Biological robot based on deep learning model
Through the biological robot technology driven by deep learning model, efficient biological tissue construction and functional cultivation are achieved, solving the problems of insufficient vascularization, low tissue differentiation efficiency and poor interface compatibility in biological robot manufacturing, and improving the performance and reliability of the robot.
Patent Information
- Application Number
- CN202510616356.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-14
- Publication Date
- 2025-07-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
There are problems in the manufacturing of existing biological robots, such as insufficient vascularization, low tissue differentiation efficiency, poor interface compatibility and difficult model migration, resulting in tissue necrosis, uneven function, and failure of signal transmission.
The biological robot based on the deep learning model is adopted to achieve high survival rate biological tissue construction and low damage interface integration through vascularized 3D bioprinting, dynamic microfluidic directional differentiation, generative adversarial network optimization interface and transfer learning adaptation, combined with multimodal sensors, embedded deep learning processors, bionic drive devices and optogenetic stimulation modules and other technologies.
It significantly improves the performance and reliability of biological robots, improves the long-term survival rate and functional culture efficiency of biological tissues, reduces manufacturing costs and staff intervention needs, and solves the problems of tissue necrosis, functional inequality and signal transmission failure in traditional methods.
Smart Images
Figure CN120287302A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a bio-robot based on a deep learning model. Background Art
[0002] Currently, the manufacturing of bio-robots mainly relies on traditional tissue engineering methods, such as static culture and mechanical assembly. These methods face many challenges when constructing complex bio-electronic hybrid systems. The existing technologies usually adopt standardized culture conditions and immobilized interface designs, lacking means for adaptively regulating the dynamic characteristics of biological tissues, and the manufacturing process often requires manual intervention, making it difficult to achieve large-scale production.
[0003] Traditional 3D-printed bio-scaffolds lack functional vascular networks, resulting in necrosis of internal tissues due to insufficient nutrient supply, affecting the long-term stability of the robot. Static microfluidic culture is difficult to precisely control the cell microenvironment, leading to low tissue differentiation efficiency and non-uniform functions. Moreover, bio-electronic interfaces usually adopt empirical designs, and stress concentration at the interface easily causes tissue damage or signal transmission failure. At the same time, the training of the control model relies on a large amount of real bio-robot data, and there are significant differences between the existing simulation environment and the real biological system, resulting in poor model migration performance.
[0004] Therefore, in view of the problems of insufficient vascularization, low tissue differentiation efficiency, poor interface compatibility, and difficult model migration in the manufacturing of the above bio-robots, the present invention proposes a bio-robot based on a deep learning model. Through vascularized 3D bioprinting, dynamic microfluidic directed differentiation, generative adversarial network-assisted interface optimization, and transfer learning model adaptation, it realizes the construction of high-survival-rate biological tissues, efficient functionalized culture, low-damage interface integration, and cross-platform control capabilities, thereby significantly improving the performance and reliability of the bio-robot. Summary of the Invention
[0005] In order to overcome the problems of tissue damage and signal transmission failure caused by stress concentration at the bio-electronic interface in the manufacturing of existing bio-robots, the present invention proposes a bio-robot based on a deep learning model.
[0006] The technical solution of the present invention is as follows: A bio-robot based on a deep learning model, comprising:
[0007] A biological tissue carrier, which is composed of living cells, muscle tissue or nerve tissue, serves as the core execution unit of the robot, has biocompatibility and self-healing ability; is genetically engineered to respond to specific stimuli, uses a three-dimensional bio-scaffold to provide structural support, and integrates a microvascular network to maintain long-term survival;
[0008] A multimodal sensor array, including bionic-designed chemical sensors, optical sensors, and mechanical sensors, which can collect the pH value, temperature, and mechanical pressure in the environment in real time, and convert the analog signals into digital signals through a bio-electronic hybrid interface and input them into the processor;
[0009] An embedded deep learning processor, which uses a low-power neuromorphic chip, deploys a hybrid architecture of a lightweight convolutional neural network and a long short-term memory network, and supports online learning and adaptive inference; the model parameters are optimized through quantization training, so that it can still process the spatio-temporal features input by the sensors under resource-constrained conditions, and output control instructions to the drive module;
[0010] A bionic drive device: composed of a flexible electroactuator coupled with biological tissue, which converts the electrical signals sent by the processor into mechanical motion; among them, the electroactuator uses an ionic gel material to simulate muscle contraction, optimizes the strain transfer path through topological design, and integrates a strain feedback circuit to achieve closed-loop control.
