Nano-robot cluster control method based on induction star chain

Abstract

The invention relates to the field of nanometer materials, and provides a nanometer robot cluster control method based on an induction star chain, which comprises the following steps: S1, pre-configuring a composite induction field controlled by a nanometer robot cluster; wherein the composite induction field drives the nano-robot cluster to be assembled into a star chain structure through a coarse-grained model and an active particle environment; s2, according to the star chain structure, kinetic parameters of the nano-robot cluster are determined; s3, according to the kinetic parameters, when a control instruction is received, regulation and control parameters of active particles in the composite induction field are generated; and S4, according to the regulation and control parameters, controlling the star chain structure to execute a target operation corresponding to the operation instruction, and after the target operation is completed, cancelling the composite induction field.

Description

Technical Field

[0001] The present invention relates to the technical field of nanomaterials, and particularly relates to a method for manipulating a nanorobot cluster based on an induced star chain. Background Art

[0002] In the prior art, nanorobots are mainly directly driven by an external physical field (such as a magnetic field or a light field), and the nanorobots are guided to move by applying a normalized control signal. However, such methods can perform basic motion control in a simple environment, but there are many deficiencies in a complex living body:

[0003] Firstly: A single driving mode has difficulties in migration in a dynamically changing biological environment, and is prone to trajectory deviation and serious energy dissipation, for example: shear force gradient in blood vessels, tissue viscosity heterogeneity.

[0004] Secondly, in the cooperative mechanism of a multi-robot cluster, the interaction modes between units cannot be adjusted based on real-time environmental changes, and it is easy to cause instability of group movement due to local disturbances.

[0005] In the existing control of nanorobot clusters, their conformational regulation depends on passively responding to external stimuli and cannot be deformed directionally through topological engineering. It shows limited dynamic adaptability in an active particle bath. When the active force increases, the traditional linear chain only undergoes disordered stretching and it is difficult to form a stable superdiffusive motion mode. For example: CN202211164823.9, a method and system for multi-modal behavior regulation of a micro-nanorobot cluster based on a magnetic field, and the example patent is a single driving method of a magnetic field and a generated linear chain is also formed. Summary of the Invention

[0006] The present application proposes a method for manipulating a nanorobot cluster based on an induced star chain. Through the cooperation of the non-equilibrium driving of active particles and the topological dependence of the star chain, the manipulation accuracy and environmental adaptability of the nanorobot cluster are significantly improved. The directional energy gradient design of the composite induction field overcomes the randomness limitation of Brownian motion in a traditional thermal bath environment, enabling the nanorobots to achieve rapid self-assembly with low energy consumption; the kinetic parameter regulation based on dynamic scheduling can respond to external instructions in real time and adaptively switch motion modes, the superdiffusive state and the normal diffusive state, suitable for multi-task requirements in a complex biological environment. In addition, the phase transition mechanism driven by active particles endows the system with reversible reconstruction ability. Through precise control of the critical active force, it can be flexibly switched between destroying phase separation and promoting self-assembly, providing a highly robust solution for targeted drug delivery or dynamic repair of micro-nano devices.

[0007] In a first aspect, the present application proposes a method for manipulating a nanorobot cluster based on an induced star chain, including:

[0008] Step S1: Pre-configure a composite induction field for controlling a nano-robot cluster; wherein, the composite induction field drives the nano-robot cluster to assemble into a star-chain structure through a coarse-grained model and an active particle environment;

[0009] Step S2: Determine the kinetic parameters of the nano-robot cluster according to the star-chain structure;

[0010] Step S3: Generate regulation parameters of active particles in the composite induction field when receiving a manipulation instruction according to the kinetic parameters;

[0011] Step S4: Control the star-chain structure to perform a target operation corresponding to the operation instruction according to the regulation parameters, and after completing the target operation, cancel the composite induction field.

[0012] In this application, a composite induction field with induced manipulation behavior is constructed through a coarse-grained model and an active particle environment. Then, through the composite induction field, the connection of the nano-robot cluster is controlled to form a star-chain structure. Then, according to the kinetic parameters of the nano-robot cluster with a star-chain structure in the composite induction field, only by controlling the environmental change in the composite induction field, the corresponding nano-robot cluster will, according to the driving force and path change under the environmental change in the composite induction field, through the star-chain structure, realize driving the nano-robot cluster to move in multiple directions and perform tasks with multiple branches simultaneously. During the movement, the stability of the overall cluster can also be ensured, and the problems of energy dissipation and decreased biocompatibility of nano-robots under the same field for a long time are solved. The kinetic model of the nano-robot cluster is determined through a coarse-grained model, and then based on the active particle environment combined with the kinetic model, the energy dissipation is enhanced. Because there is a kinetic model, in terms of structure control, part of the decrease in biocompatibility can be compensated. The reduction of the decrease in biocompatibility can in turn be reflected in structure control to reduce energy dissipation.

[0013] Combined with the first aspect, the composite induction field is configured with active particle bath environment parameters of active particles and star-chain structure parameters based on a coarse-grained model;

[0014] Among them, the active particle bath environment parameters include active particle strength parameters and particle density parameters;

[0015] The star-chain structure parameters based on the coarse-grained model include: the number of branch chains, the fluctuation range of the branch chain bond length, and the branch chain bond angle. The branch chain is connected to a unique centroid node;

[0016] The spatial gradient distribution of the magnetic field component and the optical field component in the composite induction field and the ratio of the end-to-end distance of the branch chain to the gyration radius are within the stability threshold of the star-chain structure.

