607results about How to "Improve search efficiency" patented technology

The invention relates to a traffic flow prediction optimization method based on an intelligent optimization algorithm, which is applied to the technical field of traffic flow prediction. The system comprises a multi-modal data acquisition and fusion module, a hybrid deep learning prediction module, an intelligent parameter optimization module, an integrated learning module, a multi-scale error correction module, a prediction result analysis and visualization module, a distributed learning and privacy protection module and the like. Wherein the hybrid deep learning prediction module adopts an attention mechanism to fuse models such as CNN, Transform and LSTM, and introduces an adaptive error correction mechanism, so that the prediction precision is remarkably improved; the intelligent parameter optimization module accelerates the model search efficiency by using technologies such as quantum computing and swarm intelligence optimization; the integrated learning module and the adversarial training strategy enhance the robustness and generalization of the model; and the multi-scale error correction module adopts a time sequence decomposition and self-adaptive online learning method to improve the accuracy of long-term and short-term prediction.

The invention discloses an integrated energy system short-term multivariate load prediction method fusing a deep learning combination model, and the method comprises the steps: obtaining the multivariate load historical data and meteorological data of an integrated energy system, and obtaining a correlation factor which is greatly affected by the multivariate load; dividing the multi-element load historical data into two parts, namely a training set and a test set, and performing maximum and minimum normalization processing; extracting multi-level spatial features of the multi-element load historical data by using a series-parallel superposed multi-scale convolutional neural network; load historical data time sequence features are extracted by using a bidirectional gating cycle unit neural network, and a multi-head attention mechanism is introduced to extract important information of a load prediction task, so that a multi-scale convolution bidirectional gating cycle unit neural network fused with the attention mechanism is designed; and a self-adaptive multi-strategy grey wolf optimization algorithm is utilized to realize optimization parameter search of the bidirectional gating circulation unit neural network, an evaluation index is adopted to carry out error analysis on a prediction result, an optimal parameter is obtained, and accurate load prediction is realized.

The invention discloses a method for automatically generating a dynamic language test case based on a large language model, which comprises the following steps of: firstly, in a preprocessing stage, arranging related information of a to-be-tested program into prompts, analyzing a parameter type of the to-be-tested program by the large language model according to the prompts, and generating code information required by a test through an abstract syntax tree; in the test case generation stage, a dynamic multi-objective optimization algorithm is adopted, test cases are continuously crossed and mutated through branch coverage feedback, in the partial evolution process, test data which cannot be generated by conventional variation are generated by utilizing a large language model, and finally, the screened excellent test cases are organized into a complete test suite. In a program repair phase, the test suite is run to detect potential errors, the errors are analyzed and iteratively repaired using a large language model, and an executable and error-free test suite is generated. According to the method, the test coverage rate of the dynamic language is remarkably improved, and the achievement of a coverage target is accelerated.

The invention belongs to the technical field of front-end development, and particularly relates to a React framework-based tree component optimization method fusing virtualization rendering and increment rendering. Comprising the steps of S1, data preprocessing; S2, dynamic virtualization rendering: dynamically calculating a node index range in a visible area according to a real-time scrolling position of a user viewport, only rendering nodes in the range, and realizing seamless lazy loading of data through scrolling event monitoring; s3, intelligent incremental updating: identifying changed nodes through a state change comparison algorithm, and executing local re-rendering only for the changed nodes based on a component life cycle control mechanism of a React framework; and S4, lazy loading cooperative control. The method aims at solving the problems in the aspects of performance, memory management, user experience and the like when a tree component in a React ecological system processes large-scale data, and is widely applied to various Web application program development scenes needing to display and operate a large-scale tree data structure.

The invention discloses a homogeneous real estate space intelligent search method and system based on space partition, and relates to the technical field of space data processing and intelligent search. According to the homogeneous real estate space intelligent search method and system based on space partition, the data of the target real estate area is obtained and preprocessed, so that the accuracy and integrity of the data are ensured; dividing the pre-processed area into a plurality of space subareas through a geographic information system, extracting homogeneous features through a clustering algorithm, and analyzing a peripheral matching adaptation degree index of each subarea in combination with geographic position preference of a user to form a homogeneous house resource set; and searching and matching the homogeneous house resource set through a collaborative filtering algorithm, generating a user recommended house resource list, calculating a matching degree index of house resources in each partition, setting a dynamic feedback mechanism, and sorting the recommended house resource list in real time, so as to improve the user satisfaction. Personalized housing resource recommendation is provided for the user, and the convenience and accuracy of real estate transaction are improved.

