CN106886843B - Digital microfluidic chip fault detection method and system based on improved particle swarm optimization - Google Patents
Digital microfluidic chip fault detection method and system based on improved particle swarm optimization Download PDFInfo
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Abstract
The invention relates to a digital microfluidic chip fault detection method and system based on an improved particle swarm algorithm, belongs to the field of fault detection of a micro-digital microfluidic chip, and aims to overcome the defect that the fault location time of a digital microfluidic chip fault detection method in the prior art is long, so that a digital microfluidic chip fault detection method based on the improved particle swarm algorithm is provided, and comprises the following steps: acquiring the initial position and the final position of the test liquid drop; constructing a tabu table; constructing at least one particle swarm, and constructing a position matrix corresponding to each particle swarm; determining the velocity vector of each particle in the particle swarm algorithm until all adjacent electrodes are traversed; updating the position sequence of the particles according to a formula; calculating the fitness of the position vector of each particle, and respectively determining the current shortest path and the global shortest path of each population; and repeating the steps until the preset iteration times are reached, and outputting the global shortest path. The invention is suitable for fault detection of the digital microfluidic chip.
Description
Technical Field
The invention relates to a digital microfluidic chip fault detection method and system based on an improved particle swarm algorithm, and belongs to the field of micro-digital microfluidic chip fault detection.
Background
With the development of technology, the field of automatic testing has been expanded from testing analog circuits or digital circuits to testing Micro-Electromechanical Systems (MEMS), microfluidic chips, also known as lab-on-a-chips (L ab-on-a-chips), can perform various functions of biological laboratories and conventional chemical tests on a few square centimeters of chip.
Compared with continuous fluid control, the digital microfluidic chip emphasizes that liquid is dispersed into trace droplets to be operated, each droplet is independently controlled, the energy consumption is low, and the digital microfluidic chip is particularly suitable for biochemical analysis which needs high performance and is relatively complex to operate. Compared with the traditional biochemical analyzer, the digital microfluidic chip has the advantages of reusability, small size, high automation degree, high integration level and the like. The device has the capability of accurately driving trace liquid (liquid with the level of micro-liter or even nano-liter), completing the operations of transporting, storing, separating, mixing and the like of the fluid on a chip, completing the ultrasensitive biochemical detection with low cost, remarkably reducing the testing time and the laboratory space, and increasing the stability and the accuracy of the result due to the reduction of manual operation processes. Therefore, the method has wide application prospects in the aspects of clinical diagnosis, biomedical treatment, health examination, drug diagnosis, air quality detection and the like, and has important significance.
With the development of digital microfluidic chips, in order to meet the requirements of more and more complex biochemical analysis experiment systems, the scale and chip density of the digital microfluidic chip are rapidly expanded, so that various physical faults and production faults are very easy to occur in the using process, and the faults are dangerous for the microfluidic system and are also easy to cause destructive faults. Meanwhile, the digital microfluidic biochip is often used in safety critical fields such as biological detection, clinical diagnosis, drug development and the like, and the reliability of the digital microfluidic biochip becomes an important standard for manufacturing and designing. To ensure the reliability of the chip system, an efficient and comprehensive fault detection is required. The effectiveness test of the chip is not only carried out after the chip is produced and before biochemical detection is carried out, but also is continuously carried out in the experiment process so as to ensure the stability. After the fault detection is completed, in order to implement reconfiguration of the experimental liquid drop walking route, accurate fault location needs to be carried out on the chip array. How to plan the test path of the test liquid drop is the core problem of saving the chip test time and improving the chip test efficiency.
The testing method of the digital microfluidic chip is to make the actually measured test droplet traverse the electrode unit of the chip, so the length of the path of the experimental droplet directly influences the length of the testing time. In order not to influence the normal work of experimental liquid drops, the fault detection of the digital microfluidic chip array belongs to the problem of path optimization under resource limitation and belongs to the problem of NP difficulty.