[0017] The composite induction field of the present application is set by the interaction of the environmental parameters of the active particle bath and the star-chain structure parameters to solve the problems of the stability of the star-chain structure and sufficient assembly driving force, prevent the random assembly of nanorobots, and prevent the deformation of the star-chain by correlating the spatial gradient of the magnetic field / optical field with the stability threshold of the structure size.

[0018] Combined with the first aspect, step S2 further includes:

[0019] According to the star-chain structure, detect the periodic oscillation of the mean square displacement of the branches on the star-chain relative to the centroid node, and construct the first mapping relationship between the oscillation phase and the active particles; wherein, the oscillation parameters of the periodic oscillation of the mean square displacement include the oscillation frequency and the oscillation amplitude.

[0020] According to the first mapping relationship, determine the low-activity area and the high-activity area of the active particles; wherein, under the synchronous enhancement of the oscillation phase, the spatial gradient distribution values of the magnetic field component and the optical field component change synchronously in the low-activity area and the high-activity area.

[0021] The present application controls the magnetic field / optical field of the composite induction field to be dynamically adjusted in phase synchronization in terms of spatial gradient distribution through the low-activity area and the high-activity area of the active particles, so that the nanorobot cluster with a star-chain structure can also move in multiple directions.

[0022] Combined with the first aspect, the kinetic parameters include the gyration radius, the centroid mobility, and the branch extension.

[0023] The present application can match the overall motion and the local structure in the regulation of the composite induction field through the kinetic parameters.

[0024] Combined with the first aspect, the operation instructions include branch addition and deletion instructions, bond distance adjustment instructions for the centroid node and the branches, and star-chain migration rate instructions.

[0025] In terms of the operation instructions, the present application constitutes multiple operation sets through three types of instructions to achieve multi-dimensional collaborative control.

[0026] Combined with the first aspect, when receiving the star-chain migration rate instruction in step S3, it includes:

[0027] Based on the phase separation kinetics, configure the activity intensity threshold of the active particles in the composite induction field.

[0028] According to the activity intensity threshold, determine the critical optical field power and the critical magnetic field power density.

[0029] According to the dynamic adjustment of the long parameters of the critical optical field power and the critical magnetic field power density under the Granger causality test, determine the migration path deviation rate.

[0030] In the process of controlling the migration of the nano-robot cluster in this application, the accuracy is improved through phase separation dynamics, and the long parameters are dynamically adjusted under the Granger causality test by the critical optical field power and the critical magnetic field power density to achieve critical setting and prevent path deviation.

[0031] Combined with the first aspect, when receiving the star-chain migration rate instruction in step S3, it further includes:

[0032] According to the star-chain structure, configure the first driving mode and the second driving mode for each branch chain in the star chain; wherein, the first driving mode is used to control the real-time updated migration direction of the active particles, and the second driving mode is used to configure the migration direction of the active particles along the local tangent direction of the branch chain;

[0033] The proportion of the symmetry parameters of the first driving mode and the second driving mode in the branch chain and the centroid node dynamically determines the real-time driving mode.

[0034] In this application, through two different driving modes and the symmetry parameters of the branch chain and the centroid node, when realizing the overall direction control, the branch chain automatically adjusts to prevent path conflicts between the overall star-chain structure and the branch chain.

[0035] Combined with the first aspect, when receiving the bonding distance adjustment instruction for the centroid node and the branch chain in step S3, it includes:

[0036] Preset the rotation diffusion coefficient threshold of the active particles in advance; wherein, the rotation diffusion coefficient threshold is used to determine the activation of the shear-induced stretching mode and to determine the activation of the collision-induced melting mode;

[0037] When the shear-induced stretching mode is activated, the composite induction field is configured with a hysteresis regulation area for alternately increasing and decreasing the optical field component, the rotation diffusion coefficient threshold matches the active particle density, and the bonding distance is controlled to be the target distance in the bonding distance adjustment instruction;

[0038] When the collision-induced melting mode is activated, the active particles linearly compensate for the diffusion pressure of the composite induction field.

[0039] In this application, the shear-induced stretching mode and the collision-induced melting mode are distinguished by the rotation diffusion coefficient threshold. These two bonding distance adjustment modes prevent the nano-robot cluster from being stretched too much and broken, and from being incompletely melted and sticking during the migration process.

[0040] Combined with the first aspect, the regulation parameters include magnetic field regulation parameters, optical field regulation parameters, thermodynamic parameters of the active particle bath, bonding dynamics parameters of the nano-robot cluster, and cooperative regulation parameters;

[0041] Among them, the magnetic field regulation parameters include the magnetic field strength range, the magnetic field gradient distribution, and the magnetic field frequency range;

[0042] Optical field regulation parameters: optical field wavelength range, optical field power density, spot positioning accuracy;

[0043] Thermodynamic parameters of the active particle bath: refer to the active force intensity, particle density, and rotational diffusion coefficient of the active particles;

[0044] Bonding kinetic parameters of the nanorobot cluster include bonding temperature threshold, hydrophilic-hydrophobic switching threshold;

[0045] Cooperative regulation parameters include magneto-optical coupling factor and phase separation critical ratio.

[0046] Through the coupling relationship control of various parameters such as magnetic field regulation parameters, optical field regulation parameters, thermodynamic parameters of the active particle bath, bonding kinetic parameters of the nanorobot cluster, and cooperative regulation parameters, this application can prevent drive mismatch, structural instability, and multi-field interference.

