Method, device, equipment and storage medium for consistency control of holographic optical tweezers

Abstract

The invention relates to a consistency control method, device, equipment and storage medium for holographic optical tweezers, and relates to the technical field of holographic optical tweezers. The consistency control method includes S1, obtaining the position information of the controlled cell, obstacle cells and environmental boundaries, as well as the target position. S2, constructing the desired formation including virtual cells and controlled cells based on the position information. S3, updating the position information and executing the following steps until the controlled cell moves to the target position. S6, obtaining the gravitational parameters and repulsive parameters of the virtual cell based on the target position and the position information. S7, obtaining the expected position of the virtual cell at the next moment based on the gravitational parameters and repulsive parameters. S8, obtaining the total repulsive parameters of the controlled cell based on the position information. S9, obtaining the expected position of the controlled cell at the next moment based on the total repulsive parameters, the expected formation and the expected position of the virtual cell at the next moment. S10, updating the light trap position of the holographic optical tweezers based on the expected position of the controlled cell at the next moment.

Description

Technical Field

[0001] The present invention relates to the technical field of holographic optical tweezers, and in particular to a consistency control method, device, equipment and storage medium for holographic optical tweezers. Background Art

[0002] Optical tweezers were invented by Ashkin in 1986. The principle is that laser light forms a light trap, where tiny objects are trapped by the light pressure. Moving the light beam or repositioning it allows the object to follow the trap. This allows for the displacement or surgical manipulation of tiny objects (such as viruses, bacteria, and intracellular organelles and components) under a microscope. Optical tweezers have been widely used in research at the microscopic level, from atoms to hundreds of micrometers.

[0003] Traditional single-optical tweezers can only capture and manipulate one particle at a time, which limits its application range and work efficiency. Holographic optical tweezers, on the other hand, can generate large arrays of randomly arranged and distributed point light traps to capture multiple particles simultaneously, enabling complex dynamic manipulation.

[0004] Most of the existing holographic optical tweezers' operation methods are limited to single-cell manipulation and still rely on manual manipulation, which is not only inefficient but also requires professional personnel to operate, which increases labor costs.

[0005] Some prior technologies recognize and process particle images captured by a CCD camera, employing the A-star algorithm to determine the transport path for individual particles. These techniques then employ a proportional-integral controller to control a two-dimensional motorized displacement stage to move a glass slide, achieving automated particle transport. This essentially involves individually controlling the movement of each cell, resulting in high computational complexity and low control accuracy. Furthermore, the cells are inconsistent in their movement, potentially interfering with each other.

[0006] In view of this, the applicant filed this application after studying the existing technology. Summary of the Invention

[0007] The present invention provides a consistency control method, device, equipment and storage medium for holographic optical tweezers to improve at least one of the above technical problems.

[0008] First,

[0009] An embodiment of the present invention provides a method for controlling consistency of holographic optical tweezers, which comprises:

[0010] S01. Obtain position information of at least two controlled cells, obstacle cells, and environmental boundaries, as well as a target position.

[0011] S02. Based on the position information, construct an expected formation including virtual cells and controlled cells using a virtual leader method, wherein the expected formation includes an expected relative distance between cells.

[0012] S03. Update the position information and execute the following steps until the controlled cell moves to the target position.

[0013] S06. Constructing the gravitational potential field and repulsive potential field of the virtual cell based on the target position and position information, and obtaining the gravitational parameters and repulsive parameters of the virtual cell based on the gravitational potential field and repulsive potential field of the virtual cell.

[0014] S07. Obtain the expected position of the virtual cell at the next moment according to the attraction parameter and the repulsion parameter.

[0015] S08. Construct a repulsive potential field where the controlled cell is located based on the position information. And obtain a total repulsive force parameter of the controlled cell based on the repulsive potential field where the controlled cell is located.

[0016] S09. Obtain the expected position of the controlled cell at the next moment according to the total repulsive force parameter of the controlled cell, the expected formation, and the expected position of the virtual cell at the next moment.

[0017] S10. According to the expected position of the controlled cell at the next moment, the light trap position of the holographic optical tweezers is updated by fuzzying the cerebellar neural network until the controlled cell moves to the expected position.

[0018] Second aspect,

[0019] An embodiment of the present invention provides a consistency control device for holographic optical tweezers, comprising:

[0020] The initial information acquisition module is used to obtain the position information of at least two controlled cells, obstacle cells and environmental boundaries, as well as the target position.

[0021] The expected formation construction module is used to construct an expected formation including virtual cells and controlled cells based on the position information using a virtual leader method, wherein the expected formation includes the expected relative distances between cells.

[0022] The loop module is used to update the position information and execute the following modules until the controlled cell moves to the target position.

[0023] The virtual cell force analysis module is used to construct the gravitational potential field and repulsive potential field of the virtual cell based on the target position and position information. Based on the gravitational potential field and repulsive potential field of the virtual cell, the gravitational and repulsive parameters of the virtual cell are obtained.

[0024] The virtual cell expected position module is used to obtain the expected position of the virtual cell at the next moment based on the attraction parameter and the repulsion parameter.

[0025] The controlled cell force analysis module is used to obtain the next moment expected position of the controlled cell according to the total repulsive force parameters of the controlled cell, the expected formation and the next moment expected position of the virtual cell.

[0026] The controlled cell expected position module is used to obtain the next moment expected position of the controlled cell according to the total repulsive force parameters of the controlled cell, the expected formation and the next moment expected position of the virtual cell.

[0027] The control module is used to update the light trap position of the holographic optical tweezers according to the next desired position of the controlled cell by fuzzy cerebellar neural network until the controlled cell moves to the desired position.

[0028] Thirdly,

[0029] An embodiment of the present invention provides a consistency control device for holographic optical tweezers, comprising a processor, a memory, and a computer program stored in the memory. The computer program can be executed by the processor to implement the consistency control method for holographic optical tweezers as described in any paragraph of the first aspect.

[0030] Fourthly,

[0031] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the consistency control method of holographic optical tweezers as described in any paragraph of the first aspect.

[0032] By adopting the above technical solution, the present invention can achieve the following technical effects:

[0033] The consistency control method of the embodiment of the present invention can simultaneously operate multiple cells to move to the target point in a preset formation structure, and can automatically avoid obstacles during the movement, thereby reducing the difficulty of using the holographic optical tweezers system, reducing labor costs, and improving the efficiency of using the holographic optical tweezers system to conduct various types of cell research. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1It is a flowchart of the consistency control method.