The invention provides an unmanned aerial vehicle three-dimensional path planning method based on an improved sparrow algorithm, and the method comprises the steps: carrying out the initialization of a population through fusing Tent chaotic mapping and Sine chaotic mapping, thereby improving the diversity of the population, and improving the search range of the population; secondly, introducing a butterfly optimization algorithm, updating the position of a discoverer in a sparrow search algorithm, and enhancing the search capability of the algorithm for a globally optimal solution; then, position updating is carried out on the participant by using adaptive T distribution, the adaptability and flexibility of the algorithm are increased, K-means clustering is introduced to select the alert, and the path planning capability of the unmanned aerial vehicle in a complex environment is enhanced; finally, in order to prevent the algorithm from falling into local optimum, optimal individual Gaussian disturbance variation is introduced, and the algorithm is prevented from falling into local optimum in the search process. A simulation experiment result shows that compared with a traditional sparrow algorithm, the improved sparrow algorithm can find a safe and feasible path, and has relatively stable optimization capacity.

The invention provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy is improved.

The invention provides an evolutionary neural architecture searching method and system based on similarity agent assistance, and the method comprises the steps: 1, initializing an architecture population, and selecting an architecture with the best performance as an initial reference architecture; 2, constructing a graph neural network variant as a feature extractor; 3, constructing an agent model, and training the agent model through a joint loss function; 4, the high-potential architecture is reserved according to the fitness value, authenticity performance evaluation is carried out on the high-potential architecture, and an evaluation result is added into the training set; combining the current population with the high-performance architecture predicted and screened by the proxy model, and updating the population through an environment selection strategy; and step 5, repeating the step 3 and the step 4 until the population performance converges, and finally outputting a global optimal architecture. According to the method, the high-performance neural network architecture can be quickly searched under limited computing resources, and an efficient and intelligent novel solution is provided for automatic design and optimization of the neural network.

The invention discloses an industrial internet equipment fault prediction method and system based on digital twinning, and belongs to the field of industrial internet. Operation data and data volume processing data of scene equipment related to industrial internet-of-things abnormal behaviors are acquired for equipment fault primary prediction; and finally, performing final prediction on the fault equipment based on the operation data of the primary prediction fault equipment and the equipment fault primary prediction, and finally displaying fault equipment information obtained by final prediction on the digital twinborn model, and reminding maintenance personnel to maintain the equipment. According to the method, the abnormal behavior before the fault occurs is accurately judged based on the digital twinborn model, and the equipment causing the abnormal behavior is searched through the parameter correlation degree and the time-space correlation between the equipment and the behavior in the abnormal behavior analysis process, so that the searching efficiency of the fault equipment is improved, and the fault diagnosis accuracy is improved. And the security of the industrial internet scene is further improved.

The invention discloses a ground unmanned equipment cluster path planning and formation control method. The method comprises the following steps: step 1, constructing a grid map: constructing the grid map of an operation area of a ground unmanned equipment cluster according to map information; 2, completing path planning by adopting a bidirectional A * algorithm: simultaneously searching from a starting point and an ending point, estimating the cost from a current node to a target node through an evaluation function, and completing path planning; 3, a dynamic step length adjustment mechanism: dynamically adjusting the search step length according to the environmental complexity and the state of the unmanned equipment; 4, performing formation control based on the main path: performing formation control by adopting a Leader-Follower mode; and 5, generating a cluster path: generating a final action path by using the path planning and the formation control. According to the invention, the efficiency and adaptability of the multi-ground unmanned equipment system are improved, and the stability and safety of formation control are improved.