[0047] Combined with the first aspect, the target operations corresponding to the execution of the operation instructions in step S4 include:

[0048] Deploy a flexible boundary ratchet array in the star chain structure according to the regulation parameters;

[0049] Construct a pressure gradient and torque conversion model according to the flexible boundary ratchet array;

[0050] Manipulate the nanorobot cluster to synchronously rotate through the flexible boundary ratchet array according to the pressure gradient and torque conversion model to generate laminar flow drive and move directionally.

[0051] By setting a flexible boundary ratchet array, this application can convert the synchronous movement of the nanorobot cluster into laminar flow drive, adapt to the current environment, and perform directional movement.

[0052] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will become obvious from the specification, or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification and the drawings.

[0053] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0054] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention.

[0055] In the drawings:

[0056] Figure 1It is the flowchart of a method for controlling a nano-robot cluster based on an induced star chain in an embodiment of the present invention;

[0057] Figure 2 It is the configuration diagram of a composite induction field in an embodiment of the present invention;

[0058] Figure 3 It is the mapping relationship diagram of the star chain structure in an embodiment of the present invention;

[0059] Figure 4 It is the composition diagram of kinetic parameters in an embodiment of the present invention;

[0060] Figure 5 It is the type diagram of operation instructions in an embodiment of the present invention;

[0061] Figure 6 It is the calculation process diagram of the migration rate of the star chain in an embodiment of the present invention;

[0062] Figure 7 It is the drive mode setting diagram in an embodiment of the present invention;

[0063] Figure 8 It is the implementation diagram of the bonding distance adjustment instruction in an embodiment of the present invention;

[0064] Figure 9 It is the scatter diagram of the control parameters in an embodiment of the present invention. Detailed implementation manners

[0065] The following is a description of the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0066] The nano-robot control technology is mainly applied in the medical field, for example, in the delivery of targeted drugs and during microsurgery. This application combines chemical drive and external field drive to achieve a high-precision nano-robot cluster control method.

[0067] In the traditional nano-robot cluster control method, during the external field drive process, the magnetic field magnitude and pole of the magnetic field units arranged in a matrix are controlled to be the same, and the external field driving force is changed. Through the change of the external field driving force, mainly the magnetic force, the nano-robots are guided to move towards the target area. There is also a method of using the photovoltaic cell reaction to drive the movement of nano-robots. In this process, the nano-robots are multi-dimensionally controlled by adjusting the direction, wavelength, and battery of light, so that the nano-robots can combine responses in different wavebands.

[0068] Embodiment 1:

[0069] Refer to Figure 1 This application proposes a method for controlling a nano-robot cluster based on an induced star chain. In the specific implementation process:

[0070] In step S1: A composite induction field for controlling the nanorobot cluster is pre-configured; wherein, the composite induction field drives the nanorobot cluster to assemble into a star-chain structure through a coarse-grained model and an active particle environment; the coarse-grained model simplifies the molecular dynamics of the nanorobots and environmental particles, ignores atomic-level details, retains the particle centroids, defines the repulsive and attractive potential functions between particles according to the size of the nanorobots, and generates the coarse-grained model, which can reduce the computational complexity during the assembly process of the nanorobot cluster. The active particle environment combines the self-driving ability of the nanorobots and the external driving ability, and through light particles, magnetic particles, and chemically driven particles, realizes dynamic driving and guidance control. Its main function is to be able to control the nanorobots to achieve automatic assembly. The finally generated star-chain structure contains multiple branch chains, and the length of the branch chains is adjustable. In actual implementation, the central node of the star-chain structure integrates a nano-scale microcontroller, and the branch nodes are composed of carbon nanotubes, connecting molecular motors and drug loading bins, and can be applied to transporting drugs in medical scenarios. The coarse-grained model is simulated by a server terminal device for the multi-modal generator of the active particle environment. The multi-modal generator has a magnetic field module and a light driving module, constituting an alternating magnetic field for driving the nanorobot cluster.

[0071] In step S2, according to the star-chain structure, the dynamic parameters of the nanorobot cluster are determined; the dynamic parameters are the parameters of the motion characteristics of the star-chain, including the parameters of the central node and the branch nodes, such as the relative velocity, the interaction force between nodes, the inertia of the whole cluster, and the response delay.

[0072] Step S3: According to the dynamic parameters, when a manipulation instruction is received, the regulation parameters of the active particles in the composite induction field are generated; the regulation parameter generation mechanism in step S3 utilizes the spatial asymmetry of the active particle driving force. When a manipulation instruction is received, by real-time analyzing the conformational transition path required for the target operation, such as stretching, shrinking, or rotating, the direction pattern of the active force is dynamically adjusted, along the contour tangent direction or randomly distributed and intensity gradient. For example, when rapid directional movement is required, a tangent direction driving mode is adopted to enhance the cooperative propulsion force of the branch chains; while when structural stability is required, local stress concentration is suppressed through random direction driving. The regulation parameters are used by the active particles themselves to control the magnetic field intensity in the composite induction field in real time and drive the directional frequency change of the nanorobot cluster, so as to achieve precise manipulation of the field control parameters.

[0073] Step S4: According to the regulation parameters, control the star-chain structure to execute the target operation corresponding to the operation instruction, and after completing the target operation, cancel the composite induction field. The execution operation control of Step S4 depends on the phase transition coupling effect between the active particles and the star-chain. By adjusting the active particle density and the driving force threshold, trigger the rigid-flexible transition of the branched chains, enabling the star-chain to undergo controllable conformational reconstruction. For example, increasing the active force to the critical value can disrupt the equilibrium phase separation of the multi-chain system, and use the local pressure gradient induced by the active particles to drive the nanorobot cluster to complete grasping or releasing operations. After the operation is completed, cancel the composite induction field to return the system to the thermal equilibrium state, and restore the initial configuration through the relaxation process to achieve the reversibility of the operation. During the final cancellation process, the residual field strength after canceling the composite induction field can be monitored through a magnetic field detector to prevent the uncontrolled automatic movement of the nanorobot cluster.