[0036] Figure 2 It is a schematic diagram of the structure of the expected formation between cells.

[0037] Figure 3 Schematic diagram of the intercellular topology.

[0038] Figure 4 It is the network structure diagram of the fuzzy cerebellar neural network.

[0039] Figure 5 It is a structural diagram of the consistency control device. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example 1

[0042] See also Figures 1 to 4 A first embodiment of the present invention provides a method for controlling the consistency of holographic optical tweezers, which can be performed by a consistency control device for holographic optical tweezers (hereinafter referred to as a control device). Specifically, the method is performed by one or more processors in the control device to implement steps S01 to S03 and steps S06 to S10.

[0043] S01. Obtain position information of at least two controlled cells, obstacle cells, and environmental boundaries, as well as a target position.

[0044] Specifically, the holographic optical tweezers system can acquire real-time image information of the controlled cell's environment, thereby obtaining the current position information of the controlled cell, obstacle cells, and the position information of the boundary. After acquiring the position information or after establishing the desired formation, a target position is set in the controlled cell's environment, to which the controlled cell is ultimately desired to be manipulated. The holographic optical tweezers system is then controlled using the consistency control method of an embodiment of the present invention to move the controlled cell to the target position.

[0045] In this embodiment, the holographic optical tweezers system can control two or more controlled cells, such as Figure 2 In the specific case shown, the controlled cells are three cells.

[0046] It is understandable that the control device may be an electronic device with computing capabilities, such as a portable notebook computer, a desktop computer, a server, a smart phone, or a tablet computer.

[0047] S02. Based on the position information, construct an expected formation including virtual cells and controlled cells using a virtual leader method, wherein the expected formation includes an expected relative distance between cells.

[0048] The embodiment of the present invention controls the movement of controlled cells through a virtual leadership method, thereby achieving consistent control, avoiding the trouble of individually controlling each controlled cell, and greatly improving the control efficiency.

[0049] Specifically, the virtual leadership method only needs to determine the movement state of the core individual in the group movement of the target group, and other individuals can follow the core individual to move. By maintaining the expected relative distance between the leader and the followers during the movement, the group formation can move in the expected formation and complete consistency control.

[0050] Furthermore, during multicellular movement, the path of movement is primarily determined by virtual cells. Since virtual leaders have no collision volume, they can ignore the repulsive forces of obstacles. This can address the problem of traditional leaders causing abnormal movement of the cell group, thereby reducing the high dependence of follower cells on the leader cell.

[0051] like Figure 2 and Figure 3 As shown, based on the above embodiment, in an optional embodiment of the present invention, step S02 specifically includes steps S021 to S023.

[0052] S021. Create virtual cells based on the location information.

[0053] S022. Based on the virtual cells, the controlled cells are regarded as followers of the virtual cells using the virtual leader method, and the desired relative distance between each controlled cell and the virtual cell is set to obtain the desired formation.

[0054] Specifically, a virtual cell is created within the controlled cell's environment and assigned an initial position. The controlled cell is treated as a follower in the virtual leader method. The virtual cell's size is set to match that of the controlled cells, and its initial position is set within the area enclosed by the controlled cells. Then, based on the number of controlled cells, a computer sets the desired relative distances between the virtual cell and each controlled cell, thereby forming a desired cell group formation.

[0055] like Figure 2As shown in the figure, circular icons 1, 2, and 3 represent the three yeasts to be manipulated, and circular icon 4 represents the set virtual cell. The cell diameter is 6 μm, and the relative distance between virtual cell 4 and cell 1, cell 2, and cell 3 is 7.5 μm respectively.

[0056] S023. Construct a directed topology graph based on the desired formation.

[0057] Specifically, the holographic optical tweezers system is used to obtain the current position information of the controlled cells and the obstacle cells, as well as the position information of the boundaries. Then, based on the characteristics of the holographic optical tweezers system that can obtain the image information of the cells and their environment in real time, graph theory is introduced to construct a directed topological graph G to represent the topological structure of multiple cells. Figure 3 .

[0058] In a directed topological graph G = (V,E), V = {1,2,…,n} is a node set, each representing a cell. E = {(i,j) | i,j∈V,i≠j} is an edge set, where (,j)∈E means that cell i can obtain the location information of cell j, but not vice versa.

[0059]

[0060] Given an edge weight a ij , A(G)=[a ij ]∈R n×n represents the adjacency matrix. The k-th value in the i-th row represents the information weight from the j-th node to the i-th node. The information weight given by any cell to itself is set to 0. The characteristic of this topological structure is that all controlled cells in the computer processing can obtain the location information of the virtual cell. When the n-th cell is determined to be a virtual cell, the adjacency matrix A can be expressed as:

[0061]

[0062] S03. Update the position information and execute the following steps until the controlled cell moves to the target position.

[0063] Specifically, the holographic optical tweezers system can obtain image information of the environment in which the controlled cells are located in real time, so as to update the current position information of the controlled cells, obstacle cells, and boundary position information in real time. The system updates the control of the controlled cells in real time based on the updated position information until the controlled cells move to the target position.

[0064] Preferably, based on the above embodiment, in an optional embodiment of the present invention, step S04 and step S05 are further included before step S06. In other embodiments, step S04 and step S05 may not be included.

[0065] S04. Obtain the distance between the virtual cell and the target position, and determine whether the distance is greater than a maximum moving distance threshold.

[0066] S05. When the distance is greater than the maximum moving distance threshold, an intermediate position is set according to the maximum moving distance threshold, so as to execute subsequent steps to first move the controlled cell to the intermediate position.

[0067] In this embodiment, due to the limited movement speed of cells in a solution environment in actual applications, and the desired position of the controlled cells at each moment is influenced by the virtual cells, the movement distance, as a value of the gravitational parameter, directly affects the movement speed of the controlled cells. Excessive movement speed can easily damage the cell structure. Similarly, similar constraints are also included in step S092 to limit the excessive movement speed caused by excessive repulsive parameters.

[0068] Specifically, calculate the straight-line distance between the virtual cell and the target point. Define q g is the target position, q g -q is the target point position q g The distance between the cell position q. Set a maximum moving distance threshold d max Used to limit the moving distance of the virtual cell. If the straight-line distance between the virtual cell and the target position exceeds the maximum moving distance of the virtual cell, q g -q>d max , then limit the distance to d max . This is equivalent to moving the virtual cell to the target position with d max Move in segments for standard division.