Therefore, a new fault detection method for the digital microfluidic chip is needed, so that on the premise of ensuring the fault coverage rate, the fault positioning time is shortened to the greatest extent, the fault detection efficiency is improved, the detection cost is saved, and the safety of the digital microfluidic chip is ensured.
Disclosure of Invention
The invention aims to solve the defect of long fault positioning time of a digital microfluidic chip fault detection method in the prior art, and provides a novel digital microfluidic chip fault detection and positioning method based on an improved particle swarm algorithm.
The digital microfluidic chip fault detection method based on the improved particle swarm optimization is characterized by comprising the following steps of:
the method comprises the following steps: acquiring the initial position and the final position of the test liquid drop; the test liquid drop is used for moving between adjacent electrode arrays of the digital microfluidic chip to judge whether a fault exists between the adjacent electrode arrays; the edges between every two adjacent electrode arrays are assigned with numbers different from each other;
step two: constructing a taboo table, wherein the taboo table is used for storing the sides of the liquid drops which cannot be accessed at the current position and the sides which are accessed;
step three: constructing at least one particle swarm, constructing a position matrix corresponding to each particle swarm, wherein the row number of the position matrix represents the total number of particles in the particle swarm algorithm; the column number of the position matrix represents the total number of edges formed between the adjacent electrode arrays; elements in the position matrix represent velocity vectors of particular particles at particular electrode arrays; the speed vector is the serial number of the edge where each particle is located at the next moment;
step four: determining the velocity vector Speed of each particle in the particle swarm algorithm until all edges are traversed; the velocity vector Speed is specifically determined by a random one of the following ways:
A. selecting a random, selection-allowed side Speed1；
B. Selecting a Speed of a side closest to the current position2；
C. Selecting the edge Speed adjacent to the position of the current moment in the shortest path sequence obtained by the last iteration3；
Step five: according to the formula
Updating the position sequence of the particle, wherein Xt＝(1,x1,x2,...,xt,0,...)，xtIs the position of the particle at time t, Vt＝(0,0,0,...,xt+1,0,...)，xt+1＝Speed；
Step six: calculating the fitness of the position vector of each particle and determining each particle separately
Current shortest path PbestiAnd global shortest path Gbest; the fitness is used for representing the length of a path corresponding to the generated ordered sequence of the edges;
step seven: and repeating the iteration steps from the third step to the sixth step until the preset iteration times are reached, and outputting the global shortest path Gbest.
The invention has the beneficial effects that: by using the particle swarm algorithm, the fault positioning time is shortened to the greatest extent on the premise of ensuring the fault coverage rate, the fault detection efficiency is improved, the detection cost is saved, and the safety of the digital microfluidic chip is ensured. Meanwhile, the invention integrates the ideas of the PSO algorithm and the greedy algorithm, so that the improved particle swarm optimization can solve the difficulty in solving the path optimization problem of the basic particle swarm, and has higher efficiency. The position, the speed and the operation of the particles in the basic PSO algorithm are redefined, so that the particle swarm optimization is more suitable for solving the shortest path optimization problem, and the online fault detection of the digital microfluidic chip is better completed. Meanwhile, the invention provides a method for positioning the fault by using the infrared light emitting tube and the infrared receiving tube, which can effectively find the fault point and is convenient for the reconstruction of the subsequent experimental liquid drop path.
Drawings
FIG. 1 is a flow chart of the method for detecting the fault of the digital microfluidic chip based on the improved particle swarm optimization of the invention;
FIG. 2(a) is a test result of the digital microfluidic chip without a fault unit;
fig. 2(b) is a test result of the digital microfluidic chip with a faulty unit;
FIG. 3(a) is a schematic diagram of a droplet moving from a starting point of a digital microfluidic chip according to an embodiment; wherein the connection of the two electrodes represents a short circuit; the arrow indicates the direction of movement;
FIG. 3(b) is a schematic illustration of the droplet of FIG. 3(a) moving to a short circuit position;
FIG. 4(a) is a schematic diagram of the movement of a droplet of a digital microfluidic chip from a starting point in another embodiment; wherein the connection of the two electrodes represents a short circuit; the arrow indicates the direction of movement;
FIG. 4(b) is a view of the droplet of FIG. 4(a) passing a failed electrode due to deviation from the direction of movement;
FIG. 4(c) is a case where the droplet of FIG. 4(b) does not stay at the failed electrode but moves to the next electrode;
fig. 5 is a schematic diagram of a model conversion of the microfluidic chip into a graph.
