28results about How to "Improve optimization effect" patented technology

The invention provides a code optimization method and system based on a knowledge graph, and the method comprises the steps: carrying out the source code analysis based on a to-be-optimized code library, constructing an abstract syntax tree, and determining a to-be-optimized code snippet based on the code complexity of a corresponding code in the abstract syntax tree; determining a reconstruction mode corresponding to each to-be-optimized code snippet based on the to-be-optimized code snippet in combination with a pre-constructed code optimization knowledge graph, and constructing a context dependency graph of each to-be-optimized code snippet based on the abstract syntax tree and the code optimization knowledge graph corresponding to each to-be-optimized code snippet; performing sliding window decomposition based on the context dependency graph to obtain a plurality of mutually associated code blocks, and performing simplification and fusion on the code blocks to obtain a current context; and inputting the reconstruction mode corresponding to each to-be-optimized code snippet and the current context into the large language model for structured reconstruction to obtain a target reconstruction scheme output by the large language model. According to the method and the device, the code optimization effect is improved.

The application discloses a kind of considering user false information's electric vehicle space-time scheduling optimization method and system, belong to electric vehicle optimization scheduling field.Scheduling center needs to minimize overall charging cost under the charging demand constraint of satisfying all electric vehicles, user once lies report demand, possibly will reduce own charging cost, but the charging cost of other electric vehicle users will rise, leading to the rise of total cost, to solve this problem, based on the space-time two-dimensional electric vehicle optimization scheduling framework, a new charging cost billing mechanism is designed, considering the influence of the charging strategy reported by user on optimization scheduling result, so that individual charging cost minimization and overall charging cost minimization tend to consistency.Not only reduce the charging cost of each user after participating in scheduling, but also avoid the phenomenon that user chooses to report false information to gain profit when participating in scheduling, better encourage user to participate in scheduling, improve optimization effect.

The invention relates to the technical field of edge computing and automatic optimization algorithms, and discloses a multi-edge computing task unloading and scheduling hierarchical automatic optimization method based on large language model driving, which comprises the following steps: S1, constructing a computing system, acquiring task data and edge server data to form a data set, dividing the data set into a training set, an optimization set and a verification set; and S2, constructing a joint task unloading and scheduling optimization model taking the total completion time delay of the system as an optimization target, taking minimization of the total completion time delay of the system as an optimization target function, and taking the total completion time delay of the system obtained by the joint task unloading and scheduling optimization model as a fitness value. According to the method, efficient and automatic optimization of task unloading and scheduling problems in the multi-edge computing environment is achieved, the intelligent level and generalization ability of a computing system are improved through an algorithm evolution mechanism driven by a large language model, and the method has important theoretical significance and wide engineering application prospects.

The application relates to a method and system for identifying infrastructure damage based on an improved MCMC method. The method comprises the following steps: acquiring a mode of a test cantilever beam; forming a population according to a Markov chain obtained from a standard MH algorithm according to the measured mode; iteratively including a normal period with only differential evolution and a competition period with competition after the normal period; obtaining two groups of correction vectors after mutation, crossover and selection in each iteration in the competition period, combining the two groups of correction vectors with correction vectors in the normal period, and recording two groups of complete iteration Markov chain parameter optimization schemes as Y W and Y L ; calculating the average value of the corrected parameters after convergence as Y W and Y L , learning Y W from the winner and Y L from the loser, and taking Y W as the optimal correction scheme according to comparison with the existing measured mode. By adopting the scheme, the calculation efficiency is high, the precision is higher, and the robustness of the system is stronger.

The application discloses a kind of MMC fracture model correction method and device, first based on the fitting MMC initial point obtained by a plurality of fitting of tensile test to initial MMC fracture curve is obtained, secondly, the initial fracture curve is divided according to preset division rule to obtain first, second, third segment data interval, finally, the second, third segment data interval is linearly adjusted to obtain adjusted MMC fracture curve.Compared with prior art, the adjusted MMC fracture curve of the application can have greater freedom to adjust MMC fracture curve on the basis of preserving linear characteristics and curve smoothness.If it is combined with optimization algorithm, the force-displacement curve error between simulation and experiment is minimized as the goal to carry out automatic feature point adjustment, optimization iteration can be completed faster, and better optimization effect and shorter optimization cycle can be achieved.