[0069] S06. Constructing the gravitational potential field and repulsive potential field of the virtual cell based on the target position and position information, and obtaining the gravitational parameters and repulsive parameters of the virtual cell based on the gravitational potential field and repulsive potential field of the virtual cell.

[0070] Specifically, when q g -q>d max , the target position in steps S06 to S09 is the middle position.

[0071] In the liquid environment in which cells reside, there are many obstacles, not just the cells being manipulated. Holographic optical tweezers systems must manipulate cells to avoid these obstacles. The artificial potential field method, with its intuitive definition and simple model structure, can achieve real-time obstacle avoidance without requiring extensive computational effort, making it highly practical.

[0072] On the basis of the above embodiment, in an optional embodiment of the present invention, step S06 specifically includes steps S061 to S064.

[0073] S061. Construct the gravitational potential field U of the virtual cell based on the target position and position information. att (q n ); the calculation model of the gravitational potential field where the virtual cell is located is:

[0074]

[0075] Where U att (q n ) represents the gravitational potential field of the target position on the virtual cell, k represents the gravitational coefficient, q g represents the target position, q n It represents the current position of the virtual cell.

[0076] S062. Obtain the gravitational parameter F of the virtual cell according to the gravitational potential field in which the virtual cell is located. att (q n ), where the calculation model of the gravitational parameters of the virtual cell is:

[0077]

[0078] Where, F att Indicates the gravitational parameter of the target position on the virtual cell, is the gradient operator symbol, U att (q n ) represents the gravitational potential field of the target position on the virtual cell, k represents the gravitational coefficient, q g represents the target position, q n It represents the current position of the virtual cell.

[0079] It can be seen that the closer the cell is to the target point, the smaller the calculated gravitational force is. g , it means that the virtual cell is not affected by the gravitational force field at the target point and remains stationary.

[0080] S063. Construct the repulsive potential field U of the virtual cell based on the position information. rep , and the solution environment boundary repulsive potential field U repa ; Among them, the calculation model of the repulsive potential field of the virtual cell and the repulsive potential field of the solution environment boundary is:

[0081]

[0082] Where U rep represents the repulsive potential field where the virtual cell is located, U repa represents the repulsive potential field at the boundary of the solution environment, η represents the repulsive coefficient, ρ a represents the distance between the cell and the boundary node, q irepresents the current position of the virtual cell, and ρ0 represents the distance at which the virtual cell is affected by the repulsion of the boundary nodes.

[0083] S064. Obtain the repulsive force parameter F of the virtual cell according to the repulsive potential field rep (q n ); wherein, the calculation model of the repulsive force parameters of the virtual cell is:

[0084]

[0085] Where, F repai (q n ) represents the repulsive force parameter of the boundary node on the virtual cell, η represents the repulsive force coefficient, ρ a represents the distance between the cell and the boundary node, q n represents the current position of the virtual cell, and ρ0 represents the distance at which the virtual cell is affected by the repulsion of the boundary nodes. Symbols for solving partial derivatives in advanced mathematics.

[0086] Specifically, the virtual leader can ignore the repulsive force of the obstacle cells because it has no collision volume. Therefore, the repulsive potential field in which the virtual cell is located is only the repulsive potential field of the solution environment boundary. Therefore, U rep =U repa , that is, F rep (q n )=F repai (q n ).

[0087] It can be seen that the closer the cell is to the solution environment boundary node, the larger the calculated repulsive force parameter is. When the distance between the cell and the solution environment boundary node is greater than the influence range of the environment boundary node, it is not affected by the repulsive force.

[0088] In this embodiment, to reduce the computational complexity of the algorithm, the solution environment boundary position information only selects a number of boundary nodes on the X and Y axes within a certain range centered on the virtual cell. These boundary nodes have the following characteristics: the distance between each two boundary nodes is the diameter of the controlled cell, thus ensuring a continuous repulsive field across the entire environment boundary.

[0089] S07. Obtain the expected position of the virtual cell at the next moment according to the attraction parameter and the repulsion parameter.

[0090] In this embodiment, a mass point model is established, through which the expected positions of the virtual cell and the controlled cell at each moment can be calculated. The mass point model is expressed as:

[0091]

[0092] Where n is the total number of cells (including all controlled cells and virtual cells), of which there are n-1 controlled cells and the nth cell is represented as a virtual cell. i d and q i represent the expected position and current position of the i-th cell, σ i represents the control input of the ith controlled cell, δ represents the control input of the virtual cell, and Δt represents the sampling time.

[0093] Based on the above embodiment, in an optional embodiment of the present invention, the calculation model of the expected position of the virtual cell at the next moment is:

[0094]

[0095] Where q n d (t) represents the expected position of the virtual cell at the next moment, q n (t) represents the current position of the virtual cell, δ represents the control input of the virtual cell, Δt represents the sampling time, F att (q n ) represents the gravitational parameter of the virtual cell, F rep (q n ) represents the repulsive force parameter of the virtual cell.

[0096] Specifically, the resultant force parameter of the virtual cell is expressed as F = F att +F rep The expected position of the virtual cell at the next moment is determined according to the mass point model and the resultant force parameters.

[0097]

[0098] In order to prevent excessive control input from causing the virtual cells to move too fast, thereby causing damage to the controlled cell structure, it is necessary to set δ≤5.

[0099] S08. Construct a repulsive potential field where the controlled cell is located based on the position information. And obtain a total repulsive force parameter of the controlled cell based on the repulsive potential field where the controlled cell is located.

[0100] Specifically, in real-world environments, cells often encounter numerous obstacles in addition to the cells they are controlling. Holographic optical tweezers systems must manipulate cells to avoid these obstacles. The artificial potential field method offers intuitive definitions, a simple model structure, and the ability to achieve real-time obstacle avoidance without requiring extensive computational effort, making it highly practical.

[0101] On the basis of the above embodiment, in an optional embodiment of the present invention, step S08 specifically includes steps S081 to S084.

