CN111558404B - Microfluidic chip droplet path planning method, device, equipment and storage medium - Google Patents
Microfluidic chip droplet path planning method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a microfluidic chip liquid drop path planning method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring path planning information of the liquid drop; determining respective target moving paths of the liquid drops through an evolution algorithm and a path search algorithm according to the path planning information; and if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths as legal paths. According to the embodiment of the invention, the moving path of the liquid drop is determined through the path searching algorithm and the evolution algorithm, and the corresponding liquid drop moving path can be obtained only by inputting the path planning information of the liquid drop when a user uses the liquid drop path planning method, so that the efficiency of liquid drop path planning of the digital microfluidic chip is improved, and the time spent on liquid drop path planning is reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of microfluidics, in particular to a microfluidic chip liquid drop path planning method, a device, equipment and a storage medium.
Background
Digital Microfluidic chips (DMFB) are an emerging Microfluidic technology, also known as second generation Microfluidic technology, that enables biochemical detection and analysis reactions to be performed directly on the chip in the form of discrete droplets.
In order to implement various biochemical detection and analysis tasks on the digital microfluidic chip, a complete execution process of biochemical reactions needs to be acquired, module binding and scheduling are performed, then the position of each module on the chip is planned, and finally the movement path of each droplet is planned and accidental mixing of the droplets is avoided (namely droplet path planning), and the series of complex steps are called as advanced synthesis of the digital microfluidic chip. Among them, the problem of droplet path planning is the last step of advanced integration of digital microfluidic chips, which plans the moving path of each droplet in a time-division multiplexing manner. Due to the complexity of droplet path planning and the large impact on the correctness of the analysis results, droplet path planning becomes one of the most critical challenges in digital microfluidic chip design.
At present, when the liquid drop path is planned, an algorithm or a tool with high universality is not available for researchers, so that the researchers need to design the liquid drop path planning algorithm by themselves, and the method causes high time cost and low working efficiency for the researchers to plan the liquid drop path.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a device, and a storage medium for planning a droplet path of a microfluidic chip, so as to improve the efficiency of the droplet path planning and reduce the time taken for the droplet path planning.
In a first aspect, an embodiment of the present invention provides a microfluidic chip droplet path planning method, including:
acquiring path planning information of the liquid drop;
determining respective target moving paths of the liquid drops through an evolution algorithm and a path search algorithm according to the path planning information;
and if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths as legal paths.
Further, the path planning information includes a chip size, an obstacle area, and a number, a start point, and an end point of each droplet.
Further, the determining the respective target movement paths of the droplets through an evolution algorithm and a path search algorithm according to the path planning information includes:
constructing a chip model according to the chip size and the barrier area;
in the chip model, the target moving path of each liquid drop from the starting point to the end point is determined through an evolution algorithm and a path searching algorithm.
Further, the determining the target moving path of each droplet from the starting point to the end point through the evolution algorithm and the path search algorithm comprises:
initializing a population, the population comprising a plurality of randomly generated individuals, the individuals being in a droplet movement sequence;
determining a composite movement path for each individual based on the modified dijkstra algorithm, wherein the composite movement path for an individual comprises a movement path for each droplet in the individual from a starting point to an ending point;
determining the fitness of each individual based on the comprehensive moving path of each individual;
when the iteration times of the population do not exceed the preset times, crossing and varying the individuals in the population, and returning to the step of determining the comprehensive moving path of each individual based on the improved Dijkstra algorithm;
and when the iteration times of the population exceed the preset times, determining the target moving path of each liquid drop according to the fitness of each individual.
Further, the determining a composite movement path for each individual based on the modified dijkstra algorithm comprises:
based on an improved Dijkstra algorithm, sequentially acquiring a non-time sequence moving path of each liquid drop in each individual according to the moving sequence of the liquid drop in each individual;
timing constraints are added to all non-timing movement paths for each individual to determine a composite movement path for each individual.
Further, the determining the target moving path of the droplet according to the fitness of each individual comprises:
determining an optimization type of the droplet path plan;
if the optimization type is single-target optimization, taking the comprehensive moving path corresponding to the individual with the minimum fitness as a target moving path;
and if the optimization type is multi-objective optimization, determining at least one optimal fitness through a multi-objective optimization algorithm, and taking the comprehensive moving path corresponding to the individual with the at least one optimal fitness as a target moving path.