Detailed Description
The basic principle involved in the invention is as follows:
the mode of driving the microfluid by the digital microfluidic chip is dielectric wetting driving. The liquid drop is driven by the liquid-solid surface tension change on the hydrophobic polymer surface by applying an electric field to the liquid drop through the electrode array to change the surface tension of the liquid drop. In order to make the liquid drop move, a driving voltage is applied to the adjacent electrode units, the surface of the liquid drop is enabled to accumulate electric quantity by utilizing the dielectric wetting principle, so that a surface tension gradient covering the adjacent electrodes is generated on the surface of the liquid drop, and when the tension is larger than the resistance between the upper surface and the lower surface and the liquid drop, the driving of the liquid drop movement can be completed, and the driving is the most basic method for controlling the liquid drop movement. The basic operations in the implementation of biochemical assays on a chip can be achieved by applying a sequence of voltages across the corresponding array of electrodes, such as: droplet dispensing, transporting, storing, mixing and separating, and the like.
The fault types of the digital microfluidic chip are divided into two types: parametric faults and permanent faults. Parametric faults are mainly generated in the manufacturing process, for example, due to size parameter errors, when the electrode array is not horizontal, the two layers of surfaces are not parallel or the electrode thickness is not uniform, the driving of the digital liquid drop is influenced, and the influence of the faults on the experimental result is represented as large deviation, so that the performance of the chip is seriously influenced.
Permanent failures are caused by open and short circuits between the electrode elements of the chip, which may originate from the manufacturing process or from electrode degradation due to an inappropriate control voltage. The permanent fault can cause the liquid drop to stay in the fault unit, can not advance according to the designed route, can not finish the experiment and move to the waste liquid pond, leads to biochemical detection's failure, and the application in the field that the security requirement is high can produce great harmful effects. The invention mainly considers the online fault detection method of permanent faults.
The permanent fault of the digital microfluidic chip mainly causes that liquid drops of the system cannot move, so that whether the fault exists can be judged according to whether the test liquid drops move normally, but the fault can be generated in the process of experiment, so that the chip can be ensured to work stably for a long time only by continuously testing the chip. An on-line test method that enables detection and test of droplets: the voltage is applied to the electrodes so as to control the test liquid drops to start from the independent detection liquid storage tank, the test liquid drops walk according to the test rule on the premise of not influencing the test liquid drops, and the test end point is finally reached by traversing the array unit. And (4) adding a capacitance detection circuit at the end point to judge the arrival of the test liquid drop, thus completing the fault test. As shown in fig. 2:
however, it is impossible to detect a failure by simply traversing a droplet through each electrode array, and it is known from the above description that a permanent failure may be caused by a short circuit between electrodes, and different situations may occur when a test droplet experiences a short circuit between adjacent electrodes, where fig. 3 and 4(a) show the droplet start position and the moving direction, and the continuous line indicates a short circuit between two electrodes, i.e., the driving voltages are generated simultaneously and disappear. For fig. 3, the moving direction is parallel to the short circuit direction of the electrodes, and when the droplet passes through the two electrodes with short circuit, the droplet stays in the middle of the short circuit electrodes due to simultaneous generation of left and right driving voltages and disappearance, as shown in (b). With respect to fig. 4, the direction of droplet travel is perpendicular to the direction of the shorting electrode, and there is a slight deviation as shown in (b) when passing the failure location, but still passing the shorting electrode to the next array of cells, e.g., (c), and not staying at the failed cell.
Therefore, in order to prevent the above situation, all array units and adjacent array units need to be tested, so as to ensure the validity of detection and the reliability of chip operation. The problem of online fault detection of the digital microfluidic chip is converted into the problem of finding the shortest traversing all array units and adjacent array units.