[0102] S081. Construct the boundary repulsive potential field U of the solution environment where the controlled cell is located according to the position information. repa , and according to the solution environment boundary repulsive potential field U repa , obtain the repulsive force parameter F generated by the solution environment boundary on the controlled cell repa (q i );in,

[0103]

[0104]

[0105] Where η represents the repulsion coefficient, ρ a represents the distance between the controlled cell and the boundary node, q i represents the current position of the controlled cell, ρ0 represents the distance of the controlled cell affected by the exclusion of the boundary node, is the gradient operation symbol;

[0106] S082. Construct the barrier cell repulsive potential field U where the controlled cell is located based on the position information. repb , and according to the barrier cell repulsion potential field U repb , obtain the repulsive force parameter F generated by the obstacle cell on the controlled cell repb (q i );in,

[0107]

[0108]

[0109] Where η represents the repulsion coefficient, ρ b represents the distance between the controlled cell and the obstacle cell, q i represents the current position of the controlled cell, ρ0 represents the distance that the controlled cell is affected by the repulsion of the obstacle cell, is the gradient operation symbol;

[0110] S083. Based on the position information, construct the repulsive potential field U of the controlled cell other than the controlled cell itself. repc , and according to the repulsive potential field U of other controlled cells except itself repc , obtain the repulsive force parameter F generated by other controlled cells on the controlled cell repc (q i );in,

[0111]

[0112]

[0113] Where η represents the repulsion coefficient, ρ c Represents the distance between the controlled cell and other controlled cells, q i represents the current position of the controlled cell, ρ0 represents the distance that the controlled cell is affected by the repulsion of other controlled cells, is the gradient operation symbol;

[0114] S084. Repulsion parameter F generated by the solution environment boundary of the controlled cell repa (q i ), the repulsive force parameter F generated by the obstacle cell to which the controlled cell is subjected repb (q i ) and the repulsive force parameter F generated by other controlled cells on the controlled cell repc (q i ), obtain the total repulsive force parameter F of the controlled cell rep (q i ); wherein, the calculation model of the total repulsive force parameter of the controlled cell is:

[0115]

[0116] Where, represents the total repulsive force parameter exerted by the boundary node on the i-th controlled cell, represents the total repulsive force parameter exerted by the obstacle cell on the i-th controlled cell, F represents the total repulsive force parameter exerted by other controlled cells on the i-th controlled cell. rep (q i )F repi Represents the repulsive force parameter generated by the solution environment boundary nodes, obstacle cells, and other controlled cells except the current controlled cell. a , n b , n c Respectively represent the number of boundary nodes, obstacle cells, and other controlled cells except the current controlled cell.

[0117] Specifically, controlled cells are real cells that collide with the solution boundary, obstacle cells, and other controlled cells besides themselves. Therefore, the net force on the controlled cell includes the repulsive potential field generated by the solution boundary, obstacle cells, and other controlled cells besides itself, thus achieving the collision avoidance effect.

[0118] In this embodiment, the repulsive force parameter of the boundary node on the i-th controlled cell is The calculation formula and the repulsive potential field F of the virtual cellrep (q n ) is the same as the calculation formula. The virtual cell in the calculation formula is replaced by the i-th controlled cell, and the present invention will not be repeated here.

[0119] Repulsion parameter of the obstacle cell on the i-th controlled cell The calculation formula is:

[0120]

[0121] Repulsion parameters of other controlled cells on the i-th controlled cell The calculation formula is:

[0122]

[0123] Where U repb with U repc Represent the repulsive potential fields of the obstacle cell and other controlled cells, η is the repulsive coefficient, ρ b and ρ c They represent the distances between the obstacle cell, other controlled cells and the current controlled cell respectively, and ρ0 represents the distance affected by the cell being excluded by obstacles (here referring to boundary nodes, obstacle cells, and other controlled cells).

[0124] Similar to how virtual cells are affected by repulsion, the distance between the controlled cell and each obstacle is calculated. The closer the controlled cell is to each obstacle, the larger the calculated repulsion parameter, allowing it to automatically circumvent the obstacle. However, when the distance between the controlled cell and the obstacle is greater than the obstacle's influence range, the controlled cell is unaffected by repulsion.

[0125] S09. Obtain the expected position of the controlled cell at the next moment according to the total repulsive force parameter of the controlled cell, the expected formation, and the expected position of the virtual cell at the next moment.

[0126] Specifically, the controlled cell can only be moved using the holographic optical tweezers system after obtaining the desired position at the next moment. In this embodiment of the present invention, to calculate the desired position of the controlled cell, the controlled cell only needs to obtain the position information of the virtual cell and track it, which to a certain extent reduces information redundancy and computational complexity.

[0127] The controlled cells move by tracking the virtual cells. During the movement, they maintain a certain formation by the relative distance between the controlled cells. While avoiding obstacles, they can also avoid collisions between the controlled cells, which has great practical significance.

[0128] Based on the above embodiment, in an optional embodiment of the present invention, step S09 specifically includes steps S091 to S092.

[0129] S091. Obtain the control input γ of the controlled cell based on the desired formation and the next desired position of the virtual cell. i Among them, the calculation model of the control input is:

[0130]

[0131] Where, γ i represents the consistency control input of the i-th controlled cell, ω is a constant, n is the total number of cells in the desired formation, a ij is the boundary weight in the adjacency matrix A(G) of the directed topological graph, q j is the current position of the jth controlled cell. When j = n, q n represents the expected position of the virtual cell at the next moment, q i represents the current position of the i-th controlled cell, d ij is the relative position between the i-th controlled cell and the j-th controlled cell (ie, the expected relative distance in step S02).

[0132] Specifically, the multi-cell consistency control algorithm is used to calculate the expected position of each controlled cell in an obstacle-free situation so that each cell maintains a preset cell group formation structure at every moment.

[0133] The embodiment of the present invention proposes a multi-cell consistency control algorithm for a holographic optical tweezers system. The controlled cell adjusts its actual position by receiving position information from the virtual cell. ij The existence of will maintain a certain formation.

[0134] S092. Obtain the expected position of the controlled cell at the next moment based on the control input of the controlled cell and the total repulsive force parameter of the controlled cell. The calculation model of the expected position of the controlled cell at the next moment is:

[0135]

[0136] Where q i d (t) represents the expected position of the i-th controlled cell at the next moment, q i (t) represents the current position of the i-th controlled cell, σ i represents the control input of the i-th controlled cell, Δt represents the sampling time, γ i represents the consistency control input of the i-th controlled cell, F rep (q i ) represents the total repulsive force parameter of the controlled cell.

