Research On Resource Optimization Method Of Digital Microfluidic Chips Experimental Planning

Digital microfluidic chips is a popular research direction in the field of microelectromechanical systems,its task is to integrate the basic operational units of biology and chemistry,such as sample preparation,reagent reaction and result detection,on a few square centimeters chip using microelectromechanical systems.in order to complete different biological or chemical analyses.The task of digital microfluidic chip experiment planning is to carry out comprehensive planning and decision for a biochemical experiment,and to carry out reasonable allocation and optimization of various resources in the experiment.In recent years,digital microfluidic chips technology has provided a favorable analysis platform for basic and applied research in chemistry,life science,biomedicine and other fields,and has carried out many far-reaching studies.However,with the continuous development of the application field of digital microfluidic chip,the requirements and difficulties of chips experimental planning are also increasing.There are problems such as long experiment completion time during time and chip resource optimization,excessive driving of local electrodes,and high idle electrode driving frequency during pin resource optimization,which seriously affect the application of digital microfluidic chips.Researching these issues can improve the utilization of various chip resources,save costs,reduce potential faults during chip use,and ensure its application in high-security fields.This requires more in-depth research on the experiment planning method of digital microfluidic chips.In view of the above problems,the current experiment planning task of digital microfluidic chip has three challenges: The first challenge is that the existing modular experimental planning methods for digital microfluidic chips result in long experiment completion times during resource optimization,and there is insufficient research on the optimization of electrode drive time resources,which cannot meet the application requirements of DMFs in related fields.Another challenge is that the existing routing-based experimental planning methods for digital microfluidic chips result in local electrodes being prone to excessive driving,leading to the problem of overdriving of local electrodes during resource optimization.The last difficulty is that the existing pin resource optimization methods for digital microfluidic chip experimental planning result in excessive driving of idle electrodes,leading to the problem of overdriving of idle electrodes during resource optimization.This thesis is a study of the resource optimization methods for digital microfluidic chips experimental planning.Through the describing of the status quo,this thesis aimed at the above challenges in the experimental planning of digital microfluidic chips,and studied the optimization tasks of various resources involved in the experimental planning tasks of digital microfluidic chips.The main research content of this thesis are as follows:First of all,this thesis addresses the issues present in existing resource optimization methods for chip modular experimental planning and proposes a resource optimization method for digital microfluidic chip modular experimental planning based on an improved whale algorithm.In this thesis,the mathematical model of chip modular experimental planning is established,and the improved whale algorithm is proposed to optimize the completion time of biochemical experiments.In the process of chip experimental planning,the driving time of all electrodes on the chip is balanced allocated.The proposed method is compared with other advanced methods at present,and the results show that it can optimize the time resources and chip resources,as well as the driving time of electrodes during resource optimization,thereby reducing the experimental completion time and driving time of electrodes.Secondly,for the existing experimental planning method based on routing,this thesis proposes a resource optimization method for digital microfluidic chip routing experimental planning based on cost function construction and Bayesian decision theory.A cost function for droplet movement was constructed,and it was combined with Bayesian decision theory to plan the droplet movement path.The moving path of the droplet is optimized,and the driving times of the local electrodes of the chip are reduced,so as to complete the average distribution of the driving times of all electrodes in the whole chip.The proposed method was compared with other current advanced methods.The results show that the proposed algorithm can effectively time and chip resources,as well as the driving time of electrodes during resource optimization,reduce the experimental completion time and the driving time of the local electrode.Finally,this thesis addresses the issues present in existing pin resource optimization methods for digital microfluidic chip experimental planning and proposes a pin resource allocation method for digital microfluidic chip based on clique and support vector machine.In the pin allocation process of digital microfluidic chip,the constraint of electrode drive times is fully considered,the compatible graph is constructed by clique algorithm,the support vector machine model was used to classify the results of pin allocation,rational allocation is made during pin allocation,appropriate electrode matching objects are carefully selected,achieve a more balanced drive of all electrodes and reducing the excessive use of idle electrodes.The proposed method is compared with other current advanced methods.The results show that the method proposed in this thesis effectively reduces the drive times of the idle electrode,and completes the task of pin resource optimization of the experimental planning of the digital microfluidic chip.In the above research,this thesis deeply explores and studies the problems existing in the experimental planning process of digital microfluidic chips,and provides practical and effective solutions to the key problems in the modularization and routing experimental planning of chips and the task of pin resource allocation of chips.The improved whale algorithm can optimize the completion time and occupied resources of the biochemical detection process,increase the constraint conditions of the electrode driving time,effectively reduce the driving time of the local electrode,improve the safety of the chip,and achieve the goal of chip optimization based on modular experimental planning.A cost function was constructed for resource optimization in chip path planning,and it was combined with Bayesian decision theory to optimize the movement path of the droplet,reducing the drive times of the local electrode of the chip and solving the problem of excessive electrode driving caused by high electrode drive times in the existing experimental planning method of routing.This ensures the accuracy of the experimental results and completes the task of resource optimization for path planning of digital microfluidic chips.The pin resource optimization method of digital microfluidic chip based on clique and support vector machine is adopted,and in the process of pin resource optimization,a classification model based on Clique algorithm and support vector machine is constructed to optimize the pin resources in experimental planning reasonably.This reduces the drive times of idle electrodes and reduces the excessive use of electrodes,completing the task of pin resource optimization for digital microfluidic chip experimental planning.