45results about How to "Solve modeling problems" patented technology

The invention discloses a simultaneous localization and map construction method of a monocular vision robot based on a retracking strategy. The method comprises the following steps: 1) extracting ORB characteristics of each picture; 2) tracking camera postures by utilizing characteristic matching of the adjacent pictures; 3) carrying out relocation and retracking strategy on pictures which are not tracked; 4) if the relocation is successfully executed, estimating postures of a current camera; stopping the retracking strategy and deleting a temporary variable generated by the retracking strategy; 5) if the retracking strategy is successfully executed, generating a new track; 6) judging the quantity of the generated tracks; if the quantity is more than a threshold value, eliminating an initial track; 7) carrying out closed-loop detection on each key frame and carrying out track fusion after the detection is successful; 8) when a localization system is finished, screening the tracks to obtain one track with the accurate posture. The simultaneous localization and map construction method disclosed by the invention has the advantages that the complete camera track can be located under the conditions of rapidness in movement, shielding, insufficient textures and illumination changes.

The invention provides a virtual power plant combined heat and power scheduling robust optimization model. A model aggregation unit comprises a distributed generating set, a wind turbine generator set, a photovoltaic set, a combined heat and power (CHP) set, a boiler, electric energy storage, heat energy storage, an electric load and a heat load. Participation of the CHP set in the SRM (Spinning Reserve Market) situation is considered. Aiming at the facing uncertain problem of a virtual power plant (VPP) and resulting risks, robust optimization (RO) is utilized to process uncertainty of the EM electricity price, the SRM electricity price, the wind power capacity, the photovoltaic capacity, the electric load and the heat load, and risk quantification indexes are established, and thus robustness and economical efficiency of a RO model are balanced. The model provided by the invention well solves the existing combined heat and power scheduling optimization model establishment problem of the VPP when participating EM and SRM at the same time, and improves flexibility of decision making, and thus the profit of the VPP is increased. Meanwhile, the introduction of the RO model effectively reduces system risks, and thus the effective reference is provided for a decision maker to select a proper robust factor.

The invention discloses an oilfield pumping unit oil pumping energy saving and production increasing optimization method based on the back propagation neural network (BPNN) and the strength Pareto evolutionary algorithm 2 (SPEA2). The method is characterized by including the following steps: step 1, calculating decision variables X; step 2, collecting samples of power consumption and samples of oil production Y to acquire a sample matrix; step 3, building a process model of oil pumping of a pumping unit; step 4, optimizing each decision variable in the range of an upper limit and a lower limit of each decision variable by using the SPEA2 based on a BPNN model; step 5, guiding actual production if the power consumption is reduced and the oil production is improved, and if not, returning the process to the step 1, changing S1 decision variables X on purpose and screening the decision variables X again; and step 6, assigning S1+1 to the S1, and returning the process to the step 1 if the combination of the set S1 decision variables X can not enable the power consumption to be reduced and the oil production to be improved. The oilfield pumping unit oil pumping energy saving and production increasing optimization method based on the BPNN and the SPEA2 has the advantages that an optimal value of technological parameters can be determined, and actual production guiding can be carried out according to the optimized technological parameter optimal value.

The present invention provides a self-calibration unscented Kalman filter method, the steps are as follows: one: establish a nonlinear discrete system including unknown input; two: initialize the system composed of formula (1) and formula (2); three: Time update the system; four: combined with the measurement information, measure and update the state one-step prediction and one-step prediction error variance matrix; five: perform iterative calculation; through the above steps, the present invention introduces the self-calibration technology into the unscented Kalman Filtering method, the newly obtained self-calibrating unscented Kalman filtering method firstly solves the modeling problem of the nonlinear system affected by the unknown input. The adverse effect of filtering results greatly reduces the phenomenon of filtering divergence, thereby improving the filtering accuracy, and at the same time, as a filtering method that can resist system uncertainty, it improves the robustness of the system.