[0074] Since nanorobots have no kinetic parameters when there is no structure, in this application, the nanorobot cluster is first assembled into a star-chain structure; obtain the kinetic parameters, and then use the star-chain structure to provide the potential function for optimizing the coarse-grained model of the composite induction field, and determine the regulation parameters corresponding to the control instructions. In actual implementation, the regulation effect can also verify the accuracy of the kinetic parameters, that is, the completion degree of the operation instruction.

[0075] Embodiment 2:

[0076] Refer to Figure 2 , the composite induction field of this application is configured with the active particle bath environment parameters of the active particles and the star-chain structure parameters based on the coarse-grained model; there is a dynamic equilibrium mechanism between the active particle bath and the star-chain structure in this application. By quantifying the key parameters in the composite induction field and adjusting and controlling the key parameters, that is, changing the particle density or intensity in the active particle bath environment, combined with the number of branched chains, bond length fluctuations, bond angles, and center of mass of the star-chain structure, control the composite induction field to drive the star-chain structure to move according to the control instructions.

[0077] Among the active particle bath environment parameters, the active force intensity controls the strength of the non-equilibrium driving force:

[0078] At low intensity, the active particles generate a thermal noise-like effect through random collisions, driving the weak fluctuations of the nanorobot;

[0079] At high intensity, the directional driving force dominates, and the generated shear stress field can break through the steric hindrance of the branched chains.

[0080] The particle density parameter can control the collision frequency of the active particles and adjust the assembly efficiency. At low density, the branched arms of the star-chain are loose due to insufficient particle capture, and at high density, the aggregation of active particles causes a local pressure gradient, controlling the contraction of the branched chains to maintain the topological stability of the center-of-mass node.

[0081] Among them, the environmental parameters of the active particle bath include the active particle intensity parameter and the particle density parameter;

[0082] The structural parameters of the star chain based on the coarse-grained model include: the number of branched chains, the fluctuation range of the branched chain bond length, and the branched chain bond angle. The branched chain is connected to a unique centroid node. In the structural parameters of the star chain based on the coarse-grained model, the branched chains suppress thermal fluctuations through the multi-arm synergistic effect: the more branched chains, the greater the steric hindrance between the arms, and a higher active force is required to trigger conformational changes; the fluctuation range of the branched chain bond length restricts the stretching elasticity of the branched arms through the bond potential energy function to avoid bond breakage caused by the impact of active particles; the branched chain bond angle combines with the angular potential to regulate the rigidity of the chain. The branched chain exhibits semi-rigid characteristics, and the bending energy competes with the active force to determine the cooperative motion mode of the branched arms. As the only connection point of the branched chains, the centroid node maintains the dynamic symmetry of the star topology through a concentrated energy dissipation path.

[0083] In the composite induction field of the present application, the spatial gradient distributions of the magnetic field component and the optical field component are within the stability threshold of the ratio of the end-to-end distance of the branched chain to the gyration radius in the star chain structure.

[0084] By designing the magnetic field and optical field components in the composite induction field through spatial gradients, the ratio of the end-to-end distance of the branched chain to the gyration radius is constrained to ensure structural stability. The magnetic field gradient can regulate the movement direction of active particles, and then make them arrange along the tangent direction of the branched arms, improving the driving force transmission efficiency; the optical field gradient adjusts the density distribution of active particles through the local thermal effect, and then suppresses the disordered fluctuations at the end of the branched chain. When exceeding the threshold, by automatically increasing the magnetic field gradient, compressing the stretching degree of the branched chain, or reducing the optical field intensity to reduce the particle aggregation pressure, the dynamic balance of the star chain in the non-equilibrium state is maintained.

[0085] In the present application, the active particle intensity parameter provides the basic driving force for the assembly of the star chain. Combining with the number of branched chains, it reduces the repulsive force between the branched chains.

[0086] Example 3:

[0087] Refer to Figure 3 , step S2, further includes:

[0088] In the present application, based on the star chain structure, the mean square displacement periodic oscillation of the branched chain on the star chain relative to the centroid node is detected, and the first mapping relationship between the oscillation phase and the active particles is constructed; among them, the oscillation parameters of the mean square displacement periodic oscillation include the oscillation frequency and the oscillation amplitude; that is, through the periodic characteristics of the mean square displacement oscillation, these parameters such as the frequency and the amplitude characterize the motion law of the branched chain of the star chain. The first mapping relationship is used to characterize the relationship between the oscillation phase and the distribution / driving ability of the active particles. When controlling the movement of the nanorobot cluster, it can be judged when to enhance and weaken the particle activity.

[0089] When this application detects the periodic oscillation of the mean square displacement between the branched chain and the centroid node, the oscillation frequency and amplitude are based on the non-equilibrium fluctuations caused by the collisions of active particles: If the driving direction of the active particles can match the motion phase of the branched chain, the collision energy will be converted into periodic deformation through the elastic potential energy between the arms; if the driving direction is misaligned with the phase, the energy dissipation will cause the amplitude to decay.

[0090] This application determines the low-activity region and high-activity region of active particles according to the first mapping relationship; among them, under the synchronous enhancement of the oscillation phase, the spatial gradient distribution values of the magnetic field component and the optical field component change synchronously.