The invention discloses a micro-grid scheduling optimization method and system based on a multi-objective parrot-shaped Arctic pole optimization algorithm, and the method comprises the steps: building a multi-objective optimization scheduling model of a micro-grid, and the multi-objective optimization scheduling model at least comprises an operation maintenance cost objective function and an environment protection cost objective function; a multi-strategy fused multi-target parrot-shaped north pole optimization algorithm is adopted to solve the multi-target optimization scheduling model, and a Pareto optimal solution set is obtained; and determining an optimal output strategy of each distributed power generation unit in the microgrid according to the Pareto optimal solution set. The space exploration capability of the algorithm in the early stage and the optimization precision of the algorithm in the later stage are ensured, and the optimization effect of micro-grid dispatching is improved.

The invention discloses a database query optimization method and device and a storage medium, and the database query optimization method comprises the steps: extracting statement features of an input database query language, inputting the statement features into a pre-trained task optimization model, and obtaining a plurality of candidate query execution plans; evaluating the execution effect of each candidate query execution plan, and selecting the candidate query execution plan with the optimal execution effect to obtain an optimal plan; acquiring a real-time system load corresponding to a database and / or a business priority corresponding to a database query language, and performing parameter adjustment on the optimal plan based on the real-time system load and / or the business priority to obtain a target plan; and executing the target plan on the database. After the optimal plan is obtained, the parameters of the optimal plan are adjusted according to the real-time system load and / or the service priority, so that the finally obtained target plan can be adapted to the current service scene, and the optimization effect in a complex dynamic scene is improved.

The invention discloses a network optimization method and device based on a digital communication cluster, equipment and a medium, which can be applied to the technical field of communication. The method comprises the following steps: after obtaining first current network coverage information of a to-be-detected area in a digital communication cluster, generating a network optimization task according to the first current network coverage information; performing network optimization on the to-be-detected area according to the network optimization task, obtaining network optimization coverage information after network optimization, and generating a network optimization scheme according to the network optimization coverage information and the first current network coverage information; therefore, the network optimization scheme can be used for subsequently generating the network optimization task according to the second current network coverage information, so that repeated drive test for network optimization is not needed, the manpower and weakness of network optimization are effectively reduced, and the network optimization effect is improved.

The invention discloses a display processing method and device, a storage medium and terminal equipment, and relates to the technical field of display, and the method comprises the steps: obtaining original frame data of a frame displayed in an interface scrolling stage; determining frame time consumption data according to the original frame data, the frame time consumption data being time consumption data of a frame display process; and carrying out lagging analysis according to the frame time consumption data to obtain a lagging condition in the interface rolling stage so as to carry out rolling display optimization on the interface according to the lagging condition. According to the method, the optimization effect and the optimization reliability of interface scrolling display in the interface scrolling scene can be improved.

The present application relates to a kind of urban green space scene laser radar point cloud single tree segmentation method, the method comprises: the point cloud of urban green space is carried out grid cubic processing;From the initial trunk position in grid is determined to fit DEM elevation plane, from DEM elevation plane upwards cutting tree bottom point to determine tree position;Using K-means clustering to carry out rough segmentation;Then according to single tree position, establish tree adjacent relationship, using ring extraction to carry out single tree optimization segmentation.The present application can make the trunk point cloud feature more obvious compared to tree crown point cloud, and the ground elevation information of the position where the trunk is located can be obtained, and compared with clustering time efficiency, it is based on the trunk point cloud and elevation information obtained to carry out a series of operations to complete rough single tree segmentation, and using adjacent relationship and ring extraction to optimize segmentation result, can make the accuracy of identification result higher.

This invention discloses a Java code layout optimization method that does not interrupt application services. The method includes: acquiring front-end bottleneck data of the application; when it is determined that code layout optimization is needed based on the front-end bottleneck data, collecting the dynamic control flow graph of the application service; calculating a new code layout based on the dynamic control flow graph; generating new code according to the new code layout; correcting relocation information in all code, that is, redirecting function calls and data references pointing to the old code to the new code. During the correction process, memory barriers and cache maintenance instructions are used to ensure the atomicity and consistency of the modification operations, so as to achieve uninterrupted application services; only reclaiming the memory space used by the old code that is no longer executed to store the new code generated in the next optimization, retaining the function code still on the call stack, so that most of the memory space can be reclaimed without interrupting application services. This method effectively alleviates the processor front-end bottleneck of Java applications and improves application performance.