Further, if any two droplets meet the droplet constraint rule when moving according to the respective target movement paths, after determining that all the target movement paths are legal paths, the method further includes:
and displaying the dynamic moving process of the liquid drops according to the respective target moving paths through a visual interface.
In a second aspect, an embodiment of the present invention provides a microfluidic chip droplet path planning apparatus, including:
the path planning information acquisition module is used for acquiring path planning information of the liquid drops;
the target moving path determining module is used for determining respective target moving paths of the liquid drops according to the path planning information;
and the legal path determining module is used for determining all the target moving paths as legal paths if any two liquid drops accord with the liquid drop constraint rules when moving according to the respective target moving paths.
In a third aspect, an embodiment of the present invention provides a computer device, where the computer device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for microfluidic chip droplet path planning provided by any of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for planning a droplet path of a microfluidic chip according to any embodiment of the present invention.
The method for planning the liquid drop path of the microfluidic chip determines the moving path of the liquid drop through the path search algorithm and the evolution algorithm, and a user can obtain the corresponding liquid drop moving path only by inputting the path planning information of the liquid drop when using the method, so that the efficiency of planning the liquid drop path of the digital microfluidic chip is improved, and the time spent on planning the liquid drop path is reduced.
Drawings
Fig. 1 is a schematic flow chart of a microfluidic chip droplet path planning method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a microfluidic chip droplet path planning method according to a second embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for determining a moving path of a droplet object according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a microfluidic chip droplet path planning apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "plurality", "batch" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a schematic flow chart of a microfluidic chip droplet path planning method according to an embodiment of the present invention, and this embodiment is applicable to droplet path planning of a digital microfluidic chip, and the method may be executed by a microfluidic chip droplet path planning apparatus. As shown in fig. 1, a method for planning a droplet path of a microfluidic chip according to an embodiment of the present invention includes:
and S110, acquiring path planning information of the liquid drop.
In particular, the path planning information for a drop, also commonly referred to as a drop path planning problem, includes the chip size, the obstacle area, and the number, start point, and end point of each drop. Path planning information for the droplets is obtained by user input.
The chip size refers to the width and height of the digital microfluidic chip, and the digital microfluidic chip can be regarded as a two-dimensional network structure, including a plurality of grids arranged in an array form, and the size of each grid is the same, so the chip size is usually expressed by the matrix size of the grids, for example, if the chip size is 12 × 12, the grids in the digital microfluidic chip are arranged by the matrix size of 12 × 12, and there are 144 grids. Alternatively, the chip size may be expressed in a coordinate form, where the row of the chip is an X axis and the column is a Y axis, and then the coordinates of two opposite vertex angles of the chip may be expressed as the chip size, for example, the coordinates of the lower left corner of the chip are set to (1,1), and the coordinates of the upper right corner of the chip are set to (12,12), which also means that the grid in the chip is arranged in a matrix size of 12 × 12.
The barrier region represents a region which cannot be entered by the droplet, and is usually represented by coordinates of two opposite top corners of the barrier region, for example, the coordinates of the top left corner and the bottom right corner of the barrier region, the barrier region can be represented as (2,4) (4,3), that is, the region between the row 2 to the row 4 and the column 3 to the column 4 of the digital microfluidic chip is the barrier region which cannot be entered by the droplet.
Typically, a plurality of droplets are biochemically detected and analyzed simultaneously on a digital microfluidic chip, and thus the droplets need to be numbered, with the start and end points of each droplet being different, and usually expressed in terms of coordinates. Illustratively, there are 3 droplets, numbered 1, 2 and 3, for biochemical detection and analysis simultaneously on a digital microfluidic chip, and the corresponding start and end points can be expressed as: 1(1, 1) (8, 2), 2(1, 12) (10, 8) and 3(3, 1) (1, 6), i.e. the 1 st drop needs to be moved from grid with coordinates (1,1) to grid with coordinates (8, 2), the 2 nd drop needs to be moved from grid with coordinates (1, 12) to grid with coordinates (10, 8), the 3 rd drop needs to be moved from grid with coordinates (3, 1) to grid with coordinates (1, 6).