[0011] Preferably, the deep learning model adopts a spiking neural network architecture, and its neuron model is based on the Leaky Integrate-and-Fire or Hodgkin-Huxley equation to simulate the dynamic characteristics of the membrane potential of biological neurons; the network processes the spatio-temporal spike trains input by the sensors through an event-driven asynchronous computing method, and the synaptic weights are adaptively adjusted using the STDP rule, enabling the system to have a biological-like real-time information processing ability.
[0012] Preferably, the training method of the neural network uses a reinforcement learning framework, and the framework adopts the PPO algorithm. The biofeedback signals are collected in real time through an implanted microelectrode array or fluorescence imaging technology; the reward function is designed as a multi-dimensional target combination, including motion efficiency, tissue health, and task completion. The output layer of the policy network is connected to the intensity-frequency parameters of the optogenetic regulation module to achieve the optimal matching between the control instructions and the biological system.
[0013] Preferably, the bio-robot further includes:
[0014] An optogenetic stimulation module, which converts the digital instructions output by the neural network into light pulses of a specific wavelength to precisely control the contraction / relaxation of transgenic biological tissue;
[0015] A microbial fuel cell, which dynamically adjusts the energy supply rhythm according to the metabolic demands predicted by deep learning;
[0016] A growth factor control module, and the neural network actively regulates the proliferation / apoptosis of biological tissue by analyzing long-term environmental data to achieve macroscopic morphological evolution;
[0017] The neural plasticity simulation module triggers the programmed death of biological tissues when the deep learning model detects a preset danger threshold.
[0018] Preferably, the optogenetic stimulation module integrates a micro-LED array with transgenic biological tissues, where the tissues express ChR2 or ArchT photosensitive proteins. The control instructions output by the deep learning model are converted into 470-nm blue light or 590-nm yellow light pulse sequences through a PWM circuit, with a light intensity resolution of 0.1 mW / mm 2 , and the stimulation frequency range of 1 - 100 Hz is programmable, achieving sub-millimeter spatial positioning accuracy through an optical fiber light guiding system.
[0019] Preferably, the microbial fuel cell uses the engineered strain Shewanella oneidensis MR-1 as the anode catalyst, whose extracellular electron transfer efficiency is increased by 3 times under the growth conditions optimized by deep learning. The cathode adopts a platinum / carbon-biofilm hybrid structure. The system integrates a coulomb counting chip and an LSTM prediction model, dynamically adjusts the flow rate of the nutrient solution to maintain the optimal power density, and switches to the sleep mode during the low-activity period to extend the continuous power supply time.
[0020] Preferably, the growth factor control module includes a microfluidic chip and VEGF / BMP-2 slow-release microspheres. The neural network decides the local growth factor release concentration by analyzing the movement trajectory, mechanical load, and metabolic data within a 30-day cycle; at the same time, it regulates the spatio-temporal distribution of apoptosis signals, causing the biological tissue to thicken by 5 - 20% in the stress concentration area and spontaneously degenerate in the non-essential area, realizing the adaptive remodeling of the macroscopic morphology.
[0021] Preferably, the neural plasticity simulation module uses a 100×100-scale memristor crossbar array at the hardware level, whose resistance change simulates synaptic strength. At the software level, it implements a reward and punishment mechanism similar to BDNF. When the action sequence leads to an increase in the task success rate, the Hebbian learning rate of the corresponding neural pathway automatically increases by 300%, while continuous failures trigger synaptic pruning, enabling the robot to have the ability to accumulate "experience" to avoid dangerous paths.