The invention uses the basic particle swarm algorithm to solve the problem of fault detection of the digital microfluidic chip.
The principle of the Particle Swarm Optimization (PSO) mainly simulates the foraging behavior of the flying of the bird Swarm, and the purpose of optimizing is achieved through the collective cooperation of the bird Swarm. In the PSO algorithm, each particle continuously flies in a solution space by using the historical optimal position of the particle and information provided by the global optimal solution of the whole particle swarm, so that the purpose of searching the optimal solution is achieved.
Let the search space be N-dimensional, the total number of particles be Num, and the position of the ith particle be xi＝(xi1,xi2,...,xin) The velocity vector of the ith particle is Vi＝(vi1,vi2,...,vin) The optimal position of the ith particle in flight is Pi＝(Pi1,Pi2,...,Pin)，PsRepresenting the globally optimal particles found so far throughout the population, the particles fly as follows:
vij t＝w×vij t+c1×r1×[Pij t-xij t]+c2×r2×[Psj t-xij t]
xij t+1＝xij t+vij t+1
wherein, the subscript j represents the jth dimension, and t is the flying times; w is an inertia weight, so that the particles keep moving inertia, the influence of the previous speed on the current speed is controlled, the larger w is suitable for large-scale exploration of a solution space, and the smaller w is suitable for small-range optimization; c. C1,c2The value of c is usually 0-2 for the acceleration constant1Step length for adjusting flying direction of particle to best position, c2Adjusting the flight step length of the particles to the global best position; r is1,r2Is [0,1 ]]Random numbers independent of each other. The variation range of each dimension in the position vector of the particle is [ X ]min,Xmax]The speed variation range is [ V ]min,Vmax]And taking a boundary value if the position and the speed exceed the boundary range in the iteration. The PSO algorithm changes the position by the fact that the velocity vector of the particles continuously changes in a solution space, and finally an optimal solution is found.
Analyzing from the perspective of sociology, the first part of the velocity iterative formula is a 'memory' item, which is the velocity before the particle, and indicates that the current velocity vector of the particle is affected by the last velocity; the second part of the formula is a self-cognition term which is a vector pointing to the particle individual optimum from the current point particle position and indicates that the motion of the particle is derived from the previous experience of the particle; the third part of the formula is a 'group cognition' item, which is a vector pointing from the current point to the optimal point of the group and reflects information sharing and cooperative cooperation among particles. The particle determines the velocity and position at the next time through experience and population optimization experience. The first part has the local searching and global balancing capacity, and the second part ensures that the particles have the local searching capacity and better explore a solution space; the third part shows information sharing among the particles, so that the particles can explore a wider space, and the particles can effectively search an optimal solution.
The first embodiment is as follows: the digital microfluidic chip fault detection method based on the improved particle swarm algorithm in the embodiment is shown in fig. 1, and comprises the following steps:
the method comprises the following steps: acquiring the initial position and the final position of the test liquid drop; the test liquid drop is used for moving between adjacent electrode arrays of the digital microfluidic chip to judge whether a fault exists between the adjacent electrode arrays; the edges between every two adjacent electrode arrays are assigned different numbers.
Specifically, the starting position and the end position are set to correspond to a liquid storage tank and a waste liquid tank on the lion microfluidic chip, and experimental liquid drops need to start from the liquid storage tank, traverse all electrode arrays and adjacent electrode arrays and then reach the waste liquid tank, so that the starting position and the end position of the liquid drops need to be determined firstly, the dimension of a solution space is reduced, and the efficiency of a test process is improved. Meanwhile, the sides which cannot be visited and the sides which are walked are determined according to the position of the experimental liquid drop and stored in a tabu table, and the tabu table is updated in time according to the movement of the experimental liquid drop.