And S120, determining the respective target moving paths of the liquid drops through an evolution algorithm and a path search algorithm according to the path planning information.
Specifically, a chip model with a corresponding size is constructed according to the size of a chip, then a corresponding barrier area is arranged on the chip model, and then a moving path of each liquid drop moving from a starting point to an end point on the chip model is determined.
A path search algorithm is used to determine the shortest travel path for each droplet moving from the starting point to the ending point on the chip model, such as Dijkstra's algorithm (Dijkstra's algorithm). The evolution algorithm is used for determining the sequence of the movement of the droplets, and is a bionic algorithm simulating the genetic evolution rule of nature, such as a genetic algorithm. When the sequence of the liquid drops moving in sequence is changed, the moving paths of the liquid drops are also changed, so that the optimal liquid drop moving sequence is obtained through the synthesis of a path searching algorithm and an evolution algorithm, the moving paths of all the liquid drops are generated according to the moving sequence, and the moving path generated by the liquid drops according to the optimal liquid drop moving sequence is called as the target moving path of the liquid drops.
The conditions for determining the optimal droplet movement sequence are called optimization objectives, and generally include single objective optimization, which means that only one condition is considered when determining the optimal droplet movement sequence, and multi-objective optimization, which means that a plurality of conditions are considered when determining the optimal droplet movement sequence. In general, the optimization objective of droplet path planning usually includes two conditions, completion time, which is the time it takes to move from the first droplet from the starting point until the last droplet moves to the end point, and electrode consumption number, which is the sum of the grid numbers consumed by all droplets moving from the respective starting point to the end point. The optimal droplet movement sequence may be different according to different optimization objectives, and the number is one or more, and accordingly, the target movement path of each droplet also includes one or more.
Optionally, since the path planning information of the droplets is generally referred to as a droplet path planning problem, the movement paths of all droplets generated according to one droplet movement sequence are generally referred to as a solution of the droplet path planning problem, and a target solution of the droplet path planning problem is obtained according to an evolution algorithm and a path search algorithm, where the target solution includes one or more solutions.
S130, if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths to be legal paths.
Specifically, since the plurality of droplets cannot be fused when they move simultaneously (except for the case where fusion is required), it is necessary to check the validity of the target movement paths of the obtained droplets to determine whether fusion occurs when the droplets move simultaneously according to the respective target movement paths, and when any two droplets move according to the respective target movement paths, fusion does not occur, it is determined that all the target movement paths are valid paths, that is, the target solution is a valid solution, otherwise, the target solution is an invalid solution, and the target movement paths are not usable.
Whether any two droplets are fused when moving simultaneously can be judged by whether any two droplets meet a droplet constraint rule when moving simultaneously, and the droplet constraint rule generally comprises two parts: static constraints and dynamic constraints. Static constraint means that any two droplets cannot move to the same grid or two adjacent grids at the same time in the process of moving, for example, one droplet moves to grid (4, 4) at time t, if the other droplet also moves to grid (4, 4) at this time, or the other droplet moves to grid (4, 4) adjacent to grid (4, 5), if the two droplets violate the static constraint, and fusion may occur. One grid of the digital microfluidic chip is actually an electrode, a liquid drop moves towards the direction of the activated electrode in the moving process, dynamic constraint means that two activated electrodes cannot exist around one liquid drop simultaneously in the moving process of the liquid drop, when two activated electrodes exist around one liquid drop simultaneously, the liquid drop cannot determine the moving direction, and the situation that other liquid drops around are approaching to the liquid drop and fusion easily occurs is also shown. Therefore, when any two droplets move according to respective target movement paths and accord with the droplet constraint rule, the droplet movement process is not fused, and all the target movement paths are legal paths.
According to the method for planning the droplet path of the microfluidic chip provided by the embodiment of the invention, the moving path of the droplet is determined through the path search algorithm and the evolution algorithm, and a user can obtain the corresponding droplet moving path only by inputting the path planning information of the droplet when using the method, so that the efficiency of planning the droplet path of the digital microfluidic chip is improved, and the time spent on planning the droplet path is reduced.