[0091] This application divides the low-activity region and high-activity region depending on the energy transfer efficiency of the oscillation phase: If the oscillation phase is synchronously enhanced, the high-activity region will enhance the magnetic field gradient, align the active particles along the tangent direction of the branched chain, increase the optical field intensity to increase the local particle density, and generate a directional propulsion force;

[0092] While the low-activity region needs to reduce the magnetic field gradient and optical field intensity to suppress the disordered fluctuations at the end of the branched chain. The synchronous change of the magnetic field and optical field components will adjust the spatial gradient distribution value in real time, lock the stretching-shrinking period of the branched chain with the driving frequency of the active particles, and make the star-chain structure in a dynamically stable state.

[0093] This application solves the problem of poor structural stability in the traditional method, where the driving of active particles is not synchronized with the movement of the star-chain structure and the field gradient is statically set, from three sequential perspectives of sensing the dynamics of the star-chain, matching the driving, and optimizing the field distribution.

[0094] Example 4:

[0095] Refer to Figure 4 , the kinetic parameters include the gyration radius, the centroid mobility, and the degree of branched-chain extension.

[0096] The gyration radius of this application is determined based on the conformational fluctuation phenomenon caused by the collisions of active particles.

[0097] If the active force increases, the branched chain is sheared and stretched and increases. The corresponding centroid mobility is calculated by the time evolution slope of the mean square displacement of the centroid, and the translational diffusion ability of the overall star-chain structure is enhanced;

[0098] Specifically, the translational diffusion ability is jointly affected by the density of active particles and the number of branched chains. The degree of branched-chain extension will regulate the bending stiffness through the angular potential. If it deviates from the target range, it can be dynamically adjusted to maintain the coordinated movement of the branched chains.

[0099] The main purpose of this application is to quantify the kinetic characteristics in three dimensions of rotation, translation, and local structure to prevent the mismatch between the overall motion and the local structure.

[0100] Example 5:

[0101] Refer to Figure 5 , the operation instructions include branch chain addition and deletion instructions, centroid node and branch chain bond distance adjustment instructions, and star chain migration rate instructions.

[0102] When this application needs to add a branch chain, based on light field focusing, the density of active particles in the target area is increased, and a steric hindrance is formed by the repulsive force between particles, driving the self-assembly of nanorobots around the centroid node into a new branch chain;

[0103] If a branch chain needs to be deleted, by applying a high-frequency pulsed magnetic field perturbation, the bonding potential energy between the branch chain and the centroid node is destroyed, the local light intensity is reduced to induce the escape of active particles, and the corresponding branch chain dissociates. The branch chain addition and deletion instructions can adjust the drug loading amount to prevent the inability to pass through blood vessels.

[0104] When this application increases the bonding distance, the magnetic field gradient is increased, and then the active particles are arranged along the axial direction of the branch chain to generate a tensile force to constrain the bond length; when shortening the bonding distance, the thermal effect of the light field is enhanced, the Brownian motion of the nanorobot is enhanced, and the nanorobot relaxes towards the centroid node. When controlling the bonding distance adjustment and the corresponding branch chain stretches to release the drug, no structural fracture will occur.

[0105] In this application, the migration rate instruction relies on the intensity and density of active particles for non-linear regulation, thereby realizing the control of the migration rate. During the acceleration migration process, the hydrodynamic thrust generated by controlling the collective motion of active particles enhances the centroid migration rate; if the migration is decelerated, the reverse light field gradient can be reduced and introduced to offset the active driving force, and partial offset is achieved during actual implementation. The adjustment of the migration rate, corresponding to the change in the drug delivery speed, can prevent collisions.

[0106] Generally speaking, adjusting the chain length and bonding distance can mainly ensure the precise release of drugs and the passage through different types of blood vessels during the clinical implementation process of drug transportation.

[0107] Example 6:

[0108] Refer to Figure 6 , when the star chain migration rate instruction is received in step S3, it includes:

[0109] This application configures the activity intensity threshold of active particles in a composite induction field based on phase separation dynamics. This operation is to avoid the over-concentration of active particles, their concentration or dispersion under the driving force, improve the uniformity of the driving force, and ensure the structural stability of the overall star-shaped chain. Phase separation dynamics refers to the phase separation that occurs when active particles and nanorobot clusters interact through repulsion and attraction. In this application, the active particles in the composite induction field maintain the phase separation equilibrium through different concentration gradients around the nanorobot clusters, and the activity intensity threshold represents the minimum driving intensity of active particles.

[0110] That is, when receiving the migration rate instruction, first set the activity intensity threshold based on the phase separation critical condition in the multi-chain system of the star-shaped chain structure. Based on the activity intensity threshold, calculate the driving force relationship between the active particle driving energy and the phase separation free energy barrier, mainly a competitive relationship: thus, phase separation is inhibited, the activity intensity is reduced, and the structure disintegration is prevented; otherwise, the activity force needs to be enhanced to accelerate migration.

[0111] According to the activity intensity threshold, determine the critical optical field power and the critical magnetic field power density; in this application, the critical optical field power (the minimum light intensity to maintain the stable migration of the star-shaped chain) and the critical magnetic field power density (the minimum magnetic field energy density to maintain stable migration) function to optimize the energy transfer efficiency: when setting the critical optical field power, it satisfies the dynamic balance between the local heating rate of active particles and the heat dissipation rate of the system to prevent thermal damage; when setting the critical magnetic field power density, through the vector superposition of the Lorentz force and the activity force, ensure that the deviation angle between the particle movement direction and the migration path is small.