[0022] Preferably, multiple bio-robots exchange data through a molecular communication channel designed by synthetic biology. The molecular communication channel is based on the quorum sensing mechanism. Each robot secretes AHL molecules as information carriers, and the receiver detects the concentration gradient through the LuxR protein receptor; federated learning uses a model aggregation algorithm with differential privacy protection, synchronizing the environmental recognition model parameters of each node every 8 hours. After the global model is updated, the signal molecule synthesis genes of the local flora are reprogrammed through the CRISPR-dCas9 system to achieve the self-evolution of the communication protocol.
[0023] Preferably, the bio-robot is implanted with a programmable apoptosis gene switch, which is controlled by a temperature-sensitive promoter. When the deep learning model detects that the tissue necrosis index exceeds the threshold or the task times out, it triggers local heating at 42°C to activate the expression of Caspase-3, and simultaneously releases DNase / RNase degrading agents to clear genetic materials, ensuring that the biological components are completely decomposed within 6 hours, and permanently disabling the drive circuit through an electronic fusing mechanism.
[0024] Advantages of the present invention:
[0025] 1. By integrating innovative technologies such as vascularized 3D bioprinting, dynamic microfluidic culture, GAN-optimized interface, and transfer learning adaptation, the performance and manufacturing efficiency of the bio-robot are significantly improved. The hierarchical vascular network design can enhance the long-term survival rate of biological tissues, the dynamic microfluidic system improves the differentiation efficiency of functional tissues, the fractal interface optimized by GAN reduces the contact impedance, and improves the transmission efficiency of bio-electronic signals, solving key problems such as tissue necrosis, uneven function, interface damage, and signal transmission failure in the manufacturing of traditional bio-robots.
[0026] 2. Through the intelligent manufacturing process driven by deep learning, not only the precise and controllable growth of biological tissues is achieved, but also the intervention requirements of staff are greatly reduced, the manufacturing cost is reduced, and the production cycle is shortened. Brief Description of the Drawings
[0027] Figure 1 Shown is a schematic diagram of the manufacturing process of the present invention;
[0028] Figure 2 Shown is a flowchart of the working process of the present invention. Detailed Embodiments
[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.
[0030] The present invention provides an embodiment: A bio-robot based on a deep learning model, comprising:
[0031] A biological tissue carrier, which is composed of living cells, muscle tissue, or nerve tissue, serves as the core execution unit of the robot, has biocompatibility and self-repair ability; is genetically engineered to respond to specific stimuli, and uses a three-dimensional biological scaffold to provide structural support, while integrating a microvascular network to maintain long-term survival;
[0032] A multimodal sensor array, including chemically, optically, and mechanically sensor arrays with bionic designs, collects multi-dimensional data such as pH value, temperature, and mechanical pressure in the environment in real time, and converts analog signals into digital signals through a bio-electronic hybrid interface and inputs them into a processor;
[0033] An embedded deep learning processor, using a low-power neuromorphic chip, deploys a hybrid architecture of a lightweight convolutional neural network and a long short-term memory network, supporting online learning and adaptive inference; optimizes model parameters through quantization training, enabling it to process spatio-temporal features input by sensors under resource-constrained conditions and output control instructions to a drive module;
[0034] A bionic drive device: composed of a flexible electroactuator coupled with biological tissue, converts electrical signals sent by the processor into mechanical motion; the electroactuator uses an ionic gel material to simulate muscle contraction, designs a strain transmission path through topological optimization, and integrates a strain feedback circuit to achieve closed-loop control.
[0035] The deep learning model adopts a spiking neural network architecture, and its neuron model is based on the Leaky Integrate-and-Fire or Hodgkin-Huxley equation, simulating the dynamic characteristics of the membrane potential of biological neurons; the network processes spatio-temporal spike trains input by sensors through an event-driven asynchronous computing method, and the synaptic weights are adaptively adjusted using the STDP rule, enabling the system to have a biological-like real-time information processing ability.