The invention converts the problem that the chip traverses two adjacent units into a graph and establishes a mathematical model. Considering the structural characteristics of the chip shape rule, in order to conveniently solve the problem by using the discrete particle swarm algorithm, the physical model in the chip is equivalent by using the related concepts in the graph theory. Each electrode unit is equivalent to a point in the figure, two electrode units adjacent in the vertical or horizontal direction are equivalent to a side, the abstracted figure is represented by G ═ V, E, and the digital microfluidic chip model is shown in fig. 5.
Research shows that the digital microfluidic chip test problem is a problem of traversing the electrode array unit and the adjacent array unit. The model of the chip is a non-connected graph, and by the Euler path theory, we cannot find a path through each edge once and only once. Assuming that the time for the experimental liquid drop to pass through each edge is constant, the problem of the on-line test of the chip can be converted into the problem of finding the shortest path of all the edges in the traversal map. The shortest path d between two edges i, j is obtained by the Floy algorithmij. Assuming that the total number of edges that a test drop needs to traverse is n, xijRepresenting the edge to be accessed, the solution space of the problem is X ═ X (X)1,X2,...Xk) The ith feasible solution is Xi＝(xi1,xi2,...,xin) The fitness function of the feasible solution is
The on-line test problem can be translated into solving the optimal solution z:
the first step shows that the problem of the digital microfluidic chip fault online test is to visit different points and find an optimal path, which is similar to the problem of the TSP. The TSP problem requires each point to visit and only once, whereas the present problem does not have a loop that traverses and visits only each edge.
Step two: a tabu table is constructed for storing the edges that the drop cannot access at the current location and the edges that have already been accessed.
In particular, the inaccessible edge in the tabu table may include the edge where the experimental droplet is located. The experimental droplets are the droplets on the digital microfluidic chip that are present during normal use in the experiment. And the test droplet referred to in the present invention is another droplet for testing for a failure, different from the experimental droplet. The invention can carry out experiment normally in the experimental liquid dropThe test is carried out under the condition that a certain spacing distance is required between the test liquid drop and the test liquid drop, the two liquid drops cannot be positioned in the array units which are directly adjacent or diagonally adjacent, otherwise, the liquid drops are fused, so the static constraint condition is as follows: for arbitrary time unit t, di(test droplet) and dj(test drops) need to be more than one coordinate unit apart in the horizontal or vertical direction (otherwise the test drops may merge with the test drops):
|xi t-xj t| > 1 and | yi t-yj t|＞1
At the same time, diThe target position in the next time slice cannot be compared with djAdjacently, as a dynamic constraint, the mathematical formula is expressed as:
|xi t+1-xj t| > 1 and | yi t+1-yj t|＞1
This means that the problem of online detection of faults is subject to dynamic and static constraints due to the presence of the test droplets.
Step three: constructing at least one particle swarm, constructing a position matrix corresponding to each particle swarm, wherein the row number of the position matrix represents the total number of particles in the particle swarm algorithm; the column number of the position matrix represents the total number of edges formed between the adjacent electrode arrays; elements in the position matrix represent velocity vectors of particular particles at particular electrode arrays; the velocity vector is the sequence number of the edge where each particle is located at the next time.
Specifically, the core idea of applying the particle swarm algorithm to the fault test problem is as follows: and (4) each particle starts from the initial position, sequentially generates the position of the edge of the next moment according to the algorithm flow, and forms a motion sequence. Setting algorithm parameters: maximum number of iterations gen of the algorithmmaxVelocity parameter rand1、rand2And rand3And the number of particles a. And initializing iteration times, wherein the particle swarm algorithm needs to generate the position of an initial particle swarm. In the invention, in order to improve the detection efficiency, shorten the detection time and improve the particle swarm algorithm, the particle swarm algorithm can be simultaneously producedMultiple populations are generated and searches are conducted simultaneously.
For example, when two populations a and B are generated, assuming that the test droplet starts from side "1", then initial position vectors for population a and population B particles are generated in terms of total side number and total number of particles, respectively. The position matrix is used to record the sequential arrangement of the edges that each particle goes through when searching, and the velocity vector represents the position of the particle at the next time. The particle swarm has large scale, so that the optimal solution is easier to store, the number of iterations is small when the optimal path is found, but the time required for each iteration is longer.