[0112] According to the dynamic adjustment of the long parameter under the Granger causality test of the critical optical field power and the critical magnetic field power density, determine the migration path deviation rate. Through time series analysis, extract the lag influence coefficients of the optical field intensity fluctuation and the magnetic field gradient change on the path deviation. When dynamically adjusting the parameter, preferentially regulate the field component with a higher causality weight to make the path deviation rate stable in the target interval. In this application, the Granger causality test is used to determine whether the field parameter is the Granger cause of the migration path deviation rate. The Granger causality test, also known as the Granger causality monitoring, is a statistical method used to determine whether there is a causal relationship between two time series variables. In this application, the migration path deviation rate is determined through the influence of the critical optical field power and the critical magnetic field power density on the path of the nanorobot cluster in the star-shaped chain structure under time series analysis.

[0113] The above embodiments focus on monitoring the deviation of the branch structure in a complex structure such as a star-chain structure of nano-robot clusters under the active particle concentration gradient. The deviation can be calculated from the branch to the overall star-chain structure, and then path optimization can be achieved. Traditional simple phase separation is only applicable to linear structures. In this embodiment, it is mainly the coupling condition of the optical field and the magnetic field, not a single scenario. In the specific implementation process, the critical field parameters are mainly determined through the driving force balance experiment of blood flow shear force, and the critical optical field power and critical magnetic field power density corresponding to the driving force that cancels the shear force are comprehensively determined.

[0114] Embodiment 7:

[0115] Refer to Figure 7 , when the star-chain migration rate instruction is received in step S3 of the present application, it further includes:

[0116] According to the star-chain structure, the present application configures the first driving mode and the second driving mode for each branch in the star-chain; wherein, the first driving mode is used to control the real-time updated migration direction of the active particles, and the second driving mode is used to configure the migration direction of the active particles along the local tangent direction of the branch; in the first driving mode, the driving direction of the active particles is synchronously updated with the global migration direction of the star-chain, preventing the path deviation of the star-chain structure from migrating, and it can migrate in a straight line or along a fixed route. In the second driving mode, the driving direction of the active particles is along the tangent direction of the end of the branch, preventing collision with the blood vessel wall, and the branch can curl and shrink.

[0117] In actual implementation, when the migration rate instruction is received, based on the symmetry parameter between the branch and the centroid node, the proportion weights of the first driving mode and the second driving mode are dynamically allocated. The first driving mode is through the rapid redirection of the optical field / magnetic field, and the corresponding movement direction of the active particles is matched with the external environmental resistance field in real time.

[0118] According to the proportion of the first driving mode and the second driving mode in the symmetry parameter between the branch and the centroid node, the real-time driving mode is dynamically determined. The symmetry parameter represents the angular symmetry between the branch and the centroid. Under the combination of the two modes, when the symmetry degree is high, the global mode is preferred, and when the symmetry degree is low, the local mode is preferred, preventing the asymmetric branch deviation in the global mode and the out-of-control deviation of the overall star-chain structure in the local mode.

[0119] Embodiment 8:

[0120] Refer to Figure 8 , when the bonding distance adjustment instruction of the centroid node and the branch is received in step S3, it includes:

[0121] Preset the threshold of the rotational diffusion coefficient of active particles; wherein, the threshold of the rotational diffusion coefficient is used to determine the activation of the shear-induced stretching mode and the activation of the collision-induced melting mode; when this application receives a bonding distance adjustment instruction, it sets the threshold according to the matching relationship between the active particle density and the rotational diffusion coefficient: the threshold of the rotational diffusion coefficient is the subtraction value of the rotational motion intensity corresponding to the diffusion rate of the active particles rotating around the centroid, defining when stretching is required and when melting is required. The shear-induced stretching mode characterizes the generation of shear force through the alternating increase and decrease of the light field, uniformly stretching the bonding distance to prevent chain breakage. During the precise control process, uneven stretching caused by different operation methods is avoided.

[0122] This application activates the shear-induced stretching mode at a low rotational diffusion coefficient. At this time, the direction persistence of the active particles is relatively high, and then based on the alternating increase and decrease of the light field component in the composite induction field, a hysteresis control region is formed around the centroid node.

[0123] Within this region, the alternating light field triggers periodic strong and weak collisions of the active particles, generating a shear stress gradient, driving the branched chain to gradually extend along the stretching direction until the bonding distance reaches the target value. Generally, the breakage of the star-shaped chain structure can be avoided during the precise control process.

[0124] When the shear-induced stretching mode is activated, through the hysteresis control region configured by the composite induction field, the light field component is alternately increased and decreased, and then the threshold of the rotational diffusion coefficient is matched with the active particle density to control the bonding distance to the target distance in the bonding distance adjustment instruction;

[0125] When the collision-induced melting mode is activated, the active particles linearly compensate the diffusion pressure of the composite induction field.

[0126] Due to the characteristics of the active particles, disordered collision energy accumulation occurs under rapid random turning. The collision-induced melting mode of this application cancels the local melting effect by linearly compensating the diffusion pressure: if it is detected that the potential energy fluctuation of the branched chain bonding exceeds the threshold, the magnetic field binding force is enhanced to convert the disordered collision energy into a directional heat diffusion flow, suppressing the risk of bonding breakage.

[0127] At the same time, the light field power is adjusted in the reverse direction according to the active particle density to prevent the structural collapse caused by local overheating. It can solve the technical problem that when the branched chain shortens, it cannot pass through the capillary due to adhesion in the drug delivery environment.