[0036] The training method of the neural network uses a reinforcement learning framework, and the framework adopts the PPO algorithm. Biofeedback signals are collected in real time through an implanted microelectrode array or fluorescence imaging technology; the reward function is designed as a multi-dimensional objective combination, including motion efficiency, tissue health, and task completion. The output layer of the policy network is connected to the intensity-frequency parameters of the optogenetic regulation module to achieve the optimal matching of control instructions and biological systems.
[0037] The bio-robot further includes:
[0038] An optogenetic stimulation module, which converts digital instructions output by the neural network into light pulses of a specific wavelength to precisely control the contraction / relaxation of transgenic biological tissue;
[0039] A microbial fuel cell, which dynamically adjusts the energy supply rhythm according to the metabolic demand predicted by deep learning;
[0040] A growth factor control module, where the neural network actively regulates the proliferation / apoptosis of biological tissue by analyzing long-term environmental data to achieve macroscopic morphological evolution;
[0041] The neural plasticity simulation module triggers the programmed death of biological tissues when the deep learning model detects a preset danger threshold.
[0042] The optogenetic stimulation module integrates a micro-LED array with transgenic biological tissues, where the tissues express ChR2 or ArchT photosensitive proteins. The control instructions output by the deep learning model are converted into 470-nm blue light or 590-nm yellow light pulse sequences through a PWM circuit, with a light intensity resolution of 0.1 mW / mm 2 , and the stimulation frequency range of 1 - 100 Hz is programmable, achieving sub-millimeter spatial positioning accuracy through an optical fiber light guiding system.
[0043] The microbial fuel cell uses the engineered strain Shewanella oneidensis MR-1 as the anode catalyst, and its extracellular electron transfer efficiency is increased by 3 times under the growth conditions optimized by deep learning. The cathode adopts a platinum / carbon-biofilm hybrid structure. The system integrates a coulomb counting chip and an LSTM prediction model, dynamically adjusts the flow rate of the nutrient solution to maintain the optimal power density, and switches to the sleep mode during the low-activity period to extend the continuous power supply time.
[0044] The growth factor control module includes a microfluidic chip and VEGF / BMP-2 sustained-release microspheres. The neural network analyzes the movement trajectories, mechanical loads, and metabolic data within a 30-day cycle to determine the local growth factor release concentration; at the same time, it regulates the spatio-temporal distribution of apoptotic signals, causing the biological tissue to thicken by 5 - 20% in the stress concentration area and spontaneously degenerate in the non-essential area, achieving adaptive remodeling of the macroscopic morphology.
[0045] The neural plasticity simulation module uses a 100×100-scale memristor crossbar array at the hardware level, and its resistance change simulates synaptic strength. At the software level, it implements a reward and punishment mechanism similar to BDNF. When the action sequence leads to an increase in the task success rate, the Hebbian learning rate of the corresponding neural pathway automatically increases by 300%, while continuous failures trigger synaptic pruning, enabling the robot to have the ability to accumulate "experience" to avoid dangerous paths.
[0046] Multiple bio-robots exchange data through a molecular communication channel designed by synthetic biology. The molecular communication channel is based on the quorum sensing mechanism. Each robot secretes AHL molecules as information carriers, and the receiver detects the concentration gradient through the LuxR protein receptor; Federated learning uses a model aggregation algorithm with differential privacy protection, synchronizing the environmental recognition model parameters of each node every 8 hours. After the global model is updated, the signal molecule synthesis genes of the local flora are reprogrammed through the CRISPR-dCas9 system to achieve the self-evolution of the communication protocol.
[0047] The bio-robot is implanted with a programmable apoptosis gene switch, which is controlled by a temperature-sensitive promoter. When the deep learning model detects that the tissue necrosis index exceeds the threshold or the task times out, it triggers local heating at 42°C to activate the expression of Caspase-3, and simultaneously releases DNase / RNase degrading agents to clear genetic materials, ensuring that the biological components are completely decomposed within 6 hours, and permanently disabling the drive circuit through an electronic fusing mechanism.