[0137] Specifically, the expected position of the controlled cell at the next moment can be calculated by the calculation model of the expected position of the controlled cell at the next moment, indicating the direction for the moving cell. In order to prevent the cell structure from being damaged due to excessive control input, it is necessary to set σ i ≤10.

[0138] Furthermore, before reaching the target location, the controlled cell may encounter an abnormal situation where the net force is zero, preventing the cell from properly avoiding obstacles. When this situation occurs, the embodiment of the present invention addresses this problem by applying a virtual force of 15, perpendicular to the net force direction and opposite to the direction of the nearest obstacle cell.

[0139] In this embodiment, when the controlled cell is not affected by the external repulsive force, F rep (q i )=0, then the calculation model of the expected position of the controlled cell at the next moment is:

[0140]

[0141] In this way, the expected position of each cell at each moment in which the cell maintains the preset cell group formation structure under the condition of no obstacles can be calculated.

[0142] S10. According to the expected position of the controlled cell at the next moment, the light trap position of the holographic optical tweezers is updated by fuzzying the cerebellar neural network until the controlled cell moves to the expected position.

[0143] In this embodiment of the present invention, after calculating the desired position of a cell at each moment, a fuzzy cerebellar neural network is used as a cell manipulation controller to manipulate each cell from its actual position to the desired position until it reaches the final target position. Specifically, the fuzzy cerebellar neural network has excellent approximation capabilities for uncertain linear systems.

[0144] like Figure 4 As shown, based on the above embodiment, in an optional embodiment of the present invention, step S10 specifically includes steps S101 to S102.

[0145] S101. Obtain the difference between the expected position of the controlled cell at the next moment and the current position.

[0146] S102. Input the difference into the fuzzy cerebellar neural network to obtain the light trap position of the holographic optical tweezers to move the controlled cell.

[0147] Specifically, the fuzzy cerebellar neural network is used as a cell manipulation controller, whose main function is to control the holographic optical tweezers system to place a light trap at a certain position to achieve the goal of manipulating the controlled cells to move to the desired position.

[0148] like Figure 4 As shown in the figure, the fuzzy cerebellar neural network has five network structures, namely the input layer, associative memory space, receptive field space, weight space and output layer, and each network space has its own structural rules.

[0149] In this embodiment, the cell desired position q in S034 is d The difference between the actual position q of the cell is used as the input data of the fuzzy cerebellar neural network, and then the fuzzy cerebellar neural network updates its parameters according to its own weight update rules to calculate the reasonable light trap position.

[0150] By calculating the error between the desired position and the actual position q of the cell controlled by the light trap position to be output by the current fuzzy cerebellar neural network, the network will iteratively learn and update the parameters until the error is zero or the maximum number of iterations is reached. The final output is that the controlled cell can be accurately moved to the desired position q at a certain moment. d Since the center of the light trap needs to be kept within a certain range of the cell center, the light trap position l is restricted so that it should satisfy lq|≤r0, at which point the cell will not escape the capture of the light trap.

[0151] Among the five network structures of the fuzzy cerebellar neural network:

[0152] The function of the input layer is to receive the input data of the fuzzy cerebellar neural network. H is defined as the input data.

[0153] Associative memory space is the numerical space after the input data is fuzzified. A Gaussian function, that is, the fuzzy membership function, is added to each block of each layer of the space.

[0154]

[0155] Where, α ijk Represents the center value of the Gaussian function of the kth block of the i-th input layer, β ijk Represents the variance of the Gaussian function of the i-th input j-th layer k-th block. Each input data will be blurred and stored in a block in a different layer.

[0156] The receptive domain space is composed of multiple "receptive domains", each of which has a layer structure and a block structure. The blocks activated by the same layer are accumulated to correspond to a weight address space.

[0157]

[0158] The weight space is used to store and update weights, where ω jko It is a hypercube that represents the weights required to calculate the output of each dimension.

[0159] The output layer is used to obtain the output O by solving the algebraic sum of the receptive field of each block in each layer and the activated value of the weight space.

[0160] The weights, center values ​​and variance values ​​of the fuzzy cerebellar neural network are updated according to its own parameter updating rules.

[0161]

[0162]

[0163]

[0164] The desired cell position q d The difference between the actual position q of the cell is used as the input data of the fuzzy cerebellar neural network. The network can output the appropriate light trap position and substitute it into the cell dynamics equation. is the kinetic coefficient related to the solution environment,

[0165] After the holographic optical tweezers system manipulates all controlled cells to reach the desired position at the next moment, the holographic optical tweezers system continues to detect the actual image position information of each cell and calculates the desired position of each cell at the next moment until each controlled cell reaches the desired position at the final moment under the manipulation of the cell manipulation controller.

[0166] The consistency control method of the embodiment of the present invention can simultaneously operate multiple cells to move to the target point in a preset formation structure, and can automatically avoid obstacles during the movement, thereby reducing the difficulty of using the holographic optical tweezers system, reducing labor costs, and improving the efficiency of using the holographic optical tweezers system to conduct various types of cell research.

[0167] Example 2

[0168] See also Figure 5 A second embodiment of the present invention provides a consistency control device for holographic optical tweezers, comprising:

[0169] The initial information acquisition module 1 is used to obtain the position information of at least two controlled cells, obstacle cells and environmental boundaries, as well as the target position.

[0170] The expected formation construction module 2 is used to construct an expected formation including virtual cells and controlled cells based on the position information using a virtual leader method, wherein the expected formation includes the expected relative distances between cells.

[0171] Loop module 3 is used to update the position information and execute the following modules until the controlled cell moves to the target position.

[0172] The virtual cell force analysis module 6 is used to construct the gravitational potential field and repulsive potential field of the virtual cell according to the target position and position information; and obtain the gravitational parameters and repulsive parameters of the virtual cell according to the gravitational potential field and repulsive potential field of the virtual cell.

[0173] The virtual cell expected position module 7 is used to obtain the expected position of the virtual cell at the next moment according to the attraction parameter and the repulsion parameter.

[0174] A controlled cell force analysis module 8 is configured to obtain the next moment expected position of the controlled cell according to the total repulsive force parameter of the controlled cell, the expected formation, and the next moment expected position of the virtual cell;

[0175] The controlled cell expected position module 9 is used to obtain the next moment expected position of the controlled cell according to the total repulsive force parameter of the controlled cell, the expected formation and the next moment expected position of the virtual cell.