Example two
Fig. 2 is a schematic flow chart of a microfluidic chip droplet path planning method according to a second embodiment of the present invention, which is a further refinement of the second embodiment. As shown in fig. 2, a microfluidic chip droplet path planning method provided by the second embodiment of the present invention includes:
s210, acquiring path planning information of the liquid drops, wherein the path planning information comprises the size of a chip, an obstacle area, and the number, the starting point and the end point of each liquid drop.
S220, constructing a chip model according to the chip size and the obstacle area.
Specifically, a chip model with a corresponding size is constructed according to the size of a chip, then a corresponding barrier area is set on the chip model, that is, grid electrodes with a corresponding number are connected and arranged in a matrix form according to the size of the chip, and then the grid area with a corresponding coordinate is set as the barrier area. For example, the chip size is 12 × 12, and the barrier regions are set to (2,4) (4, 3).
And S230, in the chip model, determining a target moving path of each liquid drop from the starting point to the end point through an evolution algorithm and a path search algorithm.
Specifically, the path search algorithm is to obtain the moving paths of all the drops given the moving sequence of the drops, but the more the number of the drops, the more the kinds of the moving sequence of the drops, if there are n drops, there will be n! If a path search algorithm is executed for each droplet movement sequence, the calculation amount is huge, and the calculation speed is slow, so that the path search algorithm needs to be combined with an evolution algorithm to screen the optimal droplet movement sequence, and then the respective target movement paths of the droplets in the optimal droplet movement sequence are determined through the path search algorithm.
S240, if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths to be legal paths.
And S250, displaying the dynamic moving process of the liquid drops according to the respective target moving paths through a visual interface.
Specifically, the dynamic moving process of the liquid drops according to the respective target moving paths, that is, the verification process of the target moving paths is displayed through the visual interface, so that the dynamic moving process of the liquid drops according to the respective target moving paths can be displayed to the user visually.
The visualization interface includes a control area, a model area, and a log area, where chip information including chip size and the number of obstacle areas and path statistics including the number of droplets, total completion time of droplet movement (i.e., the time it takes for the first droplet to move from the start point until the last droplet moves to the end point), and the number of electrode consumptions are displayed. The model area is used for displaying the built chip model and the dynamic moving process of the liquid drop on the chip model according to the target moving path, different liquid drops can be represented by different colors, and when the condition of violating the constraint occurs, the model area can highlight the area where the condition of violating the constraint occurs. The log area is used for displaying log information in the dynamic movement process of the liquid drop, and preferably, the log information displayed in the log area is information when the target movement path is illegal (namely, violates the liquid drop constraint rule), such as the liquid drop number, the coordinates when the liquid drop violates the constraint, the type of violation, and the like.
Furthermore, the visual interface is also provided with a plurality of control buttons, and a user can control the dynamic moving process of the liquid drops by clicking different control buttons. For example, when the user clicks the automatic play button, the droplets automatically move according to the respective target movement paths, and when the user clicks the manual play button, the user may control the movement of the droplets through the "previous step" and "next step" buttons.
According to the method for planning the droplet path of the microfluidic chip provided by the embodiment of the invention, the moving path of the droplet is determined through the path search algorithm and the evolution algorithm, and the corresponding droplet moving path can be obtained only by inputting the path planning information of the droplet when a user uses the method, so that the efficiency of planning the droplet path of the digital microfluidic chip is improved, and the time spent on planning the droplet path is reduced. The dynamic moving process of the liquid drops is displayed through the visual interface, so that a user can clearly and visually watch the dynamic moving process of the liquid drops, the legality of a target moving path is verified, and the accuracy of liquid drop path planning is improved.
EXAMPLE III
Fig. 3 is a schematic flow chart of a method for determining a target movement path of a droplet according to a third embodiment of the present invention, which is a further refinement of the step of determining a target movement path of each droplet from a start point to an end point by using an evolution algorithm and a path search algorithm in the foregoing embodiments. As shown in fig. 3, a method for determining a movement path of a droplet object according to a third embodiment of the present invention includes:
s310, initializing a population, wherein the population comprises a plurality of randomly generated individuals, and the individuals are in a droplet moving sequence.