The present application relates to the technical field of integrated circuit mask design, and particularly relates to a mask optimization method and a storage medium, the mask optimization method comprises the following steps: obtaining an initial design graph; performing edge breaking on the initial design graph; obtaining a first distance and a second distance that each broken edge can move based on a mask manufacturing rule and the position of each broken edge; performing mask optimization on the initial design graph, and taking the first distance and the second distance that each broken edge can move as a constraint condition of an optimization algorithm to obtain a target optimization graph. By applying the mask manufacturing rule to the mask optimization, it is ensured that the graph in the optimization process and the target optimization graph meet the mask manufacturing rule requirement, without the need for additional separate mask manufacturing rule checking, greatly improving the efficiency of mask production, and ensuring the yield of the mask. The present application also provides a storage medium.

The application discloses a personal image privacy protection method, system and electronic equipment. The method provided by the application allows a user to specify a certain person in a protected image through language expression, generates multi-scale visual features with sufficient fusion of image and text information by using a lightweight deep neural network to perform parallel coding on input reference information and personal images, generates a stable specified personal privacy protection image mask through a multi-scale feature fusion and mask positioning enhancement module in the decoding process, and in addition, introduces a balanced binary cross-entropy loss to solve the pixel imbalance problem in training, optimize the network performance, and improve the personal image privacy protection effect. The application can solve the problems of excessive protection of content and pixel imbalance in the training of the reference personal image privacy protection network in the existing personal image privacy protection technology.

The present application relates to the technical field of remote control, and discloses a method for changing a chute into an inclined chute for a wind chamber scraper based on a tilt angle adaptive control algorithm, which comprises the following steps: collecting operation parameters of the wind chamber scraper, collecting a current tilt angle of the chute to construct a tilt angle-load relationship function of the chute; analyzing material characteristics of corresponding materials of the chute to determine a target tilt angle of the chute, and combining the tilt angle-load relationship function to analyze a tilt angle error of the chute; constructing a tilt angle adaptive control algorithm of the chute to generate an adaptive control signal of the chute; transmitting the adaptive control signal to a corresponding hydraulic actuator of the wind chamber scraper to generate a chute height adjustment parameter and a scraper chain speed adjustment parameter; and analyzing a packing coefficient of the materials, and performing adaptive adjustment of the wind chamber scraper and the chute based on the packing coefficient. The present application can improve the optimization effect of changing the chute into the inclined chute for the wind chamber scraper.

The invention discloses a cyclone feeding concentration prediction method, relates to the field of iron ore grinding and separation feeding concentration prediction, and aims to solve the problem that an ore grinding process cannot be effectively optimized in real time due to the fact that ore grinding parameters are difficult to measure in real time in the current ore separation production process. According to the method, a lion group optimization algorithm is improved, the global search capability is improved by changing a lion king updating mode and introducing an attenuation factor, the problem that the algorithm is trapped in a local optimal solution is avoided, a dynamic learning strategy is introduced for a parent lion and learning lion updating mode, and the local search capability is optimized; the performance of the lion group algorithm in a complex prediction task is effectively improved, and the limitation of a traditional algorithm in solving a high-dimensional complex problem is avoided.

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 relates to air conditioning equipment and a control method thereof. The controller of the air conditioning equipment is configured to create a first set and a second set; determining a new second set based on the fitness value of each parameter value combination in the first set and the second set; performing genetic manipulation processing based on the new second set to obtain a new parameter value combination, and adding the new parameter value combination to the new second set; integrating the first set into the new second set to obtain a new first set, and returning to the step of determining the new second set based on the fitness value of the combination of the parameter values in the first set and the second set until a preset maximum number of iterations is reached; and the parameter value combination with the maximum fitness value is selected from the new first set and the new second set to serve as a target parameter value combination, and air conditioner operation parameters of the air conditioner equipment are adjusted according to the target parameter value combination. The optimization effect of the performance indexes of the air conditioning equipment is improved.