The invention relates to the technical field of retail industry search, and particularly discloses a retail industry search system and method based on a large language model, which are characterized in that after a target commodity query statement input by a user is acquired, a natural language processing technology based on deep learning is adopted to carry out retrieval intention extension on the target commodity query statement; according to the method, more abundant query content description is generated, after the user confirms that the query content description is correct, context semantic feature extraction and fine-grained semantic query interaction are carried out on the query content description and the description information of the alternative commodities, and therefore whether the alternative commodities meet the retrieval requirement of the user or not is intelligently recognized. Through the mode, the query intention of the user can be more accurately understood, and the commodity information highly related to the query intention can be provided, so that the shopping experience and the search efficiency of the user are remarkably improved.

The invention discloses a PID (Proportion Integration Differentiation) parameter optimization method based on an improved graffiti search algorithm, and relates to the technical field of PID. The invention aims to solve the problems of insufficient local search capability, slow convergence speed and poor system stability in the prior art, thereby realizing a better PID parameter optimization effect. Specifically, the PID parameter optimization method based on the improved graffiti search algorithm comprises the following steps: constructing a PID controller model; a step of replacing a fixed perception probability and a step length in the model with an adaptive perception probability and a Levy flight step length; a step of adding a crow misleading prevention factor into the model; and acquiring preset parameters in the PID controller model based on a particle swarm algorithm. The preset parameters comprise proportional (Kp), integral (Ki) and differential (Kd) parameters. Parameters of the Levy flight step length are dynamically adjusted through a Bayesian optimization algorithm. The method is suitable for being applied to PID parameter optimization work.

The invention provides an unmanned aerial vehicle path planning method based on a river horse state optimization particle swarm algorithm, and the method comprises the steps: obtaining the coordinates of a static obstacle, and carrying out the environment modeling through a grid method; initializing a particle swarm, and establishing a global path optimization function by taking the shortest driving path and no collision with an obstacle as a target; and optimizing the particle swarm optimization algorithm through a Heman state mechanism to obtain an improved particle swarm optimization algorithm, and solving the global path optimization function through the improved particle swarm optimization algorithm to obtain a global planning path. According to the unmanned aerial vehicle path planning method based on the river horse state optimization particle swarm algorithm, a traditional particle swarm algorithm is optimized by combining a river horse state mechanism, adaptive adjustment of algorithm parameters is achieved, the convergence speed and global search capability of path planning are improved, and the precision and efficiency of path planning are improved.

The invention discloses a conference task processing method and device, and belongs to the technical field of electronic equipment. The method comprises the steps of determining at least one piece of first conference task information of a target conference according to a first conference record of the target conference; generating a first task atlas of the target conference according to the at least one piece of first conference task information; wherein the first task atlas is used for performing graph structured representation on at least one piece of first conference task information in the form of task nodes and edges, and one piece of first conference task information at least comprises at least one of the following items: task content, task processor information and task deadline information.

The invention provides a base station maintenance path planning method and device and a readable storage medium, and relates to the technical field of communication, and the method comprises the steps: obtaining a to-be-maintained base station; calculating position information of the to-be-overhauled base stations through a fence map, analyzing the position information of the to-be-overhauled base stations and the environment information by using a clustering algorithm, and classifying the to-be-overhauled base stations; inputting the classification result into a trained first neural network model to obtain an optimal path of the base station to be overhauled; and taking the optimal path as a part of an initial population of a genetic algorithm in a pre-constructed genetic ant colony hybrid algorithm, and further performing path optimization by using the genetic ant colony hybrid algorithm to obtain a final optimal path of the to-be-overhauled base station. According to the method, the device and the readable storage medium, the problems of low inspection efficiency and long path planning time due to the fact that an existing base station maintenance path planning method mainly depends on manual implementation can be solved.

The invention discloses an intelligent hydraulic engineering flood discharge anti-seepage dam safety assessment method and system. The method comprises the steps of data collection, data preprocessing, dam safety assessment model construction, dam safety assessment model parameter search and dam safety real-time assessment. The invention belongs to the technical field of safety evaluation, and the scheme comprehensively considers the mean value, the standard deviation and the similarity of weight distribution, designs a screening threshold and combines dam monitoring subsets, trains a weak evaluator, adjusts the weak evaluator based on an error rate and calculates a weight, introduces category weighting and historical performance to update data weight, and improves the safety of dam monitoring. Combining the weak evaluators based on the weights to obtain a dam safety evaluation model; a first control factor is designed based on the dynamic position correlation coefficient, a second control factor is designed based on the search stage, the individual position is updated according to the random number and the control factors, the optimal parameter is determined, effective integration and analysis of dam monitoring data are achieved, and the accuracy and applicability of dam safety assessment are improved.