Step four: determining the velocity vector of each particle in the particle swarm algorithm until all edges are traversed; the velocity vector is specifically determined by a random one of the following ways:
A. a random one of the allowed selected edges Speed1 is selected.
B. The side Speed2 closest to the current position is selected.
C. And selecting Speed3 adjacent to the current time position in the shortest path sequence obtained in the last iteration.
Specifically, the present invention defines the position of the particle at the next time, i.e., the serial number of the edge where the particle is located, as the velocity vector. The particle velocity vectors are selected using a combination of greedy and particle swarm optimization, where the velocity vectors are randomly selected by A, B, C, among the three methods described above. The optimal parameter can be obtained by adjusting A, B, C the ratio of probabilities.
The method for determining the velocity vector is described below by taking the microfluidic chip of fig. 5 as an example:
setting the shortest optimal path of the complete particles formed by the previous iteration as follows: gbest ═ 1, 5, 7, 3, 8, 2, 6, 4, 12, 9, 10, 11)
3 particles are totally obtained, the path search is carried out to the fifth moment, and the formed motion sequence is as follows;
for granuleSeed 1, randomly generating a new location Speed that has not been walked over18. As can be seen from the figure, the edges closest to the position "10" at the previous moment are "12, 7, 5", where Speed is chosen randomly212. Looking at the optimal path, "11" after the "10" position, and particle 1 has not yet walked "11" in this search, Speed3＝11。
Let the influence of the 3-part velocity component be 20%, 50%, 30%, respectively. Then the parameter rand is set1＝0.2、rand2＝0.7、rand3Generating a random number p between 0 and 1 if p is 0.1<rand1Selecting Speed as Speed1Is the next time position; if rand1<p<rand2Selecting Speed as Speed2Is the next time position; rand2<p<rand3Selecting Speed as Speed3The next time position.
Assuming that the random number p is 0.5627, Speed is 12, and the position matrix becomes:
according to this method, the position of each particle at each time is updated.
Step five: according to the formula
Updating the position sequence of the particle, wherein Xt＝(1,x1,x2,...,xt,0,...)，xtIs the position of the particle at time t, Vt＝(0,0,0,...,xt+1,0,...)，xt+1＝Speed。
Specifically, as can be seen from the above description, the digital microfluidic online test is a TSP-like problem, and belongs to a discretization problem. Solving this problem with the particle swarm algorithm described above requires redefining the position, velocity codes and operators of the particles in the basic PSO algorithm. The position of a particle is the sequence of edges traversed, the velocity is defined as the position of the particle at the next instant, the position sequence of a particle at the t-th instantColumn is Xt＝(1,x1,x2,...,xt,0,...)，Vt＝(0,0,0,...,xt+10. -) where x ist +1Speed (see step four), the location update formula is defined as:
namely Xt+1＝(1,x1,x2,...,xt,xt+1,0,...)。
Step six: calculating the fitness of the position vector of each particle, and respectively determining the current shortest path Pbest of each particle swarmiAnd global shortest path Gbest; fitness is used to represent the length of the path to which the generated ordered sequence of edges corresponds.
Step seven: repeating the third step to the sixth step until reaching the preset iteration number genmaxAnd outputs the global shortest path Gbest.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is:
in step four, the velocity vector Speed is specifically 20% determined by way a, 50% determined by way B, and 30% determined by way C.
It was mentioned above that the optimum parameters can be adjusted by adjusting A, B, C the ratios of the three modes, which in this embodiment provides a set of preferred ratios.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first or second embodiment in that:
the digital microfluidic chip is also provided with an experimental liquid drop, and the experimental liquid drop and the test liquid drop meet the following conditions:
|xi t-xj t| > 1 and | yi t-yj t|＞1
|xi t+1-xj t| > 1 and | yi t+1-yj t|＞1
Wherein x isi tDenotes the abscissa, y, of the test drop at time ti tDenotes the ordinate, x, of the test drop at time tj tDenotes the abscissa, y, of the experimental drop at time tj tDenotes the ordinate, x, of the test drop at time ti t+1Denotes the abscissa, y, of the test drop at time t +1i t+1The ordinate of the test drop at time t +1 is shown.