The application provides a kind of training data processing method, device and equipment and readable medium, by from the multiple categories that classification model can classify, select multiple key optimization categories.Then at least according to first screening condition, training sample data is screened from candidate sample set.Wherein, the category of the candidate sample data to be screened and any one key optimization category match, candidate sample set includes multiple candidate sample data.Because the similarity degree between any two key optimization categories in multiple key optimization categories is less than similarity degree threshold, and the similarity degree between two key optimization categories is the similarity degree between sample data under two key optimization categories, therefore, according to the training sample data between different categories screened according to first screening condition, when using the training sample data to train the classification model to be optimized, the optimization effect of the classification model to be optimized can be improved.

This invention relates to an optimization method for the energy management system of a petrochemical enterprise's oil refining unit; it includes the following construction steps: Step 1, constructing an optimization model of the energy management system of the petrochemical enterprise's oil refining unit; Step 2, constructing an improved moth-flame optimization algorithm; Step 3, collecting basic data of the energy management system of the petrochemical enterprise's oil refining unit; Step 4, solving the optimization model of the energy management system of the petrochemical enterprise's oil refining unit using the constructed improved moth-flame algorithm. The above-constructed optimization method for the energy management system of a petrochemical enterprise's oil refining unit can effectively improve the utilization rate of petrochemical energy, reduce the operating cost of the petrochemical energy system, and ensure sustainable environmental development.

This application discloses an instruction optimization method and apparatus, relating to the field of computer technology. The method includes: determining an optimization range for a global pointer in the address region of a program segment, the optimization range defining the period from the start to the end point of the program segment when instruction optimization is performed using the global pointer; controlling the global pointer to sequentially perform optimization operations at multiple locations based on the optimization range, obtaining optimized program segments corresponding to different locations; selecting the optimal program segment from the multiple optimized program segments, and determining the location corresponding to the optimal program segment as the deployment location of the global pointer, wherein the quality of the optimized program segment is negatively correlated with the number of instructions.

This invention discloses an intelligent control method for environmentally friendly gas ring main units (RNBs) used for power supply stability regulation, belonging to the field of power equipment control technology. It includes historical information acquisition: obtaining historical information such as electrical operating parameters, gas insulation status parameters, and insulation margin from the past operation of the environmentally friendly gas RNB. This invention acquires historical information from the past operation of the environmentally friendly gas RNB, extracts historical degradation fragments, establishes a historical insulation degradation sample library, and then converts the real-time state of the target environmentally friendly gas RNB into a state sequence that can characterize the current insulation change trend. This sequence is then matched with the historical degradation fragments to predict the insulation margin in future periods. Compared to relying solely on real-time monitoring values ​​for threshold alarms, this invention can identify the current stage of insulation evolution from historical degradation patterns, obtain the development trend of insulation risk in advance, and improve the foresight and accuracy of insulation status assessment.

The embodiment of the application provides a security check image feature enhancement model training method and a security check image processing method. The security check image feature enhancement model comprises an image encoder, an image generation module and an image decoder. A first sample projection image and a text label of the first sample projection image are obtained. The image encoder is trained by using the first sample projection image and the text label. A second sample projection image and a picture label of the second sample projection image are obtained. The second sample projection image is input into the image encoder, and a second sample image encoding result is output. The image generation module and the image decoder are trained based on the second sample image encoding result and the picture label of the second sample projection image. The image encoder is first trained, and then the trained image encoder is used to train the image generation module and the image decoder, so as to obtain a trained security check image feature enhancement model. The model has a good optimization effect on image quality.

The application provides an optimization method for a joint reading signal of a quantum bit, and the optimization method comprises the following steps: determining a parameter initial value of a parameter in a joint reading signal to be optimized based on a quantum bit to be jointly read; generating at least one initial joint reading signal with the parameter initial value; obtaining a first reading fidelity of each quantum bit when the initial joint reading signal acts on the quantum bit to be jointly read; obtaining a loss function value corresponding to the initial joint reading signal, the loss function value being used to represent a difference between the first reading fidelity of all quantum bits and a theoretical value of the first reading fidelity; optimizing the value of the parameter based on the loss function value, and returning to the step of generating the initial joint reading signal until the loss function value converges. The technical scheme of the application can improve the optimization efficiency of the joint reading signal.

