied and embedded into a neural network to construct a hybrid driving mode. The neural network is trained offline using historical measurement data to achieve online inference of voltage phasors for nodes with missing distribution network measurements. The calculation and derivation problem is transformed into an allocation coefficient optimization problem through the current matching mechanism equation, while satisfying power flow equation constraints. Finally, the voltage phasor of any node is quickly solved using a mechanism-data hybrid driving neural network. This invention effectively solves the voltage calculation problem under conditions of missing distribution network measurements, improving computational efficiency and accuracy.

This invention provides a method for constructing a space particle environment model. The method involves acquiring a first detection dataset from a main detector and second detection datasets from several auxiliary detectors; incorporating corresponding geomagnetic information into the first and second detection datasets based on several space environment data, an external field model, and an internal field model; assimilating the data in the second detection datasets to construct a generalized linear correlation model; performing correlation analysis between the generalized linear correlation model data and the space environment data, and selecting target environmental factors based on the analysis results; integrating the target environmental factors into the first detection dataset to obtain a target detection dataset; and training the model based on the target detection dataset to obtain a target model. This invention can more effectively standardize short-term environmental disturbances, relax constraints on multi-source data, data time span, and spatiotemporal disturbance characteristics, while also offering a faster solution speed.

The application relates to the technical field of computer data processing, and discloses a portfolio optimization method based on matrix decomposition technology and related equipment; the method comprises the following steps: obtaining portfolio data from an external memory or a local memory; the portfolio data comprises asset names of all assets in a portfolio and historical price data of a preset time period; based on the portfolio data, the average yield of the portfolio and a covariance matrix are calculated; the covariance matrix is subjected to eigenvalue decomposition to obtain a first matrix Q and a second matrix D; an auxiliary variable is constructed, a target function and a constraint condition are constructed based on the auxiliary variable; the target function and the constraint condition are input into an SQP algorithm for solving to obtain a solution of the auxiliary variable; and the weight distribution of each asset in the portfolio is calculated based on the solution of the auxiliary variable. The application can greatly improve the solving efficiency of the model, reduce the memory occupation, save the computer data processing time, and reduce the computer energy consumption.

The application relates to the technical field of computer application, in particular to an optimization method for quickly solving a motor train unit interchange plan. The method comprises the following steps: obtaining real-time state data of a motor train unit; constructing a time-space axis network of the motor train unit interchange plan based on the real-time state data of the motor train unit; establishing an original model of the motor train unit interchange plan based on actual state data of the motor train unit and the time-space axis network; the actual state data comprises real-time state data and maintenance state data of the motor train unit; establishing an aggregated optimization model based on the real-time state data of the motor train unit and the original model of the motor train unit interchange plan, the aggregated optimization model is based on the real-time state data of the motor train unit to obtain a solving result; constructing a disaggregated solving model based on the solving result of the aggregated model, the disaggregated solving model combines the aggregated result with the obtained actual state data to generate a specific real-name interchange plan.

The embodiment of the invention discloses a quantum solving method and device for an optimization problem, electronic equipment and a storage medium. The method comprises the steps that an objective function and constraint conditions of the optimization problem and a simulation circuit used for solving the optimization problem are obtained; constructing a target loss function according to the target function, constructing a constraint loss function according to the constraint condition, and constructing a composite loss function according to the target loss function and the constraint loss function; and finally, when the value of the composite loss function meets a convergence judgment condition, extracting a quantum state of a to-be-set circuit by using a quantum state chromatography technology to obtain a solution of an optimization problem. By adopting the embodiment of the invention, the approximate value meeting the requirement can be obtained while the solving speed of the optimization problem is improved, and the memory occupation of a computer can be reduced.

The invention provides an optimal calculation method for a gas-liquid phase root of a PC-SAFT equation. The optimal calculation method comprises the steps that the temperature, actual pressure and composition of a calculation system are obtained; iteratively solving a point of which the derivative of the differential pressure value to the contrast density is 0 by utilizing a secant method based on a gas-liquid phase hypothesis of a calculation system; in response to a solving result of the secant method, a pressure difference value corresponding to the critical value is calculated; when the pressure difference value corresponding to the gas phase critical value is greater than 0 or the pressure difference value corresponding to the liquid phase critical value is less than 0, solving a point with the pressure difference value being 0 by using a Newton iteration method to obtain an equation root; when the pressure difference value corresponding to the gas phase critical value is smaller than 0 or the pressure difference value corresponding to the liquid phase critical value is larger than 0, the virtual equation root is solved through a curve reconstruction method; in response to the condition that the solving result of the secant method is no solution, solving a point with the differential pressure value of 0 by using a Newton iteration method to obtain an equation root, and solving an interpolation minimum point by using a quadratic interpolation method; and determining a gas-liquid phase of the equation root according to the interpolation minimum point to determine an output value.

