13results about How to "Simplify the optimization process" patented technology

The invention relates to an equivalent two-level fast double-vector model predictive control method applied to a three-level inverter. The method specifically comprises the following steps: sampling output current and direct current side upper and lower capacitor voltages at a k moment; predicting an expected voltage according to the mathematical model of the three-level inverter and the expected current; performing first value function calculation according to an equivalent two-level vector set composed of positive and negative small vectors to obtain an optimal small vector; calculating a second value function according to the equivalent two-level vector set where the optimal small vector is located to obtain a suboptimal vector; according to the characteristic that the vector action time is inversely proportional to a value function, the action time of an optimal small vector and the action time of a suboptimal vector are calculated; and applying a switching signal under the combined action of the optimal small vector and the suboptimal vector to a three-level inverter. According to the method, the vector set equivalent to two levels is established, so that the vector optimization process is simplified, and the model calculation time is greatly shortened; a value function of a single control target is established, and the tedious design process of weight factors is omitted; and a double-vector model predictive control method is adopted, so that output current harmonics are reduced.

The invention discloses an auxiliary system and method based on data mining for technology identification, and relates to the field of data matching analysis. The invention trains and tests a recurrent neural network model to construct a final recurrent neural network model, and provides a model for extracting keywords from patent document data to be analyzed later; trains and tests an SVM classification model to construct a final SVM classification model, and provides a classification model for classifying keywords from patent document data to be analyzed extracted by the final recurrent neural network model later; and simultaneously stores the queried technology identification data in separate libraries, and allocates different permissions to different users according to the number of times different users call these different types of technology identification data, thereby reducing invalid calls by users, avoiding resource occupation, and improving the efficiency of calls.

The invention discloses a method for evaluating lag time of influence of blast furnace ironmaking operation parameters on state parameters, which comprises the following steps of: constructing a mapping relation between time delay sequence data of operation variables and blast furnace state parameters, and processing blast furnace state parameter data by adopting modes such as wavelet filtering and difference so as to construct a drift term, a diffusion term and a random process model. And then, evaluating the operation time delay through the established stochastic differential equation simulation result. Constraint conditions are added for optimization of the model according to the blast furnace smelting characteristics and the hierarchical clustering result, and the optimization efficiency and precision are further improved. Finally, the importance of the parameters is evaluated based on the gain or SHAP algorithm, the regulation and control priorities of the operation variables are ranked, and a basis is provided for regulation and control of the blast furnace ironmaking process. The method has the advantages that the delay time of the influence of the operation variables on the state parameters of the blast furnace can be accurately evaluated by fusing the production rule of blast furnace smelting, the production cost is reduced while the quality and the efficiency of the ironmaking process are improved, and the method has a wide application prospect.

The present invention relates to the field of image recognition, and in particular to a fully interpretable staging prediction method, device and medium for small sample rectal cancer MR images; the co-occurrence matrix of the preprocessed rectal cancer MR images is calculated to extract the physical features of each image, and the real label is also used as the physical feature; K clusters are determined according to the image and the real label to characterize the staging stage of rectal cancer; the relevant information of the K clusters is obtained by using a fuzzy C-means algorithm for the physical features; a TSK fuzzy system is trained according to the relevant information of the K clusters to obtain an initial rectal cancer staging prediction model; the rectal cancer MR images and the real labels are input into the model to output the staging prediction value; the objective function of the fuzzy rule weight in the model is constructed by using the prediction error, the output variance and the improved feature distribution loss; the objective function is solved in the form of an analytical solution to obtain the optimized rule weight and the rectal cancer staging prediction model, so as to realize fully interpretable staging prediction based on a small number of rectal cancer MR images.