That is, in order to ensure that the test droplet and the test droplet do not merge, static constraints and dynamic constraints need to be satisfied.
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment mode and one of the first to third embodiment modes is:
in the mode a of the fourth step, the "edge allowed to be selected" is any edge not in the tabu table. The advantage of setting up like this is, can avoid the combination of test liquid drop and experiment liquid drop as far as possible, reduces the probability that the test failed.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the embodiment provides a digital microfluidic chip fault detection system based on an improved particle swarm algorithm, which comprises: at least one reservoir for generating test droplets and/or test droplets; the waste liquid pool is used for recovering the experimental liquid drops passing through the microfluidic chip; an electrode connected with the liquid storage pool on the microfluidic chip of the microfluidic chip is used as an initial electrode, and an electrode connected with the waste liquid pool is used as a terminal electrode; and the main control unit is used for generating a global shortest path according to the method of any one of the first to the fourth embodiments, and controlling the experiment liquid drop to move along the global shortest path by controlling the electrode of the microfluidic chip.
The embodiment has the beneficial effect of providing a hardware structure for specifically applying the online test method of the digital microfluidic chip.
The sixth specific implementation mode: the fifth embodiment is different from the fifth embodiment in that:
the electrode array of micro-fluidic chip is n × n, and the system still includes:
the first infrared light-emitting tube group and the second infrared light-emitting tube group are respectively arranged on the outer sides of two adjacent edges of the microfluidic chip and respectively comprise n infrared light-emitting tubes corresponding to the electrodes; the first infrared receiving tube group and the second infrared receiving tube group are respectively arranged on the outer sides of the other two adjacent edges of the microfluidic chip, and each of the first infrared emitting tube group and the second infrared emitting tube group comprises n infrared emitting tubes corresponding to the electrodes and used for receiving infrared light emitted by the first infrared emitting tube group and the second infrared emitting tube group; the main control unit is used for determining the position of the electrode with the fault according to the serial number of the second infrared receiving tube which does not receive the infrared light.
Specifically, the liquid drop of the system can not move mainly due to the permanent fault of the digital microfluidic biochip, so that whether the fault exists can be judged according to the movement of the test liquid drop, namely the test liquid drop is triggered from the liquid storage tank, moves in the electrode array unit, and whether the test liquid drop arrives at the terminal end is detected. If a faulty cell is encountered, the droplet will catch on the array cell and fail to reach the end point. Therefore, the beneficial effect of the embodiment is that the fault position can be positioned by utilizing the infrared light emitting tube and the receiving tube, the infrared light emitting tube is arranged at the left end and the lower end of the digital microfluidic chip, and the infrared receiving tube is arranged at the electrode corresponding to the right end and the upper end. This allows a fast and accurate localization of the fault.
After the fault detection is finished, the digital microfluidic chip is provided with an experiment liquid drop and a detection liquid drop, the experiment liquid drop can continuously move along with the experiment, the detection liquid drop at the fault position is fixed at the position, the infrared light emitting tube is synchronously triggered by the driving voltage of the experiment liquid drop to emit light, and the position where the liquid drop exists is not output by the receiving tube. Assuming that n droplets are on the chip, after all the n droplets move, the row and column where the receiving tube does not output is the fault location.