The invention relates to a method for building an energy function of a generator excitation controller, and belongs to the technical field of generator control. According to the method, a hamilton energy function of the generator excitation controller comprising an exciting automatic voltage regulator (AVR) and a power system stabilizer (PSS) is built by a supply rate method; the supply rate is described by input and output variables of various links; and after the energy function of the controller is obtained, the energy function of the controller is added to a generator hamilton energy function, so as to obtain the generator energy function comprising the overall excitation system. By the supply rate method, the modeling problem of the energy function of the controller is solved; the controller can be conveniently written into the generator hamilton energy function; and the correctness of the modeling method is validated through a simulation result.

The invention discloses a multi-loop multi-splitting ground wire three-dimensional model real-time construction method for a 500kV tower. The method is characterized in that firstly, according to basic machine account data, tower and mount point model construction is finished in advance, the corresponding relationship of tower mount points is determined by means of a tower mount point encoding relation algorithm, and a ground wire sag curve in a design state is simulated; then, by means of business parameter fusion, ground wire spatial connection is carried out automatically according to actual conditions, and the ground wire sag curve in the design state is corrected; then, by means of business parameter fusion, ground wire spatial connection is carried out automatically according to actual conditions. The method guarantees modular generation of three-dimensional forming, accords with actual operation conditions, achieves the aim of three-dimensional simulation of a power transmission line, and has important economic and social value.

The invention provides a distribution function-based WCET (Worst Case Execution Time) quick estimation method. The method comprises the following steps: disassembling a DSP (Digital Signal Processor) project objective code (out file) to obtain a disassembled file F; analyzing the disassembled file F to acquire each divided basic block to obtain a basic block set B of a program; recognizing relation between every two basic blocks in the basic block set B to construct a program flow graph C; calculating execution time T of each basic block; obtaining weighted program flow graph Cw by weighting the basic block execution time T and the basic block execution frequency Ts; analyzing the weighted program flow graph Cw to obtain a path with the maximum total weight value and taking the maximum total weight value as the WCET. The defect that a program is required to be operated to obtain a test sample in the prior art is overcome, and the problems of more manual intervention and irrationality of a beta distribution parameter estimation method in the conventional PERT (Program Evaluation and Review Technique) are solved.

The invention discloses an aero-engine reliability analysis method based on a multi-state multi-valued decision diagram, and mainly solves the problem of overlarge state space in multi-state system modeling in the prior art. The implementation scheme comprises the following steps: 1) establishing a multi-state multi-value decision diagram model of a bottom event; 2) sorting the bottom events of the dynamic doors; 3) establishing a multi-state multi-value decision diagram model of the static door and the dynamic door; 4) sequencing the bottom events in a non-descending order according to the number of event states; 5) adjusting the sequence of the bottom events in the step 4); and 6) establishing a multi-state multi-value decision diagram model of the aero-engine rotor system according to the bottom event sequence of the step 5). According to the method, the influence of the state number of the bottom event on the size of the multi-state multi-value decision diagram is considered; the optimized bottom event sequence is obtained by sorting the bottom events, the size of the multi-state multi-value decision diagram is reduced, the scale of the state space in the system reliability analysis model is reduced, and the method can be used for carrying out reliability analysis on the aero-engine rotor system.

The invention discloses a modeling method of an LCC-MMC series hybrid direct-current power transmission system, and belongs to the field of power system modeling. The LCC-MMC series hybrid direct-current power transmission system is divided into two parts, namely a main circuit and a controller, an equivalent circuit of a converter is used for replacing the converter, the equivalent circuit of themain circuit is established, and then a dynamic model of the main circuit is established by utilizing a Kirchhoff voltage law KVL and a Kirchhoff current law KCL. The equivalent circuits of the LCC and the MMC are used for replacing the LCC and the MMC for the first time, and the modeling problem of the LCC-MMC series system is solved. A dynamic model of the LCC-MMC series hybrid direct current power transmission system is a basis for establishing a steady-state model and a small-signal model of the LCC-MMC series hybrid direct current power transmission system. The whole modeling method hasthe advantages of being simple to operate and easy to expand.