Specifically, Evolution Algorithm (EA) is also called evolution algorithm, and is a bionic algorithm for simulating the genetic evolution law in nature, and the genetic algorithm is one of the branches. The calculation objects of the genetic algorithm are called population, the objects contained in the population are called individuals, and the size of the population is the number of the individuals. The initial population of the genetic algorithm is called an initial population, the initial population is evolved to generate a second-generation population, the second-generation population is evolved to generate a third-generation population, the populations are sequentially evolved, the population before evolution is also called a parent population, the population after evolution is also called a child population, and the number of evolutions is called the number of iterations. The population evolution means that individuals in the population perform biological evolution operations such as selection, crossing, variation and the like, and preferably, in the embodiment, the population evolution mainly includes crossing and variation.
In this embodiment, the individual in the population is a randomly generated droplet moving sequence, which is represented by an arrangement sequence of droplet numbers, for example, there are 5 droplets in total, and the numbers are: 1. 2, 3, 4, 5, then the individuals in the population may include: 12345. 13245, 12435, 23415, 54321, etc., with numbers in the front showing a first shift and numbers in the back showing a second shift. The size of the population can be determined from the number of drops, the larger the population, but it is obvious that the population should be smaller than the sum of all the drop movement sequences, i.e. if there are n drops, the population should be smaller than n! .
The initialization population mainly comprises: and setting the size, the cross probability, the mutation probability, the iteration times and generating individuals in the population.
And S320, determining a comprehensive moving path of each individual based on the improved Dijkstra algorithm, wherein the comprehensive moving path of the individual comprises the moving path of each liquid drop in the individual from the starting point to the end point.
Specifically, Dijkstra (Dijkstra) is one of path search algorithms, which is a typical shortest path algorithm for calculating the shortest path from one node to another node, and is mainly characterized in that the shortest path is expanded outward layer by layer with a starting point as a center (i.e., breadth-first search concept) until the shortest path is expanded to an end point. The improved dijkstra algorithm means that other functions or algorithms are introduced while the dijkstra algorithm is applied, so that the calculation process is more inclined, and the calculation result is more accurate. An individual represents a specific movement sequence of the liquid drops, and the movement path of each liquid drop from the liquid drop to the terminal point in the specific movement sequence can be determined through a modified Dijkstra algorithm, so that the movement paths of all the liquid drops in the individual are called the comprehensive movement path of the individual.
Further, a method of determining a comprehensive moving path of each individual based on the modified dijkstra algorithm includes steps S321 to S322 (not shown in the drawings).
S321, sequentially acquiring a non-time-sequence moving path of each liquid drop in each individual according to the moving sequence of the liquid drop in each individual based on an improved Dijkstra algorithm.
In this embodiment, the modified dijkstra algorithm means that a cost function is introduced into the dijkstra algorithm, so that the droplets preferentially select a moving grid. The non-time-series moving path is a moving path of each droplet obtained in turn according to the moving sequence of the droplets when the time series is ignored, and this stage is also called two-dimensional path planning. For example, if the individual is 13245, that is, the droplet movement order is 13245, then based on the modified dijkstra algorithm, in the case where none of the droplets numbered 2, 3, 4 and 5 move, the movement path of the droplet numbered 1 from its starting point to its end point is acquired, then in the case where none of the droplets numbered 1, 2,4 and 5 move, the movement path of the droplet numbered 3 from its starting point to its end point is acquired, and so on, the movement path of the droplet numbered 2 from its starting point to its end point, the movement path of the droplet numbered 4 from its starting point to its end point, and the movement path of the droplet numbered 5 from its starting point to its end point are sequentially acquired.
And S322, adding timing constraints to all the non-timing movement paths of each individual to determine a comprehensive movement path of each individual.
Specifically, a time-series constraint is added to all non-time-series moving paths of each individual, that is, moving paths of all droplets in the individual are integrated under the condition of considering time series, so that the droplets can move simultaneously under the condition of considering the time-series constraint, the stage is also called three-dimensional path integration, and the moving paths of all droplets finally added with the time-series constraint are called an integrated moving path of the individual.
And S330, determining the fitness of each individual based on the comprehensive movement path of each individual.