[0176] The control module 10 is used to update the light trap position of the holographic optical tweezers according to the next desired position of the controlled cell by fuzzy cerebellar neural network until the controlled cell moves to the desired position.

[0177] On the basis of the above embodiment, in an optional embodiment of the present invention, the consistency control device further includes a distance judgment module and an intermediate position module.

[0178] The distance judgment module is used to obtain the distance between the virtual cell and the target position and determine whether the distance is greater than the maximum moving distance threshold.

[0179] The intermediate position module is used to set the intermediate position according to the maximum moving distance threshold when the distance is greater than the maximum moving distance threshold, so as to execute the subsequent steps to move the controlled cell to the intermediate position first.

[0180] Based on the above embodiment, in an optional embodiment of the present invention, the desired formation building module 2 specifically includes:

[0181] The virtual cell creation unit is used to create virtual cells according to position information.

[0182] The expected formation construction unit is used to regard the controlled cells as followers of the virtual cells according to the virtual cell and the virtual leader method, and to set the expected relative distance between each controlled cell and the virtual cell to obtain the expected formation.

[0183] The topology graph construction unit is used to construct a directed topology graph according to the desired formation.

[0184] Based on the above embodiment, in an optional embodiment of the present invention, the virtual cell force analysis module 6 specifically includes:

[0185] A virtual cell gravitational potential field construction unit is used to construct the gravitational potential field U of the virtual cell according to the target position and the position information. att (q n );in, Where U att (q n ) represents the gravitational potential field of the target position on the virtual cell, k represents the gravitational coefficient, q g represents the target position, q n It represents the current position of the virtual cell;

[0186] The virtual cell gravity parameter calculation unit is used to obtain the gravity parameter F of the virtual cell according to the gravitational potential field. att (q n ),in, Where, F att Indicates the gravitational parameter of the target position on the virtual cell, is the gradient operator symbol, U att (q n ) represents the gravitational potential field of the target position on the virtual cell, k represents the gravitational coefficient, q g represents the target position, q n It represents the current position of the virtual cell;

[0187] A virtual cell repulsive potential field construction unit is used to construct the repulsive potential field U of the virtual cell according to the position information. rep , and the solution environment boundary repulsive potential field U repa ;in, Where U rep represents the repulsive potential field where the virtual cell is located, U repa represents the repulsive potential field at the boundary of the solution environment, η represents the repulsive coefficient, ρ a represents the distance between the cell and the boundary node, q i represents the current position of the virtual cell, ρ0 represents the distance of the virtual cell affected by the exclusion of the boundary node;

[0188] The virtual cell repulsion parameter calculation unit is used to obtain the repulsion parameter F of the virtual cell according to the repulsion potential field. rep (q n ); wherein the repulsive force parameter F of the virtual cell is rep (q n ) is calculated as:

[0189]

[0190] Where, F repai (q n) represents the repulsive force parameter of the boundary node on the virtual cell, η represents the repulsive force coefficient, ρ a represents the distance between the cell and the boundary node, q n represents the current position of the virtual cell, and ρ0 represents the distance at which the virtual cell is affected by the repulsion of the boundary nodes.

[0191] Based on the above embodiment, in an optional embodiment of the present invention, the calculation model of the expected position of the virtual cell at the next moment is:

[0192]

[0193] Where q n d (t) represents the expected position of the virtual cell at the next moment, q n (t) represents the current position of the virtual cell, δ represents the control input of the virtual cell, Δt represents the sampling time, F att (q n ) represents the gravitational parameter of the virtual cell, F rep (q n ) represents the repulsive force parameter of the virtual cell.

[0194] Based on the above embodiment, in an optional embodiment of the present invention, the controlled cell force analysis module 8 specifically includes:

[0195] The first repulsive force parameter calculation unit is used to construct the boundary repulsive force potential field U of the solution environment where the controlled cell is located according to the position information. repa , and according to the solution environment boundary repulsive potential field U repa , obtain the repulsive force parameter F generated by the solution environment boundary on the controlled cell repa (q i );in,

[0196]

[0197]

[0198] Where η represents the repulsion coefficient, ρ a represents the distance between the controlled cell and the boundary node, q i represents the current position of the controlled cell, ρ0 represents the distance of the controlled cell affected by the exclusion of the boundary node, is the gradient operation symbol;

[0199] The second repulsion parameter calculation unit is used to construct the obstacle cell repulsion potential field U where the controlled cell is located according to the position information. repb , and according to the barrier cell repulsion potential field U repb, obtain the repulsive force parameter F generated by the obstacle cell on the controlled cell repb (q i );in,

[0200]

[0201]

[0202] Where η represents the repulsion coefficient, ρ b represents the distance between the controlled cell and the obstacle cell, q i represents the current position of the controlled cell, ρ0 represents the distance that the controlled cell is affected by the repulsion of the obstacle cell, is the gradient operation symbol;

[0203] The third repulsion parameter calculation unit is used to construct the repulsion potential field U of other controlled cells except the controlled cell itself according to the position information. repc , and according to the repulsive potential field U of other controlled cells except itself repc , obtain the repulsive force parameter F generated by other controlled cells on the controlled cell repc (q i );in,

[0204]

[0205]

[0206] Where η represents the repulsion coefficient, ρ c Represents the distance between the controlled cell and other controlled cells, q i represents the current position of the controlled cell, ρ0 represents the distance that the controlled cell is affected by the repulsion of other controlled cells, is the gradient operation symbol;

[0207] The total repulsion parameter calculation unit is used to generate the repulsion parameter F generated by the solution environment boundary of the controlled cell. repa (q i ), the repulsive force parameter F generated by the obstacle cell to which the controlled cell is subjected repb (q i ) and the repulsive force parameter F generated by other controlled cells on the controlled cell repc (q i ), obtain the total repulsive force parameter F of the controlled cell rep (q i ); wherein, the calculation model of the total repulsive force parameter of the controlled cell is:

[0208]

[0209] Where, represents the total repulsive force parameter exerted by the boundary node on the i-th controlled cell, represents the total repulsive force parameter exerted by the obstacle cell on the i-th controlled cell, represents the total repulsive force parameter exerted by other controlled cells on the i-th controlled cell.