[0048] Please refer to Figure 1 , and further, the method for manufacturing the bio-robot is described in detail:
[0049] 3D bioprinting step: Using a gelatin-sodium alginate composite bioink, a porous scaffold structure with a biomimetic hierarchical vascular network (main blood vessel diameter 200μm, capillary <50μm) is constructed through an extrusion printing mechanism. During the printing process, an endothelial cell suspension (density 1×10 6 cells / mL) is perfused synchronously, and cultured at 37°C and 5% CO2 for 48 hours to promote cell adhesion, forming a living matrix with the ability to transport nutrients. Its Young's modulus can be regulated in the range of 10-100 kPa to match the mechanical requirements of the target tissue.
[0050] Microfluidic culture step: Inject induced pluripotent stem cells (iPSCs) into the directed differentiation chamber (chamber height 100μm) of the PDMS chip. By dynamically perfusing a gradient medium containing Wnt3a / Activin A factors (flow rate 0.5 mL / h), they are differentiated into functional skeletal muscle tissue (myosin heavy chain expression >80%) within 7 days, and microelectrode arrays are used to apply 0.1 Hz / 5V electrical pulse stimulation to promote myotube alignment, finally obtaining a driving unit with the ability of spontaneous contraction (contractile force up to 0.5 mN / mm 2 ).
[0051] GAN optimization step: Construct a database containing 10,000 groups of bio-electronic interface samples, train the generator of the conditional generative adversarial network (cGAN) to output the optimal interface topology structure (resolution 1μm). The input of the generator is tissue elastic modulus, conductive nanowire (such as polypyrrole) distribution and strain field simulation data. The discriminator verifies the standard of interface shear stress <1 kPa based on finite element analysis. Finally, a conductive microbump array with fractal characteristics (height 20μm, spacing 50μm) is obtained, reducing the tissue-electrode contact impedance to below 5 kΩ·cm 2 .
[0052] Transfer learning steps: Based on the pre-trained ResNet-50 model (ImageNet dataset), a feature extraction layer freezing strategy is adopted, and only the fully connected layer is fine-tuned to adapt to specific tasks of the bio-robot, such as obstacle avoidance or grasping. The training data comes from 200 hours of real-time operation records of 5 prototypes (including optical microscope images and mechanical sensor data). The domain adaptation loss function is used to reduce the gap between the simulation and the real environment. Finally, the model still maintains a control accuracy of over 85% when migrating across tissue types (cardiac muscle / skeletal muscle).
[0053] Please refer to Figure 2 , furthermore, the workflow of the bio-robot is described in detail as follows:
[0054] S1. The bio-robot uses an integrated multi-modal sensor array (including optical, chemical, and mechanical sensors) to monitor the surrounding environmental parameters in real time. The optical sensor captures light signals in the 400 - 700 nm band based on a retina-like structure. The chemical sensor detects chemical indicators such as pH value and specific molecular concentration through a functionalized nanowire array. The mechanical sensor uses piezoelectric materials to convert pressure signals into electrical signals. All sensing data is transmitted to the central processor at a sampling rate of 100 Hz, forming a basic dataset for environmental perception.
[0055] S2. After receiving the raw sensor data, the embedded deep learning processor performs feature extraction and analysis through a pre-trained spiking neural network (SNN) architecture. The network contains 5 convolutional layers and 3 LSTM layers, and uses an event-driven computing mode to process spatio-temporal features, completing the conversion from raw data to environmental semantic information (such as obstacle position, target recognition) within 20 ms. At the same time, the network parameters are continuously optimized through an online learning mechanism to adapt to dynamic environmental changes.
[0056] S3. Based on the environmental analysis results, the reinforcement learning decision-making module generates an optimal action sequence. This module integrates a pre-trained PPO algorithm policy network and value network, considers the current state of the biological tissue (such as energy level, fatigue degree) and environmental constraints (such as space limitations, task requirements), and outputs a control instruction set containing movement direction, speed, and duration. At the same time, the long-term action consequences are evaluated through Monte Carlo tree search to ensure the global optimality of the decision.