Specifically, determining the individual fitness is determining the value of an optimization objective for determining the optimal droplet movement sequence. The optimization objectives for droplet path planning typically include two conditions, completion time, which is the time it takes to move from the first droplet from the start point until the last droplet moves to the end point, and electrode consumption number, which is the sum of the number of grids consumed by all droplets moving from the respective start point to the end point. The fitness of the individual is determined by determining the completion time and the number of electrode consumptions of the individual, for example, the completion time of the individual 13245 is 20 seconds and the number of electrode consumptions is 40.
S340, when the iteration times of the population do not exceed the preset times, crossing and mutating the individuals in the population, and returning to the step S320.
Specifically, the preset times are the maximum times of population evolution, and when the iteration times of the population do not exceed the preset times, it is indicated that the population evolution has not yet reached the requirement, and the evolution needs to be continued, so that new individuals are generated after the individuals in the population are subjected to crossing and variation operations, that is, a new population is generated, and then the step S320 is returned to determine the comprehensive moving path and fitness of the new individuals.
In this embodiment, the crossover operation is performed on the individuals in the population according to the crossover probability, and then the mutation operation is performed on the crossed individuals according to the mutation probability. Cross-over refers to the exchange of Partial genes of two individuals in some way to create two new individuals, for example, by cross-over of individuals using Partial Mapped Cross (PMX). Mutation manipulation refers to the exchange of codes or numbers at certain positions of an individual with their alleles.
And S350, when the iteration times of the population exceed the preset times, determining the target moving path of each liquid drop according to the fitness of each individual.
Specifically, when the iteration times of the population exceed the preset times, the population is shown to have undergone sufficient time evolution, and at this time, the target movement path of each droplet is determined according to the fitness of each individual.
Further, a method of determining a target moving path of the droplet according to the fitness of each individual includes steps S351 to S353 (not shown).
S351, determining the optimization type of the liquid drop path planning.
Specifically, the optimization type of the droplet path planning is determined, namely, the optimization target type is determined, namely, whether the optimization is single-target optimization or multi-target optimization is determined. The optimization objectives of droplet path planning usually include two conditions of completion time and electrode consumption amount, and single objective optimization only needs to consider one of the two conditions of completion time and electrode consumption amount, and multi-objective optimization needs to synthesize the two conditions of completion time and electrode consumption amount.
And S352, if the optimization type is single-target optimization, taking the comprehensive moving path corresponding to the individual with the minimum fitness as a target moving path.
Specifically, in the present embodiment, the single-target optimization defaults to the completion time as the optimization target, and then the integrated moving path of the individual whose completion time meets the preset requirement is the target moving path of each droplet, for example, the integrated moving path of the individual whose completion time is the smallest may be used as the target moving path of each droplet, or the integrated moving path of the individual whose completion time is less than a certain set time may be used as the target moving path of each droplet.
And S353, if the optimization type is multi-objective optimization, determining at least one optimal fitness through a multi-objective optimization algorithm, and taking the comprehensive moving path corresponding to the individual with the at least one optimal fitness as a target moving path.
Specifically, if the liquid drop is multi-objective optimization, the optimal fitness needs to be determined through a multi-objective optimization algorithm, the multi-objective optimization algorithm generally classifies individuals with different fitness, and the finally determined optimal fitness is generally all fitness in one category, so that at least one optimal fitness determined through the multi-objective optimization algorithm is available, and the comprehensive moving path corresponding to at least one individual with optimal fitness can be used as the target moving path of the liquid drop. Preferably, the multi-objective optimization Algorithm may adopt an NSGA2 Algorithm, and an NSGA2 Algorithm (Non-dominant Sort Genetic Algorithm with Elitist strand, Non-dominant Sort Genetic Algorithm with elite Strategy) is an improvement on the NSGA Algorithm (Non-dominant Sort Genetic Algorithm), which reduces the complexity of the Non-inferior Sort Genetic Algorithm and has the advantages of fast operation speed and good convergence of solution set.
The method for determining the target moving path of the liquid drop provided by the third embodiment of the invention determines the target moving path of the liquid drop through a genetic algorithm and an improved Dijkstra algorithm, improves the efficiency of liquid drop path planning of a digital microfluidic chip, and reduces the time spent on liquid drop path planning.