The invention discloses a heterogeneous computing power resource configuration optimization method for deep reinforcement learning model training, and the method comprises the steps: constructing a heterogeneous graph for describing the features of a module and a data flow relation for a core module of a model-based Muzero series algorithm, mapping the features of nodes and edges to a same hidden space through a node-edge encoder based on multi-head attention, and carrying out the optimization of the heterogeneous computing power resource configuration. And predicting algorithm training time under different configurations by using the summarized global features. And performing configuration optimization according to the prediction training time output by the model by using a simulated annealing algorithm. And allocating CPU resources, GPU resources and memory resources according to the optimized configuration, and carrying out a training task. And calculating a resource equalization factor according to a training result, evaluating a resource configuration scheme, and continuously monitoring and optimizing. According to the method, the optimal balance between the performance and the resource consumption in the model training process is realized, the efficiency and the effect of model training are remarkably improved, and the technical difficulty of resource configuration optimization in a complex scene is reduced.

The invention discloses a metallographic specimen grinding and polishing PID control method based on improved multi-material compatibility, and belongs to the technical field of PID control, and the method specifically comprises the steps: S10, building a multi-material compatible metallographic specimen grinding and polishing control model based on a PID controller; s20, improving an escape optimization algorithm, including initializing a population based on a satellite surge piloting mechanism, introducing an adaptive flexible factor, dynamically adjusting a panic index, updating a population position by using an elite gravity guiding method, and improving diversity and stability of elite pool updating by using a trace heuristic optimization method; s30, performing iteration by using an improved escape optimization algorithm to obtain an optimal PID controller parameter; s40, simulating a PID (Proportion Integration Differentiation) controller of the multi-material compatible metallographic specimen grinding and polishing control model; the improved algorithm not only improves the optimization efficiency, the response speed and the PID control precision, but also significantly enhances the robustness of the system and the adaptive capacity under different material conditions.

The invention provides a grey wolf optimization feature selection method, device and equipment based on an adaptive mechanism, and relates to the technical field of data processing. The method comprises the following steps: acquiring a data set to be subjected to feature selection, and initializing a grey wolf optimization algorithm; the fitness value of each grey wolf is calculated with the purpose of obtaining the current optimal solution, and three current optimal wolves are obtained; then taking the current optimal three wolves as search guidance, combining a nonlinear parameter control strategy, adopting an improved grey wolf step length updating calculation mode, adaptively adjusting variation intensity in the search process, updating and calculating related parameters, positions and fitness values of each grey wolf, and continuously converging the distance between the prey and the grey wolf in the search process; and calculating the score of each grey wolf, and selecting the solution candidate with the highest score as the optimal solution in the current population until the maximum number of iterations is reached, thereby obtaining a global optimal solution. The method is used for processing feature selection in high-dimensional and complex classification, and is high in convergence speed and high in accuracy.

The invention relates to a surveying and mapping unmanned aerial vehicle route planning method based on an intelligent algorithm, and aims to improve the route planning efficiency and task execution capability of an unmanned aerial vehicle in a complex environment. The method comprises the following steps: firstly, constructing a task environment model, performing two-dimensional rasterization on an outdoor low-altitude environment in a mountainous area through a grid method, and taking bird flock density as one of main constraint conditions into the model; and then, an improved depth-first search (DFS) algorithm is used for flight path planning. According to the algorithm, a bwlabel function and a FOrientitOK5 function are introduced, the search direction is optimized, the unmanned aerial vehicle is prevented from falling into a search dead zone, repeated traversal is reduced, and therefore the search efficiency is remarkably improved. In addition, by simplifying the parameter adjustment process, the universality and stability of the algorithm are improved, and the method is suitable for unmanned aerial vehicle surveying and mapping tasks in various complex environments. Through the improvement, effective technical support is provided for intelligent navigation in a complex environment, and the task completion efficiency of the unmanned aerial vehicle is remarkably improved.