The other steps and parameters are the same as those in the fifth embodiment.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (6)
1. A digital microfluidic chip fault detection method based on an improved particle swarm algorithm is characterized by comprising the following steps:
the method comprises the following steps: acquiring the initial position and the final position of the test liquid drop; the test liquid drop is used for moving between adjacent electrode arrays of the digital microfluidic chip to judge whether a fault exists between the adjacent electrode arrays; the edges between every two adjacent electrode arrays are assigned with numbers different from each other;
step two: constructing a taboo table, wherein the taboo table is used for storing the sides of the liquid drops which cannot be accessed at the current position and the sides which are accessed;
step three: constructing at least one particle swarm, constructing a position matrix corresponding to each particle swarm, wherein the row number of the position matrix represents the total number of particles in the particle swarm algorithm; the column number of the position matrix represents the total number of edges formed between the adjacent electrode arrays; elements in the position matrix represent velocity vectors of particular particles at particular electrode arrays; the speed vector is the serial number of the edge where each particle is located at the next moment;
step four: determining the velocity vector Speed of each particle in the particle swarm algorithm until all edges are traversed; the velocity vector Speed is specifically determined by a random one of the following ways:
A. selecting a random, selection-allowed side Speed1As a velocity vector Speed;
B. selecting a Speed of a side closest to the current position2As a velocity vector Speed;
C. selecting the last iterationEdge Speed adjacent to position of current time in shortest path sequence obtained by generation3As a velocity vector Speed;
step five: according to the formula
Updating the position sequence of the particle, wherein Xt＝(1,x1,x2,...,xt,0,...)，xtIs the position of the particle at time t, Vt＝(0,0,0,...,xt+1,0,...)，xt+1＝Speed；
Step six: calculating the fitness of the position vector of each particle, and respectively determining the current shortest path Pbest of each particle swarmiAnd global shortest path Gbest; the fitness is used for representing the length of a path corresponding to the generated ordered sequence of the edges;
step seven: and repeating the iteration steps from the third step to the sixth step until the preset iteration times are reached, and outputting the global shortest path Gbest.
2. The method according to claim 1, characterized in that in step four the velocity vector Speed is 20% in particular by way a, 50% in particular by way B and 30% in particular by way C.
3. The method according to claim 1 or 2, wherein the digital microfluidic chip further comprises an experimental droplet, and the experimental droplet and the test droplet satisfy the following condition:
|xi t-xj t| > 1 and | yi t-yj t|＞1
|xi t+1-xj t| > 1 and | yi t+1-yj t|＞1
Wherein x isi tDenotes the abscissa, y, of the test drop at time ti tDenotes the ordinate, x, of the test drop at time tj tDenotes the abscissa, y, of the experimental drop at time tj tDenotes the ordinate, x, of the test drop at time ti t+1Denotes the abscissa, y, of the test drop at time t +1i t+1The ordinate of the test drop at time t +1 is shown.
4. The method according to claim 3, wherein in the mode A of the step four, the edges allowed to be selected are any edges which are not in a tabu table.
5. A digital micro-fluidic chip fault detection system based on improved particle swarm optimization is characterized by comprising:
at least one reservoir for generating test droplets and/or test droplets;
the waste liquid pool is used for recovering the experimental liquid drops passing through the microfluidic chip;
the electrode connected with the liquid storage pool on the micro-fluidic chip is used as a starting electrode, and the electrode connected with the waste liquid pool is used as an end electrode;
a master control unit for generating a global shortest path according to the method of any one of claims 1 to 4, and controlling the experimental droplets to move along the global shortest path by controlling the electrodes of the microfluidic chip.
6. The system of claim 5, wherein the array of electrodes of the microfluidic chip is n × n, and further comprising:
the first infrared light-emitting tube group and the second infrared light-emitting tube group are respectively arranged on the outer sides of two adjacent edges of the microfluidic chip and respectively comprise n infrared light-emitting tubes corresponding to the electrodes;
the first infrared receiving tube group and the second infrared receiving tube group are respectively arranged on the outer sides of the other two adjacent edges of the microfluidic chip and are used for receiving infrared light emitted by the first infrared light-emitting tube group and the second infrared light-emitting tube group;
and the main control unit is used for determining the position of the electrode with the fault according to the serial number of the second infrared receiving tube which does not receive the infrared light.
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| CN112836397B (en) * | 2021-03-30 | 2023-02-21 | 福州大学 | Advanced comprehensive design method for minimizing storage under continuous microfluidic biochip |
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