[0128] Example 9:

[0129] Refer to Figure 9 , the regulation parameters include magnetic field regulation parameters, light field regulation parameters, thermodynamic parameters of the active particle bath, bonding dynamics parameters of the nanorobot cluster, and cooperative regulation parameters;

[0130] Among them, the magnetic field regulation parameters include the magnetic field strength range, magnetic field gradient distribution, and magnetic field frequency range;

[0131] The optical field regulation parameters include the optical field wavelength range, optical field power density, and spot positioning accuracy;

[0132] The thermodynamic parameters of the active particle bath refer to the active force strength, particle density, and rotational diffusion coefficient of the active particles;

[0133] The bonding kinetic parameters of the nanorobot cluster include the bonding temperature threshold and the hydrophilic-hydrophobic switching threshold;

[0134] The cooperative regulation parameters include the magneto-optical coupling factor and the phase separation critical ratio.

[0135] In this application, for the regulation parameters, it is mainly for the cooperation of the magnetic field, optical field, thermodynamics, bonding kinetics, and the star-chain structure of the nanorobot integration to achieve precise control migration, such as drug delivery. Therefore, in the magnetic field regulation parameters, the magnetic field strength range of this application constrains the movement direction of the active particles through the Lorentz force, and the gradient distribution is dynamically matched with the end-to-end distance of the star-chain branch to generate a directional tensile force;

[0136] The setting of the magnetic field frequency range of this application controls the movement inertia of the active particles through the relaxation effect of the alternating magnetic field and suppresses the instability of the star-chain structure under high-frequency oscillation. For the optical field regulation parameters of this application, the wavelength range matches the absorption spectrum of the photothermal conversion material of the nanorobot, and local energy focusing can be carried out;

[0137] The optical field power density of this application mainly controls the propulsion intensity of the active particles through the photophoretic force and the photothermal expansion effect, and the spot positioning accuracy uses the spatial light modulation technology to control the selective activation of the ends of the branches of the star-chain structure.

[0138] The thermodynamic parameters of the active particle bath of this application are based on the dynamic balance of the active force strength and the rotational diffusion coefficient to control the star-chain structure to execute the non-equilibrium driving mode:

[0139] At low Dr, the direction persistence of the active particles is enhanced, forming shear-induced stretching;

[0140] At high Dr, the collision melting effect is triggered.

[0141] The particle density cooperates with the spatial distribution entropy and the active force to determine the critical conditions for phase separation.

[0142] In the bonding kinetic parameters of this application, the bonding temperature threshold is based on the photothermal-triggered hydrophilic-hydrophobic switching to induce selective adhesion or dissociation of different nanorobots in the nanorobot cluster in the target area.

[0143] Among the co-regulation parameters of this application, the magneto-optical coupling factor quantifies the interaction effect between the magnetic field gradient and the optical field power, and controls the vector superposition effect of the tensile force and the thermal expansion stress;

[0144] The phase separation critical ratio controls the assembly stability threshold of the multi-chain system through the ratio of the Flory-Huggins parameter to the active force. The Flory-Huggins parameter is a key parameter used to quantify the interaction between polymers and solvents in the polymer solution theory. In this application, it is a key parameter characterizing the interaction between the nanorobot cluster and the composite induction field, indicating the strength of the interaction force.

[0145] This application solves the problem of adhesion during migration and failed drug delivery that may occur while enabling the star-shaped chain structure to migrate precisely in the blood by combining various different parameters in the regulation parameters.

[0146] Example 10:

[0147] Performing the target operation corresponding to the operation instruction in step S4 includes:

[0148] Deploying a flexible boundary ratchet array in the star-shaped chain structure according to the regulation parameters;

[0149] Constructing a pressure gradient and torque conversion model based on the flexible boundary ratchet array;

[0150] Controlling the nanorobot cluster to perform laminar flow drive and move directionally through the synchronous rotation of the flexible boundary ratchet array according to the pressure gradient and torque conversion model.

[0151] In this application, the flexible boundary ratchet array is an array composed of deformable micro-columns. The tips of the micro-columns are serrated and can deform with the fluid pressure. In this application, its deployment is based on the non-uniform pressure distribution of the active particle bath. By adjusting the magnetic field gradient and optical field power density in the regulation parameters, this application constructs a flexible boundary ratchet array with an asymmetric geometry based on the periodic curvature distribution at the end of the star-shaped chain branches. The flexible boundary ratchet array includes multiple ratchet units. The flexible boundary curvature of each ratchet unit matches the rotational diffusion coefficient of the active particles.

[0152] In this application, the pressure gradient and torque conversion model is based on the spatio-temporal correlation of active particle collisions. The pressure gradient is the rate of change of pressure with spatial position of active particles in the fluid, i.e., the composite induced field of this application. The torque conversion model is a microscopic calculation model for the rotational torque of the star chain: In this application, the spatio-temporal distribution of collision events on the ratchet surface is statistically analyzed, the net pressure gradient is calculated and mapped into an equivalent torque. If there are multiple ratchet units arranged at equal intervals along the circumferential direction of the star chain, their synchronous rotation generates a laminar shear field through hydrodynamic coupling, generating an overall propulsion force. The directional movement of the star chain structure is controlled by adjusting the magneto-optical coupling factor to perform the movement. By enhancing the constraint of the magnetic field on the ratchet arrangement and guiding the laminar flow direction through the optical field gradient at the same time, the nano-robot cluster moves along the preset path. Through the above steps, the problem of jamming caused by the irregularity of the blood vessel wall to the rigid structure can be solved when delivering drugs. The laminar flow driving method is used to handle the phenomenon of nano-robot deviation caused by blood flow interference.