The invention discloses a disaster relief unmanned aerial vehicle path planning method based on an APF-IRRT fusion algorithm, and the method comprises the following steps: initializing parameters, expanding nodes, optimizing and smoothing a path, carrying out the smoothing of the path through cubic spline curve fitting or polynomial interpolation, and finally, carrying out the planning of a disaster relief unmanned aerial vehicle path. And outputting the optimized and smoothed flight path of the unmanned aerial vehicle by setting an algorithm termination condition. According to the disaster relief unmanned aerial vehicle path planning method based on the APF-IRRT fusion algorithm, the attraction force and the repulsive force are combined with adaptive step length and sampling angle optimization, the high efficiency of path planning and the obstacle avoidance safety are realized, and meanwhile, the smoothness and the performability of the path are improved through path smoothing processing; the method is suitable for efficient path planning of unmanned aerial vehicles in complex disaster relief environments.

The invention discloses a shield tunnel settlement prediction method based on artificial intelligence. The method comprises the steps of data acquisition, data optimization, construction of a shield tunnel settlement prediction model, model optimization and settlement prediction. The invention relates to the technical field of tunnel construction monitoring, in particular to a shield tunnel settlement prediction method based on artificial intelligence. According to the scheme, data cleaning is carried out by defining a dimension refining distance function and calculating gravitation between data, potential relations between the data are deeply mined, noise interference is reduced, and the accuracy and reliability of the data are improved; by designing a mapping function, a circulation control layer, a depth correlation layer and a loss function, the recognition and prediction precision of the model on a complex settlement mode is improved; model parameters are dynamically searched by designing an intervention factor, defining an adaptive step length, performing bidirectional iterative search and gradually adjusting search, so that the search efficiency is improved, time consumption and a loss value of model training are balanced, and the model is more efficient and stable.

The invention provides a college campus planning and design multi-objective optimization method based on cGAN and NSGA-II, and the method specifically comprises the steps: obtaining a large number of existing different campus planning and design related data, and carrying out the correlation of the data with corresponding carbon emission and incremental cost data, and forming a model data set; inputting the model data set into a pre-constructed cGAN model for training, and optimizing an initial campus planning and design scheme output by the cGAN model through an NSGA-II algorithm; and performing optimization adjustment and scheme visual display on the cGAN model based on the target campus planning design. The college campus carbon emission level and incremental cost collaborative optimization model constructed by the invention provides scientific decision support for an architect to fully consider the balance between the carbon emission reduction performance and the economic benefit in a scheme stage, and is helpful to further realize a campus carbon neutralization construction plan.

The invention discloses a branch coverage rate calculation method and device based on BOOL logic, and relates to the technical field of software testing. The method is suitable for the test case taking the BOOL logic expression as basic test data, the branch path diagram is generated according to the BOOL logic expression, the coverage rate condition of the test case set on the BOOL logic branches is calculated according to the covered path condition in the case execution process, covered and uncovered BOOL branch reports are output according to the test execution condition, and the test efficiency is improved. The test case executed by the black box is transparent, and meanwhile, a test case set can be completed according to uncovered branch guidance in an output report.

The invention relates to a charging robot path planning method. The method comprises the following steps: calculating an initial path from an initial grid to a target grid of a charging robot in a grid map by using an A * algorithm; dividing a search space determined by the initial path into sub search spaces; initializing in each sub-search space and expanding a population through a pseudo-reverse learning strategy; the obtained current population is randomly divided into two sub-populations, and the two sub-populations are respectively marked as a first sub-population and a second sub-population; for the first sub-population, iterative optimization is carried out through a first strategy according to the fitness, and for the second sub-population, iterative optimization is carried out through a second strategy according to the fitness; the first 50% of individuals in the first sub-population and the second sub-population form a third population; performing crossover operation on the No.3 population to obtain a current population; calculating the fitness of the current population individuals; and iteration is carried out until the number of iterations reaches the maximum number of iterations, or the optimal values of the two sub-populations tend to be the same, and an optimal path is obtained.