Example four
Fig. 4 is a schematic structural diagram of a microfluidic chip droplet path planning apparatus according to a fourth embodiment of the present invention, which is applicable to droplet path planning of a digital microfluidic chip, and the apparatus can implement the microfluidic chip droplet path planning method according to any embodiment of the present invention, and has corresponding functional structures and beneficial effects of the implementation method.
As shown in fig. 4, a microfluidic chip droplet path planning apparatus provided by the fourth embodiment of the present invention includes: a path planning information obtaining module 410, a target moving path determining module 420 and a legal path determining module 430, wherein:
the path planning information obtaining module 410 is configured to obtain path planning information of the droplet;
the target moving path determining module 420 is configured to determine a target moving path of each droplet according to the path planning information;
the legal path determining module 430 is configured to determine that all target movement paths are legal paths if any two droplets move according to their respective target movement paths and meet the droplet constraint rule.
Further, the path planning information includes a chip size, an obstacle area, and a number, a start point, and an end point of each droplet.
Further, the target moving path determining module 420 includes:
the modeling unit is used for constructing a chip model according to the chip size and the barrier area;
and the target moving path determining unit is used for determining the target moving path of each liquid drop from the starting point to the end point through an evolution algorithm and a path searching algorithm in the chip model.
Further, the target moving path determining unit includes:
a population initializing subunit, configured to initialize a population, where the population includes a plurality of randomly generated individuals, and the individuals are in a droplet moving sequence;
a comprehensive movement path determination subunit for determining a comprehensive movement path for each individual based on the modified dijkstra algorithm, wherein the comprehensive movement path for an individual includes a movement path for each droplet in the individual from a starting point to an end point;
a fitness determining subunit, configured to determine a fitness of each individual based on the comprehensive movement path of each individual;
a cross variation subunit, configured to, when the iteration number of the population does not exceed a preset number, cross and vary the individuals in the population, and then return to the step of determining the comprehensive movement path of each individual based on the improved dijkstra algorithm;
and the target moving path determining subunit is used for determining the target moving path of each liquid drop according to the fitness of each individual when the iteration times of the population exceed the preset times.
Further, the integrated moving path determining subunit is specifically configured to: based on an improved Dijkstra algorithm, sequentially acquiring a non-time sequence moving path of each liquid drop in each individual according to the moving sequence of the liquid drop in each individual; timing constraints are added to all non-timing movement paths for each individual to determine a composite movement path for each individual.
Further, the target moving path determining subunit is specifically configured to: determining an optimization type of the droplet path plan; if the optimization type is single-target optimization, taking the comprehensive moving path corresponding to the individual with the minimum fitness as a target moving path; and if the optimization type is multi-objective optimization, determining at least one optimal fitness through a multi-objective optimization algorithm, and taking the comprehensive moving path corresponding to the individual with the at least one optimal fitness as a target moving path.
Further, the method also comprises the following steps:
and the visualization module is used for displaying the dynamic moving process of the liquid drops according to the respective target moving paths through the visualization interface.
The microfluidic chip liquid drop path planning device provided by the embodiment of the invention determines the moving path of the liquid drop through the path search algorithm and the evolution algorithm, and the user can obtain the corresponding liquid drop moving path only by inputting the path planning information of the liquid drop when using the device, so that the liquid drop path planning efficiency of the digital microfluidic chip is improved, and the time spent on liquid drop path planning is reduced.
EXAMPLE five
Fig. 5 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary computer device 512 (hereinafter device 512) suitable for use in implementing embodiments of the invention. The device 512 shown in fig. 5 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.
As shown in fig. 5, device 512 is in the form of a general purpose device. Components of device 512 may include, but are not limited to: one or more processors 516 (one processor is illustrated in fig. 5), a memory device 528, and a bus 518 that couples various system components including the memory device 528 and the processors 516.
A program/utility 540 having a set (at least one) of program modules 542 may be stored, for example, in storage 528, such program modules 542 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. The program modules 542 generally perform the functions and/or methods of the described embodiments of the invention.
The device 512 may also communicate with one or more external devices 514 (e.g., keyboard, pointing terminal, display 524, etc.), with one or more terminals that enable a user to interact with the device 512, and/or with any terminals (e.g., network card, modem, etc.) that enable the device 512 to communicate with one or more other computing terminals. Such communication may occur via input/output (I/O) interfaces 522. Also, the device 512 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the internet) via the Network adapter 520. As shown in FIG. 5, the network adapter 520 communicates with the other modules of the device 512 via the bus 518. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the device 512, including but not limited to: microcode, end drives, Redundant processors, external disk drive Arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems, among others.