[0210] Based on the above embodiment, in an optional embodiment of the present invention, the controlled cell desired position module 9 specifically includes:

[0211] The control input calculation unit is used to obtain the control input γ of the controlled cell based on the desired formation, the next desired position of the virtual cell and the repulsive potential field. i Among them, the calculation model of the control input is:

[0212]

[0213] Where, γ i represents the consistency control input of the i-th controlled cell, ω is a constant, n is the total number of cells in the desired formation, a ij is the boundary weight in the adjacency matrix A(G) of the directed topological graph, q j is the current position of the jth controlled cell. When j = n, q n represents the expected position of the virtual cell at the next moment, q i represents the current position of the i-th controlled cell, d ij is the relative position between the i-th controlled cell and the j-th controlled cell.

[0214] The controlled cell expected position unit is used to obtain the next moment expected position of the controlled cell according to the control input of the controlled cell and the total repulsive force parameter of the controlled cell. The calculation model of the next moment expected position of the controlled cell is:

[0215]

[0216] Where q i d (t) represents the expected position of the i-th controlled cell at the next moment, q i (t) represents the current position of the i-th controlled cell, σ i represents the control input of the i-th controlled cell, Δt represents the sampling time, γ i represents the consistency control input of the i-th controlled cell, F rep (q i ) represents the total repulsive force parameter of the controlled cell.

[0217] Based on the above embodiment, in an optional embodiment of the present invention, the control module 10 specifically includes:

[0218] The difference unit is used to obtain the difference between the expected position of the controlled cell at the next moment and the current position.

[0219] The moving unit is used to input the difference into the fuzzy cerebellar neural network to obtain the optical trap position of the holographic optical tweezers so as to move the controlled cell.

[0220] Example 3:

[0221] An embodiment of the present invention provides a consistency control device for holographic optical tweezers, comprising a processor, a memory, and a computer program stored in the memory. The computer program can be executed by the processor to implement the consistency control method for holographic optical tweezers described in any paragraph of the first embodiment.

[0222] Example 4:

[0223] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the consistency control method of holographic optical tweezers as described in any paragraph of Example 1.

[0224] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or can be implemented using a combination of dedicated hardware and computer instructions.

[0225] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0226] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk. It should be noted that, in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without further constraints, an element defined by the phrase "comprises a..." does not preclude the existence of additional identical elements in the process, method, article or apparatus that includes the element.

[0227] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0228] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0229] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0230] The "first" and "second" mentioned in the embodiments are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0231] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for controlling consistency of holographic optical tweezers, characterized in that: Include: Obtaining position information of at least two controlled cells, obstacle cells, and environmental boundaries, as well as a target position; Based on the position information, a desired formation including virtual cells and controlled cells is constructed using a virtual leader method; wherein the desired formation includes a desired relative distance between cells; Update the position information and execute the following steps until the controlled cell moves to the target position; constructing a gravitational potential field and a repulsive potential field where the virtual cell is located according to the target position and the position information; and obtaining gravitational parameters and repulsive parameters of the virtual cell according to the gravitational potential field and the repulsive potential field where the virtual cell is located; Obtaining the expected position of the virtual cell at the next moment according to the attraction parameter and the repulsion parameter; constructing a repulsive potential field where the controlled cell is located according to the position information; and obtaining a total repulsive force parameter on the controlled cell according to the repulsive potential field where the controlled cell is located; Obtaining the expected position of the controlled cell at the next moment according to the total repulsive force parameter of the controlled cell, the expected formation, and the expected position of the virtual cell at the next moment; According to the expected position of the controlled cell at the next moment, the light trap position of the holographic optical tweezers is updated by fuzzy cerebellar neural network until the controlled cell moves to the expected position.

2. The consistency control method of holographic optical tweezers according to claim 1, characterized in that: Based on the position information, a desired formation including virtual cells and controlled cells is constructed using a virtual leader method; wherein the desired formation includes a desired relative distance between cells, specifically including: creating a virtual cell according to the position information; According to the virtual cells, the controlled cells are regarded as followers of the virtual cells using a virtual leader method, and a desired relative distance between each controlled cell and the virtual cells is set to obtain a desired formation; A directed topological graph is constructed according to the desired formation.

3. The consistency control method of holographic optical tweezers according to claim 1, characterized in that: According to the target position and the position information, a gravitational potential field and a repulsive potential field of the virtual cell are constructed, and according to the gravitational potential field and the repulsive potential field of the virtual cell, gravitational parameters and repulsive parameters of the virtual cell are obtained, wherein the method also includes: Obtaining the distance between the virtual cell and the target position, and determining whether the distance is greater than a maximum moving distance threshold; When the distance is greater than the maximum moving distance threshold, an intermediate position is set according to the maximum moving distance threshold, so as to execute subsequent steps to first move the controlled cell to the intermediate position.

4. The method for controlling consistency of holographic optical tweezers according to claim 1, wherein: Constructing the gravitational potential field and the repulsive potential field of the virtual cell according to the target position and the position information, and obtaining the gravitational parameters and the repulsive parameters of the virtual cell according to the gravitational potential field and the repulsive potential field, specifically including: According to the target position and the position information, the gravitational potential field U of the virtual cell is constructed. att (q n );in, Where U att (q n ) represents the gravitational potential field of the target position on the virtual cell, k represents the gravitational coefficient, q g represents the target position, q n It represents the current position of the virtual cell; According to the gravitational potential field, the gravitational parameter F of the virtual cell is obtained. att (q n ),in, Where, F att Indicates the gravitational parameter of the target position on the virtual cell, is the gradient operator symbol, U att (q n ) represents the gravitational potential field of the target position on the virtual cell, k represents the gravitational coefficient, q g represents the target position, q n It represents the current position of the virtual cell; According to the position information, the repulsive potential field U of the virtual cell is constructed. rep , and the solution environment boundary repulsive potential field U repa ;in, Where U rep represents the repulsive potential field where the virtual cell is located, U repa represents the repulsive potential field at the boundary of the solution environment, η represents the repulsive coefficient, ρ a represents the distance between the cell and the boundary node, q i represents the current position of the virtual cell, ρ0 represents the distance of the virtual cell affected by the exclusion of the boundary node; According to the repulsive potential field, the repulsive force parameter F of the virtual cell is obtained. rep (q n );in, Where, F repai (q n ) represents the repulsive force parameter of the boundary node on the virtual cell, η represents the repulsive force coefficient, ρ a represents the distance between the cell and the boundary node, q n represents the current position of the virtual cell, and ρ0 represents the distance at which the virtual cell is affected by the repulsion of the boundary nodes.