The invention relates to the technical field of temperature control, in particular to an aging chamber temperature control method and system, and the method comprises the steps: obtaining a temperature sequence in an aging chamber, and dividing the temperature sequence into a plurality of equal-length data segments; calculating a clutter factor of any data segment, and taking a normalized value of a difference between the clutter factor of the data segment and an average clutter factor of a plurality of data segments before the data segment as an oscillation index of the data segment; and calculating a product of the temperature optimization index and a preset temperature to obtain a target temperature of the data segment, taking the target temperature as an initial temperature, and obtaining an optimal control temperature of the data segment by using a simulated annealing algorithm based on the initial temperature so as to control the temperature of the aging chamber according to the optimal control temperature. According to the invention, the accuracy and efficiency of aging chamber temperature control can be improved.

The invention discloses a foot tub parameter intelligent adjustment method and system based on machine learning. The method comprises the steps of data collection, data screening, foot tub parameter prediction model establishment, global parameter optimization and foot tub parameter adjustment. The invention relates to the technical field of smart homes, in particular to a foot tub parameter intelligent adjusting method and system based on machine learning, according to the scheme, data screening is carried out by dividing data neighborhoods and defining data complexity and local density, and the accuracy and reliability of abnormal value recognition are improved; a model is established by designing an excitation function, a front double transformation layer, a residual adjustment layer and a loss function, so that the expression ability, prediction accuracy and prediction error control ability of the model are improved; parameters are optimized by designing a parameter search function and a search rule, and the search direction and step length are dynamically adjusted, so that the search process is more efficient, the anti-interference capability of the search process is enhanced, and local optimum can be jumped out.

A causal meta-knowledge discovery method based on a knowledge graph comprises the steps that a first knowledge graph normal form KGS is determined, and the first KGS comprises a head entity, a tail entity and a target relation between the head entity and the tail entity; the target relationship is a set relationship between the head entity and the tail entity; a second KGS is determined, the second KGS is a relation path in the knowledge graph, and the relation path comprises a plurality of entities; encoding the first KGS as a first variable, and encoding the second KGS as a second variable; encoding a data object composed of any two entities in the knowledge graph into a sample; sampling each sample in the relation path, and determining a value of a second variable according to a sampling result; the sampling result comprises sampling success and sampling failure; sampling each sample in the real data set, and determining a value of a first variable according to a sampling result; and obtaining the causal rule of the knowledge graph according to the samples and the values of the corresponding first and second variables.

The invention discloses a reel-to-reel multilayer printing control method based on improved simulated annealing optimization LQR, and belongs to the field of multilayer printing flexible electronics. According to the reel-to-reel multilayer printing flexible electronic control method based on the improved simulated annealing optimization LQR, initial LQR controller design is carried out on an overprinting system according to a reel-to-reel overprinting system dynamic model, and a simulated annealing algorithm is selected to carry out LQR controller optimization; and self-adaptive annealing, dynamic step length, mixed neighborhood search and other methods are used to make up for the defects of slow convergence speed, difficult temperature control and the like of the traditional simulated annealing algorithm, so as to obtain the optimal control quantity and improve the overprinting precision and control stability of the system. The control quantity optimization of the overprinting system is realized, the control stability of the overprinting system is ensured, and the overprinting precision is improved.

The invention discloses a micro-grid optimization scheduling method based on a distributed bee foraging algorithm. The method comprises the following steps: establishing a micro-grid optimization scheduling objective function; determining micro-grid optimization scheduling related constraint conditions; performing initialization setting on parameters of the distributed bee foraging algorithm; according to the optimization scheduling related constraint conditions, randomly generating an optimization scheduling scheme; evaluating and sorting nectar sources searched by the investigation bees; carrying out local search by the bees and updating the selected nectar source; carrying out local search by worker bees and updating the selected nectar source; the reconnaissance bees generate optional optimal scheduling schemes in a random search mode; the nectar sources searched by the investigation bees and the selected nectar sources are sorted together, and the selected nectar sources used for next iteration are screened out; and outputting a globally optimal solution and a fitness function value of the target function. According to the method, the bee foraging algorithm is improved to adapt to the optimization requirements of the distributed micro-grid, and the efficient solution of the distributed micro-grid optimization scheduling scheme is realized.