The processor 516 executes programs stored in the storage device 528 to perform various functional applications and data processing, for example, implementing a microfluidic chip droplet path planning method provided in any embodiment of the present invention, which may include:
acquiring path planning information of the liquid drop;
determining respective target moving paths of the liquid drops through an evolution algorithm and a path search algorithm according to the path planning information;
and if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths as legal paths.
EXAMPLE six
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for planning a droplet path of a microfluidic chip according to any embodiment of the present invention, and the method may include:
acquiring path planning information of the liquid drop;
determining respective target moving paths of the liquid drops through an evolution algorithm and a path search algorithm according to the path planning information;
and if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths as legal paths.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (7)
1. A microfluidic chip droplet path planning method is characterized by comprising the following steps:
acquiring path planning information of the liquid drop;
determining respective target moving paths of the liquid drops through an evolution algorithm and a path search algorithm according to the path planning information;
if any two liquid drops accord with the liquid drop constraint rule when moving according to the respective target moving paths, determining all the target moving paths as legal paths;
the path planning information comprises chip size, barrier area, and the number, starting point and end point of each liquid drop;
determining the respective target movement paths of the droplets through an evolution algorithm and a path search algorithm according to the path planning information comprises:
constructing a chip model according to the chip size and the barrier area;
in the chip model, determining a target moving path of each liquid drop from a starting point to an end point through an evolution algorithm and a path searching algorithm;
the determining the target movement path of each droplet from the start point to the end point by the evolution algorithm and the path search algorithm comprises:
initializing a population, the population comprising a plurality of randomly generated individuals, the individuals being in a droplet movement sequence;
determining a composite movement path for each individual based on the modified dijkstra algorithm, wherein the composite movement path for an individual comprises a movement path for each droplet in the individual from a starting point to an ending point;
determining the fitness of each individual based on the comprehensive moving path of each individual;
when the iteration times of the population do not exceed the preset times, crossing and varying the individuals in the population, and returning to the step of determining the comprehensive moving path of each individual based on the improved Dijkstra algorithm;
and when the iteration times of the population exceed the preset times, determining the target moving path of each liquid drop according to the fitness of each individual.
2. The method of claim 1, wherein said determining a composite movement path for each individual based on the modified dijkstra algorithm comprises:
based on an improved Dijkstra algorithm, sequentially acquiring a non-time sequence moving path of each liquid drop in each individual according to the moving sequence of the liquid drop in each individual;
timing constraints are added to all non-timing movement paths for each individual to determine a composite movement path for each individual.
3. The method of claim 2, wherein determining the target movement path of the droplet based on the fitness of each individual comprises:
determining an optimization type of the droplet path plan;
if the optimization type is single-target optimization, taking the comprehensive moving path corresponding to the individual with the minimum fitness as a target moving path;
and if the optimization type is multi-objective optimization, determining at least one optimal fitness through a multi-objective optimization algorithm, and taking the comprehensive moving path corresponding to the individual with the at least one optimal fitness as a target moving path.
4. The method of claim 1, wherein if any two droplets move according to respective target movement paths and meet a droplet constraint rule, determining all target movement paths to be legal, further comprising:
and displaying the dynamic moving process of the liquid drops according to the respective target moving paths through a visual interface.
5. A microfluidic chip droplet path planning apparatus suitable for use in the microfluidic chip droplet path planning method of claims 1-4, comprising:
the path planning information acquisition module is used for acquiring path planning information of the liquid drops;
the target moving path determining module is used for determining respective target moving paths of the liquid drops according to the path planning information;
and the legal path determining module is used for determining all the target moving paths as legal paths if any two liquid drops accord with the liquid drop constraint rules when moving according to the respective target moving paths.
6. A computer device, characterized in that the device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the microfluidic chip droplet path planning method of any one of claims 1-4.
7. A computer-readable storage medium, on which a computer program is stored, which program, when executed by a processor, implements a microfluidic chip droplet path planning method according to any one of claims 1 to 4.
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