The invention provides a course scheduling optimization method and device executed by electronic equipment, and can be applied to the technical field of computer technologies. The method comprises the following steps: constructing a cumulative retention function of a user through an arithmetic unit based on scheduling data of courses and preset user experience data; decomposing the cumulative retention function to obtain a plurality of sub cumulative retention functions; adding the initial solution space into a to-be-understood space list, and storing the to-be-understood space list into a register; n times of solving are carried out based on the initial solution space, and the nth time of solving comprises the steps that the current solution space is solved based on a plurality of sub cumulative retention functions through a solver, and a local optimal solution and a sub cumulative retention value of the current solution space are obtained; under the condition that the local optimal solution is located in the current solution space, dividing the current solution space based on the local optimal solution to obtain a plurality of sub-solution spaces; and determining an optimal curriculum arrangement strategy based on the local optimal solution, and scheduling the curriculums based on the optimal curriculum arrangement strategy.

The application discloses an active power flow adjusting method, device and equipment for an AC-DC distribution network, which is characterized in that: the actual active power vector of a converter station is calculated by using the rated voltage of a DC system and the parameters of the converter station, the connection node of the DC system converter station is equivalent to an AC load node, and the actual active power vector of an AC system node is solved, so that the steps of the whole adjusting process are simple, the data does not need to be iteratively processed, the solving speed is fast, and the adjusting efficiency of the active power flow of the AC-DC distribution network is improved.

The application discloses a routing and scheduling based time sensitive network topology design method, and relates to the field of industrial networks.The application carries out mathematical modeling on a TSN topology-routing-scheduling joint problem, designs an optimization target, and solves the optimization target of the TSN topology-routing-scheduling joint problem based on schedulability analysis.The application can not only effectively reduce the number of switches and cables, but also significantly reduce deployment cost and maintenance cost;by simplifying the modeling of the scheduling part and converting complex nonlinear constraints into linear / quadratic modeling, the TSN routing and scheduling problem can be directly input into a commercial solver for efficient solving;the TSN network can be quickly deployed, real-time requirements of an industrial site are met, and the TSN network is adapted to rapid changes of a flexible production environment.

The invention discloses a security constraint economic dispatching method and device, a storage medium and electronic equipment, and the method comprises the steps: training a fault classification model based on historical operation data which comprises at least one of a system load, renewable energy power generation and a node net load; corresponding active fault labels are marked on the historical operation data; acquiring real-time operation data, and inputting the real-time operation data into the fault classification model to obtain a predicted active fault set; performing model-driven safety verification on the predicted active fault set, identifying and supplementing missing active faults, and forming a target active fault set; and based on the target active fault set, solving a security constraint economic scheduling problem, and outputting a scheduling decision. Through fusion of data-driven fault prediction and model-driven safety verification, on-line calculation efficiency of safety constraint economic dispatching can be improved on the premise of ensuring an 'N-1' safety criterion of a power system.

This invention relates to a dual-timescale task offloading and resource allocation method for multi-timeslot MEC systems, comprising: establishing a long-term average energy consumption minimization model for a multi-user, multi-server MEC network; solving the long-term average energy consumption minimization model at two timescales; on the one hand, at a smaller timescale, decoupling the long-term stochastic task offloading and resource allocation problem into a series of online optimization deterministic problems by invoking the Lyapunov method, obtaining closed-form solutions for offloading decisions and resource allocation at each timeslot; on the other hand, at a larger timescale, formulating the server active / sleep mode selection and user-server association decision problem as a constrained Markov decision process, and employing a bi-adversarial deep Q-network (D3QN) from deep reinforcement learning to learn the mode selection and association decisions. Compared with existing technologies, this invention can effectively reduce the computational complexity of multi-timeslot systems and improve the solution speed.