5. The method for controlling consistency of holographic optical tweezers according to claim 1, wherein: The calculation model of the expected position of the virtual cell at the next moment is: Where q n d (t) represents the expected position of the virtual cell at the next moment, q n (t) represents the current position of the virtual cell, δ represents the control input of the virtual cell, Δt represents the sampling time, F att (q n ) represents the gravitational parameter of the virtual cell, F rep (q n ) represents the repulsive force parameter of the virtual cell.

6. The method for controlling consistency of holographic optical tweezers according to claim 1, wherein: Constructing a repulsive potential field where the controlled cell is located according to the position information; and obtaining a total repulsive force parameter of the controlled cell according to the repulsive potential field where the controlled cell is located, specifically comprising: According to the position information, the boundary repulsive potential field U of the solution environment where the controlled cell is located is constructed. repa , and according to the solution environment boundary repulsive potential field U repa , obtain the repulsive force parameter F generated by the solution environment boundary on the controlled cell repa (q i );in, Where η represents the repulsion coefficient, ρ a represents the distance between the controlled cell and the boundary node, q i represents the current position of the controlled cell, ρ0 represents the distance of the controlled cell affected by the exclusion of the boundary node, is the gradient operation symbol; According to the position information, the obstacle cell repulsive potential field U where the controlled cell is located is constructed repb , and according to the barrier cell repulsion potential field U repb , obtain the repulsive force parameter F generated by the obstacle cell on the controlled cell repb (q i );in, Where η represents the repulsion coefficient, ρ b represents the distance between the controlled cell and the obstacle cell, q i represents the current position of the controlled cell, ρ0 represents the distance that the controlled cell is affected by the repulsion of the obstacle cell, is the gradient operation symbol; According to the position information, the repulsive potential field U of other controlled cells except the controlled cell itself is constructed. repc , and according to the repulsive potential field U of other controlled cells except itself repc , obtain the repulsive force parameter F generated by other controlled cells on the controlled cell repc (q i );in, Where η represents the repulsion coefficient, ρ c Represents the distance between the controlled cell and other controlled cells, q i represents the current position of the controlled cell, ρ0 represents the distance that the controlled cell is affected by the repulsion of other controlled cells, is the gradient operation symbol; The repulsive force parameter F generated by the solution environment boundary of the controlled cell repa (q i ), the repulsive force parameter F generated by the obstacle cell to which the controlled cell is subjected repb (q i ) and the repulsive force parameter F generated by other controlled cells on the controlled cell repc (q i ), obtain the total repulsive force parameter F of the controlled cell rep (q i ); wherein, the calculation model of the total repulsive force parameter of the controlled cell is: Where, represents the total repulsive force parameter exerted by the boundary node on the i-th controlled cell, represents the total repulsive force parameter exerted by the obstacle cell on the i-th controlled cell, represents the total repulsive force parameter exerted by other controlled cells on the i-th controlled cell; Obtaining the expected position of the controlled cell at the next moment according to the total repulsive force parameter of the controlled cell, the expected formation, and the expected position of the virtual cell at the next moment, specifically comprising: According to the desired formation and the next moment desired position of the virtual cell, the control input γ of the controlled cell is obtained. i ; Among them, the calculation model of the control input is: Where, γ i represents the consistency control input of the i-th controlled cell, ω is a constant, n is the total number of cells in the desired formation, a ij is the boundary weight in the adjacency matrix A(G) of the directed topological graph, q j is the current position of the jth controlled cell. When j = n, q n represents the expected position of the virtual cell at the next moment, q i represents the current position of the i-th controlled cell, d ij is the relative position between the i-th controlled cell and the j-th controlled cell; The expected position of the controlled cell at the next moment is obtained according to the control input of the controlled cell and the total repulsive force parameter of the controlled cell. The calculation model of the expected position of the controlled cell at the next moment is: Where q i d (t) represents the expected position of the i-th controlled cell at the next moment, q i (t) represents the current position of the i-th controlled cell, σ i represents the control input of the i-th controlled cell, Δt represents the sampling time, γ i represents the consistency control input of the i-th controlled cell, F rep (q i ) represents the total repulsive force parameter of the controlled cell.

7. The method for controlling consistency of holographic optical tweezers according to any one of claims 1 to 6, characterized in that: According to the next desired position of the controlled cell, the optical trap position of the holographic optical tweezers is updated by fuzzy cerebellar neural network until the controlled cell moves to the desired position, specifically including: Obtaining the difference between the expected position of the controlled cell at the next moment and the current position; The difference is input into the fuzzy cerebellar neural network to obtain the light trap position of the holographic optical tweezers to move the controlled cell.

8. A consistency control device for holographic optical tweezers, characterized in that: Include: An initial information acquisition module, used to obtain position information of at least two controlled cells, obstacle cells, and environmental boundaries, as well as a target position; An expected formation construction module is used to construct an expected formation including virtual cells and controlled cells using a virtual leader method according to the position information; wherein the expected formation includes an expected relative distance between cells; The loop module is used to update the position information and execute the following modules until the controlled cell moves to the target position; a virtual cell force analysis module, configured to construct an attractive potential field and a repulsive potential field in which the virtual cell is located based on the target position and the position information; and to obtain attractive force parameters and repulsive force parameters exerted on the virtual cell based on the attractive potential field and the repulsive potential field in which the virtual cell is located; A virtual cell expected position module, configured to obtain the expected position of the virtual cell at the next moment according to the attraction parameter and the repulsion parameter; A controlled cell force analysis module, configured to obtain the next moment expected position of the controlled cell according to the total repulsive force parameter of the controlled cell, the expected formation, and the next moment expected position of the virtual cell; A controlled cell expected position module, configured to obtain the next moment expected position of the controlled cell according to the total repulsive force parameter on the controlled cell, the expected formation, and the next moment expected position of the virtual cell; The control module is used to update the light trap position of the holographic optical tweezers according to the next expected position of the controlled cell by fuzzy cerebellar neural network until the controlled cell moves to the expected position.

9. A consistency control device for holographic optical tweezers, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory; the computer program can be executed by the processor to implement the consistency control method of the holographic optical tweezers according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the consistency control method for holographic optical tweezers according to any one of claims 1 to 7.

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