The invention discloses a big data management method and a big data management system, and relates to the field of big data management, the big data management system comprises a data management platform, the data management platform is in communication connection with an operation management module, a data acquisition module, a data preprocessing module, a data storage module, a data analysis module and a data processing module; the operation management module is used for acquiring operation process data and setting corresponding operation monitoring point positions; the data acquisition module is used for acquiring data information and running state data; the data preprocessing module is used for optimizing the data information; the data analysis module is used for setting the working state of the elastic processing node according to the running state data; the data processing module is used for analyzing and processing the obtained data information and completing all processing flows for the processed optimized data information according to the operation flow sub-sequence; according to the invention, the load planning in the data management process is improved to a certain extent.

This invention relates to the field of path optimization technology, specifically to a multi-objective logistics path optimization method and system for intelligent logistics. First, based on the relatively fixed transportation paths near forward transit stations in logistics transportation, the probability of fixed nodes, initially representing node priority, is determined by combining the frequency of node occurrence. Then, the number of substitutable nodes, representing substitutability, is determined based on the probability of fixed nodes, spatial distance, and similarity in sequence distribution. Further, combining the distribution of the number of substitutable nodes and the probability of fixed nodes in the current and historical periods, a priority index for each transportation path node is comprehensively represented. Thus, the pheromone concentration of the local path segment corresponding to each transportation path node is adjusted in real time based on the priority index to obtain a more accurate adjusted pheromone concentration, resulting in better logistics path optimization based on the adjusted pheromone concentration.

The application discloses a clock tree synthesis method based on clustering and reinforcement learning collaborative optimization, and relates to the technical field of integrated circuit design and electronic design automation. The method comprises the following steps: firstly, reading the coordinates of flip-flops, the position of clock sources and design constraint parameters in a circuit; secondly, clustering and grouping the flip-flops in the circuit through a clustering method, generating an initial hierarchical clock tree topology and determining the candidate insertion positions of buffers; thirdly, constructing a reinforcement learning environment, defining a state space, an action space and a multi-objective reward function; fourthly, within displacement constraints, learning a flip-flop relocation strategy and a buffer insertion and removal strategy by maintaining independent moving Q tables and action Q tables; and finally, evaluating performances such as clock skew and delay, generating a synthesis performance evaluation result, and iteratively optimizing and judging through a closed-loop feedback mechanism. The application effectively controls clock skew and delay, avoids the local optimal problem of traditional methods, and improves the clock tree synthesis quality and automation efficiency.

This invention relates to the field of ridge filter design technology, and discloses a design method, system, device, and storage medium for ridge filters. The method includes acquiring first and second actual water depth dose distribution data for proton and heavy ion beams with multiple energies; performing Monte Carlo simulation on the proton and heavy ion beams to obtain a Monte Carlo beam model; constructing a geometric optimization model for the ridge filter using its geometric parameters as optimization variables, maximizing its performance index as the objective function, and using geometric parameter constraints as constraints; and solving the geometric optimization model using a particle swarm optimization algorithm to obtain the optimal geometric parameters. This invention can improve the parameter design accuracy of ridge filters, reduce the processing accuracy requirements of ridge filters, improve the flatness of the effective biological dose in the extended Bragg peak, and reduce the radiation risk from secondary neutrons.

The application discloses a cable-stayed bridge completion cable force optimization method and device and a medium, wherein the method comprises the following steps: establishing a cable-stayed bridge model; determining the upper and lower bounds of the solution space of an optimization problem, and calculating an initial data set DB; obtaining a training set according to the data set DB, and training a radial basis neural network as a surrogate model; based on the surrogate model, using a social learning particle swarm optimization algorithm as an optimizer to optimize the prediction function g(x) in the solution space; after the optimizer reaches a convergence threshold, selecting multiple individuals in the last population for real solution evaluation, and adding the evaluated solutions to the data set DB; and outputting the data set DB. By adopting the radial basis neural network assisted particle swarm optimization algorithm, the application can achieve good optimization effect within a limited number of solution evaluations, and obtain an excellent feasible solution in the cable-stayed bridge completion cable force optimization problem at a lower cost. The application can be widely applied to the fields of bridge design and intelligent optimization algorithms.