The invention provides an active power distribution network low-carbon economic dispatching optimization method based on carbon-energy coupling, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a double-chromosome model consisting of a carbon chromosome and an energetic chromosome; binding carbon-energy coupling constraint for the carbon chromosome and the energy chromosome, and initializing the double chromosome model; constructing a total cost function including an energy cost item and a carbon constraint penalty item; constructing a selection operator based on the total cost function, a crossover operator based on carbon violation feedback and a mutation operator of the genetic algorithm; constructing a local optimization function of the genetic algorithm; and performing iterative optimization on the double-chromosome model by using the optimized genetic algorithm, and obtaining an optimal scheduling scheme after iteration is finished. According to the method, through the double-chromosome model, the unified cost function, the adaptive genetic operator and the enhanced local optimization function, collaborative optimization of economical efficiency and low-carbon targets of the power distribution network can be realized, and a high-quality optimization result is provided for actual dispatching operation.

The invention discloses a PCI (Peripheral Component Interconnect) replanning method and device. The method comprises the steps that cell information containing position information, adjacent cell information containing MOD3 interference information and PCI replanning configuration information containing a PCI to be distributed, the minimum multiplexing distance and the number of layers of all cells in a target area are obtained, and the target area covers a PCI replanning area and adjacent areas in a peripheral preset range of the PCI replanning area; dividing all the cells into a plurality of physical location points according to location information, wherein each point corresponds to three cells with the same location; dividing the to-be-distributed PCIs into a plurality of PCI groups according to the MOD3 values, wherein each group comprises three PCIs with different MOD3 values; and determining a first PCI group of each physical location point of the replanning area from the PCI groups by combining the minimum multiplexing distance and the number of layers on the basis of a PCI conflict-free principle of adjacent cells, and distributing an optimal PCI for the cell of each location point according to the MOD3 interference information. According to the method and the device, the technical problem that PCI distribution cannot be accurately and efficiently completed due to the fact that a current PCI replanning process is easily influenced by problem data is solved.

The invention provides a scene interface optimization method and device, a storage medium and electronic equipment, and is applied to the technical field of computers, and the method comprises the steps: determining a target node in a node tree of a scene interface currently in a display state, and when the target node is a non-leaf node, generating a node view of the target node, the method comprises the following steps: displaying each child node of a target node in a node view, after receiving node modification information of the node view, determining a target child node in each child node by using the node modification information, modifying attribute data of the target child node, and then according to the modified attribute data of the target child node, determining the target child node according to the modified attribute data of the target child node. According to the method, the scene interface which is being displayed is re-rendered, so that the scene interface is optimized, and a worker does not need to spend time in searching the corresponding code for modification in the optimization process, so that the optimization process is simpler, the optimization application time is shorter, and the optimization efficiency is improved.

The invention provides a CMOS (complementary metal oxide semiconductor) inverter performance evaluation method, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining the width size information of a target PMOS (P-channel metal oxide semiconductor) of a CMOS inverter, and enabling the threshold voltage of the CMOS inverter to be equal to half of the operation voltage when the width size of a PMOS tube in the CMOS inverter is the width size of the target PMOS; according to the target PMOS width size information, performing circuit simulation on the CMOS phase inverter to obtain target performance parameter information of the CMOS phase inverter under the target PMOS width size; and according to the target performance parameter information, carrying out Monte Carlo simulation on the CMOS inverter for a preset number of times to obtain performance parameter distribution information of the CMOS inverter under the target PMOS width size. According to the method, the performance parameter distribution characteristics of the CMOS phase inverter during mass production can be known in advance, so that the resource investment for performance analysis of the CMOS phase inverter at the initial stage of production and manufacturing can be effectively reduced.

The invention discloses a humanoid robot real-time limb trajectory optimization method and device based on centroid dynamics and a robot, and belongs to the field of robot motion control. The method comprises the following steps: acquiring a robot take-off state and a next grounding target; based on the conservation of angular momentum in the flying stage, constructing an optimization problem which takes a joint trajectory polynomial coefficient as an optimization variable and takes the minimization of the body direction deviation during grounding as a target and comprises kinematics position and speed constraint; the core of the method is that the key property that a translation coupling term in a centroid angular momentum matrix is zero is utilized and proved, so that body rotation dynamics and centroid translation are completely decoupled, and a complex whole-body trajectory is optimized and simplified into an efficient problem which can be solved in real time at a millisecond level; according to the method, a coordinated limb swinging track can be automatically generated, the body posture is accurately controlled in the running and jumping flying stage, and manual intervention is not needed.