This invention discloses an accelerator and method for solving triangular sparse equations in a power system. The accelerator includes a general-purpose processor and an acceleration processor. The general-purpose processor determines the level of each row node in the triangular sparse matrix according to the dependencies in the solution process, wherein higher-level row nodes depend on lower-level row nodes, and row nodes at the same level are independent of each other. The acceleration processor includes multiple solution processing units related to the solution. The acceleration processor performs a first solution on the triangular equations based on the non-zero elements of the lower-level row nodes, and performs a second solution on the triangular equations based on the non-zero elements of the higher-level row nodes and the result of the first solution. When there are multiple row nodes at the same level, the acceleration processor processes the data of different row nodes at the same level in parallel through multiple solution processing units to solve the triangular equations in parallel, thereby improving the solution efficiency.

The application provides a tension-compression coupled gap beam unit and an iterative calculation method thereof, and belongs to the field of bolted structure design and safety evaluation. A specific degree of freedom is released in an existing unit stiffness matrix, and an axial gap control parameter is introduced, so as to realize coupled simulation of tension-compression bidirectional gap and bending moment transmission. Meanwhile, an adaptive iterative calculation method embedded with dichotomy is provided. The tension-compression coupled gap beam unit can realize coupled simulation of tension-compression bidirectional gap and bending moment transmission, can solve the problem that the prior art gap rod unit cannot consider horizontal bending moment, and provides a reliable means for accurate analysis of internal force and deformation of a three-dimensional bolt (pin) joint structure and has high precision. The calculation method of the gap beam unit comprises the following steps: (1) degree of freedom condensation and release; (2) matrix element supplement; (3) supplement of axial gap, specific in-plane bending stiffness and rod torsional stiffness elements.

The invention discloses a quantitative description method for a transient synchronous stability boundary of a multi-converter system, and belongs to the technical field of power electronic control. Aiming at the challenges that the relative power angle motion between the converters in the multi-converter system is difficult to accurately describe and a multi-group relative power angle coupling action mechanism is difficult to analyze, the input-output stability of a relative power angle motion subsystem between every two converters in the multi-converter system is analyzed; and constructing a dissipation matrix for describing the motion stability property of each power angle of the multi-converter system and the interconnection relationship of the power angle motion, and realizing quantitative description of the dissipation property of the multi-power-angle synchronous motion of the multi-converter system. Furthermore, a Lyapunov function for describing the power angle motion of the multi-converter system is constructed, so that the quantitative estimation of the transient synchronous stability boundary of the multi-converter system is realized.

The application discloses a data center management method and system and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring power data of a data center server, and calculating server energy consumption; acquiring cooling data of the data center server; establishing a target function according to the cooling data and the server energy consumption; acquiring the utilization rate and the task amount of the data center server in real time; solving the target function by using a particle swarm algorithm; and performing task allocation on the data center according to the solution of the target function. The power data comprises processor power data, memory power data, disk power data and interface power data. The application models the server energy consumption according to the power data of the data center server, considers the task amount and the cooling capacity of the server, models and solves the total energy consumption of the server, obtains a task allocation scheme in which the total energy consumption is the lowest and the cooling of each server is sufficient, and reduces the energy consumption of the data center.

The application provides a joint-restricted redundant robot arm position layer repetitive motion and obstacle avoidance method and system, and a robot. The method comprises the following steps: (1) determining a safe distance between the robot arm and the obstacle; (2) constructing a multi-task optimization scheme for the robot arm position layer repetitive motion and obstacle avoidance based on a quadratic programming (QP); (3) converting the QP problem in step (2) into a nonlinear equation set based on a nonlinear complementarity problem (NCP) function; (4) designing a zero neural network (ZNN) solver to solve the nonlinear equation set according to a ZNN evolution law; and (5) transmitting the solution in step (4) to a lower machine controller to drive the robot arm to complete a specified end task. The application designs a multi-task optimization scheme in the position layer, so that the robot arm can realize repetitive motion, obstacle avoidance and joint limit avoidance in the position layer, and can avoid physical damage of the robot arm without affecting the end task.

The invention discloses an event constraint unit commitment robust optimization solving method based on primary function adaptive chaos polynomial expansion, and the method comprises the following steps: 1) building an event constraint unit commitment robust optimization model which comprises a day-ahead unit commitment model and a state evaluation model considering a composite scene; 2) carrying out approximation on the state evaluation model by adopting base self-adaptive sparse chaos polynomial expansion, and constructing a state evaluation agent model; 3) replacing a state evaluation model in the unit commitment robust optimization model with a state evaluation agent model; 4, the unit commitment robust optimization model after replacement is solved, and a unit commitment plant.The state evaluation model is reconstructed through the relaxation technology to improve the smoothness of model response, on this basis, an agent model of the state evaluation model is established based on BASPCE, and the precision of the agent model is remarkably improved.