[0153] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A method for controlling a nanorobot cluster based on an induced star chain, characterized in that Including: Step S1: Pre-configure a composite induction field for controlling a nano-robot cluster; wherein, the composite induction field drives the nano-robot cluster to assemble into a star-chain structure through a coarse-grained model and an active particle environment; Step S2: Determine the kinetic parameters of the nano-robot cluster according to the star-chain structure; Step S3: Generate regulation parameters of active particles in the composite induction field when receiving a manipulation instruction according to the kinetic parameters; Step S4: Control the star-chain structure to execute the target operation corresponding to the operation instruction according to the regulation parameters, and cancel the composite induction field after completing the target operation.

2. The method for controlling a nano-robot cluster based on an induced star chain according to claim 1, characterized in that, The composite induction field is configured with active particle bath environment parameters of active particles and star-chain structure parameters based on a coarse-grained model; Among them, the active particle bath environment parameters include an active particle intensity parameter and a particle density parameter; The star-chain structure parameters based on the coarse-grained model include: the number of branch chains, the fluctuation range of the branch-chain bond length, and the branch-chain bond angle, and a unique centroid node is connected to the branch chain; The spatial gradient distribution of the magnetic field component and the optical field component in the composite induction field and the ratio of the end-to-end distance of the branch chain to the gyration radius are within the stability threshold of the star-chain structure.

3. The method for controlling a nanorobot cluster based on an induced star chain according to claim 2, wherein, The step S2 further includes: Detect the periodic oscillation of the mean square displacement of the branch chain on the star chain relative to the centroid node according to the star-chain structure, and construct a first mapping relationship between the oscillation phase and the active particles; wherein, the oscillation parameters of the periodic oscillation of the mean square displacement include the oscillation frequency and the oscillation amplitude; Determine the low-activity area and the high-activity area of the active particles according to the first mapping relationship; wherein, under the synchronous enhancement of the oscillation phase, the spatial gradient distribution values of the magnetic field component and the optical field component change synchronously in the low-activity area and the high-activity area.

4. The method for controlling a nanorobot cluster based on an induced star chain according to claim 1, wherein, The kinetic parameters include the gyration radius, the centroid mobility, and the branch-chain extension.

5. A method for controlling a nanorobot cluster based on an induced star chain, as claimed in claim 1, wherein The operation instructions include branch-chain addition and deletion instructions, bond distance adjustment instructions for the centroid node and the branch chain, and star-chain migration rate instructions.

6. The method for controlling a nanorobot cluster based on an induced star chain according to claim 5, wherein, When receiving the star-chain migration rate instruction in the step S3, it includes: Configure the active intensity threshold of the active particles in the composite induction field based on phase separation kinetics; Determine the critical optical field power and the critical magnetic field power density according to the active intensity threshold; Determine the migration path deviation rate according to the dynamic adjustment long parameter of the critical optical field power and the critical magnetic field power density under the Granger causality test.

7. The method for controlling a nanorobot cluster based on an induced star chain according to claim 5, wherein When receiving the star-chain migration rate instruction in the step S3, it further includes: Configure the first driving mode and the second driving mode for each branch chain in the star chain according to the star-chain structure; wherein, the first driving mode is used to control the real-time updated migration direction of the active particles, and the second driving mode is used to configure the migration direction of the active particles along the local tangent direction of the branch chain; Dynamically determine the real-time driving mode according to the proportion of the symmetry parameters of the branch chain and the centroid node in the first driving mode and the second driving mode.

8. The method for controlling a nano-robot cluster based on an induced star chain according to claim 5, wherein, When receiving the bond distance adjustment instruction for the centroid node and the branch chain in the step S3, it includes: Preset the rotational diffusion coefficient threshold of the active particles; wherein, the rotational diffusion coefficient threshold is used to determine the activation of the shear-induced stretching mode and the activation of the collision-induced melting mode; When the shear-induced stretching mode is activated, the composite induction field is configured with a hysteresis regulation region for alternately increasing and decreasing the optical field components, matching the rotational diffusion coefficient threshold with the active particle density, and controlling the bonding distance to the target distance in the bonding distance adjustment command; When the collision-induced melting mode is activated, the active particles linearly compensate for the diffusion pressure of the composite induction field.

9. The method for controlling a nano-robot cluster based on an induced star chain according to claim 1, wherein, The regulation parameters include magnetic field regulation parameters, optical field regulation parameters, thermodynamic parameters of the active particle bath, bonding dynamics parameters of the nanorobot cluster, and cooperative regulation parameters; Among them, the magnetic field regulation parameters include the magnetic field strength range, magnetic field gradient distribution, and magnetic field frequency range; The optical field regulation parameters include the optical field wavelength range, optical field power density, and spot positioning accuracy; The thermodynamic parameters of the active particle bath refer to the active force intensity, particle density, and rotational diffusion coefficient of the active particles; The bonding dynamics parameters of the nanorobot cluster include the bonding temperature threshold and the hydrophilic-hydrophobic switching threshold; The cooperative regulation parameters include the magneto-optical coupling factor and the phase separation critical ratio.

10. The method for controlling a nanorobot cluster based on an induced star chain according to claim 1, characterized in that, The target operations corresponding to the execution of the operation instructions in step S4 include: Deploying a flexible boundary ratchet array in the star chain structure according to the regulation parameters; Constructing a pressure gradient and torque conversion model based on the flexible boundary ratchet array; Controlling the nanorobot cluster to rotate synchronously through the flexible boundary ratchet array according to the pressure gradient and torque conversion model to generate laminar driving and move directionally.

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