This invention provides a method and apparatus for optimizing a filtered antenna. The method includes: obtaining the value range of the antenna structural parameters to be optimized in the target filtered antenna; iteratively optimizing the full-band electromagnetic response sequence of the target filtered antenna using a multi-objective optimization algorithm based on a pre-constructed multi-objective evaluation function and the value range, to obtain the optimal full-band electromagnetic response sequence of the target filtered antenna; and optimizing the target filtered antenna based on the target values ​​of the antenna structural parameters corresponding to the optimal full-band electromagnetic response sequence. The multi-objective evaluation function is determined based on a trained target multi-resolution Transformer surrogate model, which is trained based on sample value data corresponding to the antenna structural parameters and simulated values ​​of the full-band electromagnetic response sequence. This invention can achieve collaborative optimization of multiple performance indicators, with high optimization accuracy, short optimization time, and low optimization cost.

The invention provides an automatic calculation and design method for a grouting reinforcement project amount in a rail transit tunnel, which comprises the following steps of: running an AutoCAD secondary development program, opening a design scheme file of a grouting reinforcement longitudinal section in the rail transit tunnel, and reading data; inputting a grouting reinforcement design and engineering quantity calculation range in the rail transit tunnel; based on the read deviation value of the grouting reinforcement range in the tunnel hole and the tunnel radius, the grouting reinforcement radius and the grout reinforcement range angle in the rail transit hole are automatically calculated; and finally, the grouting reinforcement work amount in the rail transit tunnel is automatically calculated. According to the invention, automation and intelligentization of the whole design process are realized, and the working efficiency is greatly improved; high precision and high consistency of the calculation process are ensured, and human errors are greatly reduced; digitization and structuring of design parameters are promoted, and management and scheme iteration are facilitated; and designers are liberated and are prompted to concentrate on creative work with higher value.

The present invention discloses a voltage-reactive optimization method and system based on time-space-aware safety reinforcement learning, constructs a PE-VVO framework taking into account photovoltaic PV and electric vehicle charging station EV, and converts it into a constrained Markov decision process; the PE-VVO framework includes an objective function, constraints, and decision variables, and the Markov decision process includes a state space, an action space, a reward function, constraints, a state transition probability distribution, and a PDN environment; designs a TGCN-IPO algorithm for solving CMDP, including the algorithm taking into account the goal of the obstacle function and the Actor and Critic network taking into account TGCN; designs the training rules of the TGCN-IPO algorithm, and uses real historical operating data and a PDN simulation environment to train the TGCN-IPO agent; uses a test set to test the trained TGCN-IPO agent, and deploys it online to complete the online voltage-reactive control of the distribution network. This technical solution can solve the problems of increased operating costs and voltage over-limit caused by high-penetration PV and EV to the distribution network.

The application discloses a group of nucleic acid aptamers specifically combined with FAP proteins and application thereof. The application provides nucleic acid aptamers targeting FAP proteins, wherein the nucleic acid aptamers comprise a core structure; and the core structure is a G-rich hairpin structure. In order to fully utilize the rich aptamer candidate library generated in screening and collect more information for truncation and optimization of the aptamers, a machine learning clustering method is adopted to identify the core region in each aptamer family and perform secondary structure analysis, and a common structure (G-rich hairpin structure) capable of specifically combining with FAP proteins is successfully found. The common structure simplifies the truncation and optimization process of the aptamer candidate library, and promotes the design of novel aptamers which can combine with FAP proteins with high specificity and high affinity.