The invention provides a hybrid state estimation method for a high-dimensional deep coupling uncertain system, which comprises the following steps of: firstly, constructing a state, parameter and measurement model of the high-dimensional deep coupling uncertain system, and designing a corresponding observer model to obtain estimated values of the state and the parameter; secondly, discretizing thesystem to decompose the system into a low-dimensional discretized hybrid model, and further obtaining the low-dimensional discretized hybrid model and a parameter model; and finally, taking the observer output estimation value as an auxiliary signal, performing filtering processing on the state estimation value of the low-dimensional discretization hybrid model by using a volume Kalman filteringalgorithm, and outputting a state value of the low-dimensional discretization hybrid model. According to the method, the system model is corrected through the estimated value output by the observer, the system state estimation precision can be effectively improved on the premise that the system stability is guaranteed, meanwhile, the calculation dimension of the filtering calculation process is reduced through the low-dimensional volume Kalman filtering algorithm, and the method is suitable for high-dimensional, coupled and nonlinear system state estimation with uncertainty.

The invention discloses a crack image identification and modeling method. The method comprises the following steps: 1, carrying out coarse identification on a crack image based on a deep learning Mask-RCNN network and obtaining a coarse extraction crack image; 2, performing OpenCV image processing on the roughly extracted crack image to obtain pixel coordinates of inflection points and abrupt change points of the cracks on the two-dimensional image; 3, performing image feature matching by using an SIFT algorithm to obtain coordinates of homonymous points; and 4, constructing a three-dimensional model of the crack. According to the invention, the advantages of the deep learning image recognition technology and the OpenCV image processing technology are combined; the image recognition methodfrom coarse recognition to accurate recognition is formed, the recognition accuracy is effectively improved, crack three-dimensional modeling is finally generated by means of extraction of picture coordinates of different angles at the same crack position, the problem that cracks are difficult to recognize and model due to small sizes is solved, and the method is high in feasibility and robustness.

The invention discloses a flexible load modeling method and device of a load side, and the method comprises the steps: constructing an interruptible load model according to the demand response characteristics of an interruptible load; constructing a transferable load model according to the demand response characteristics of the transferable load and the demand response excitation strategy of the transferable load; constructing a user side energy storage model according to the service mode of the user side energy storage power station; performing equivalence on the air conditioner load by adopting the equivalent thermal parameter model, and constructing an air conditioner load model; constructing an electric vehicle load model according to the operation characteristics of different types of electric vehicles; and in combination with the interruptible load model, the transferable load model, the user side energy storage model, the air conditioner load model and the electric vehicle load model, obtaining a load side flexible load model. By adopting the flexible load model of the load side constructed by the invention, the flexible load of the load side can be accurately analyzed, and the coordinated scheduling of various flexible loads can be optimized.

The invention provides a heterogeneous data cooperative modelling industrial fault detection method based on neighborhood projection preservation, and belongs to the technical field of the fault monitoring and diagnosis. The method comprises the specific steps as follows: collecting N groups of current and image data; solving a structure weight coefficient of each image data; establishing a current neighborhood projection map dovetailing matrix; rejecting the data greater than the current neighborhood weight threshold, and performing normalization; constructing a target function, and solving aprojection vector optimal solution by using a manifold dimension reduction method; performing dimension reduction on the image data produced in the electrolytic magnesium process after graying; and performing fault detection on the dimension-reduced image data by using a SVM classification method. A heterogeneous data modelling problem is mainly solved, the fault detection is performed when the image information and the current information are combined, a process monitoring result for the method shows that the false alarm is greatly reduced, and the accuracy of the fault detection is furtherimproved.

The invention discloses a three-dimensional stratum modeling method based on drilling data, and belongs to the field of computer science visualization, and the method comprises the steps: carrying out the statistics of original drilling data in a modeling region through the processing of the drilling data, and obtaining a table related to the drilling number, sounding depth, coordinate data and layering information; establishing a point model reading table, establishing a drilling database, and constructing hierarchical point model data according to drilling information; kriging interpolation is carried out, a layered point data model is exported for Kriging interpolation, virtual drilling data of each layer is obtained and imported into a drilling database, and the established point model is updated; surface models are established, each layer model is established according to the drilling data, and the curved surface is edited according to the contact condition of the control points and the curved surface; the method comprises the following steps: establishing a three-dimensional stratum model, selecting a modeling area, selecting all stratums to construct stratum columns, establishing a stratum plane, adjusting the stratum plane according to an actual profile map and the like, establishing a geological grid, and finally generating the three-dimensional stratum model.