[0057] S4. After being converted by DAC, the control instructions are transmitted to the bionic drive system. The electrical signals act on the optogenetic module through a custom-designed stimulation circuit to generate 470 nm blue light pulses to precisely activate the transgenic muscle tissue. The contraction force generated by the drive unit is amplified 3 - 5 times by a flexible transmission mechanism to achieve multi-degree-of-freedom movement with millimeter-level precision. At the same time, the built-in strain sensor will feedback the execution status in real time, thus forming a closed-loop control.
[0058] S5. The microbial fuel cell system continuously monitors the energy reserve. When the detected voltage is lower than 3V, it activates the nutrient delivery pump to supplement the substrate. The deep learning prediction model predicts the energy demand 30 minutes in advance based on historical activity data, dynamically adjusts the energy supply strategy, switches to the energy-saving mode (power consumption < 10mW) during the low-activity period, and at the same time improves the energy utilization efficiency by 40% through the metabolic by-product recovery system, ensuring the continuous working ability for more than 72 hours.
[0059] S6. After each task is completed, the system automatically collects the execution data (including motion trajectory error, energy consumption curve, and tissue state indicators), updates the local model parameters through the federated learning framework, uses differential privacy technology to protect data security, and the updated model realizes hardware-level parameter adjustment through the synaptic plasticity simulation module, enabling the robot to show continuous performance improvement in subsequent tasks.
[0060] Through the above steps, the hierarchical vascular network design can improve the long-term survival rate of biological tissues, the dynamic microfluidic system improves the differentiation efficiency of functional tissues, and the GAN-optimized fractal interface reduces the contact impedance and improves the bio-electronic signal transmission efficiency, so as to solve the problems of tissue damage and signal transmission failure caused by stress concentration at the bio-electronic interface in the existing bio-robot manufacturing.
Claims
1. A bio-robot based on a deep learning model, characterized in that, Comprising: A biological tissue carrier, which is composed of living cells, muscle tissue or nerve tissue, serves as the core execution unit of the robot, and has biocompatibility and self-repair ability; it is genetically engineered to respond to specific stimuli, uses a three-dimensional biological scaffold to provide structural support, and integrates a microvascular network to maintain long-term survival; A multi-modal sensor array, including bionic-designed chemical sensors, optical sensors and mechanical sensors, which real-time collect the pH value, temperature and mechanical pressure in the environment, and convert the analog signals into digital signals through a bio-electronic hybrid interface and input them into the processor; An embedded deep learning processor, which uses a low-power neuromorphic chip, deploys a hybrid architecture of a lightweight convolutional neural network and a long short-term memory network, and supports online learning and adaptive inference; the model parameters are optimized through quantization training, so that it can still process the spatio-temporal features of the sensor input under resource-constrained conditions, and output control instructions to the drive module; A bionic drive device: composed of a flexible electroactuator coupled with biological tissue, which converts the electrical signals sent by the processor into mechanical motion; among them, the electroactuator uses an ionic gel material to simulate muscle contraction, optimizes the strain transmission path through topological design, and integrates a strain feedback circuit to achieve closed-loop control.
2. The bio-robot based on the deep learning model according to claim 1, characterized in that: The deep learning model adopts a spiking neural network architecture, and its neuron model is based on the Leaky Integrate-and-Fire or Hodgkin-Huxley equation to simulate the membrane potential dynamic characteristics of biological neurons; the network processes the spatio-temporal spike sequence of the sensor input through an event-driven asynchronous computing method, and the synaptic weights are adaptively adjusted using the STDP rule, enabling the system to have a biological-like real-time information processing ability.
3. The bio-robot based on a deep learning model according to claim 2, wherein the training method of the neural network is characterized in that: The training method uses a reinforcement learning framework, and the framework adopts the PPO algorithm. The biofeedback signal is real-time collected through an implantable microelectrode array or fluorescence imaging technology; the reward function is designed as a multi-dimensional target combination, including motion efficiency, tissue health and task completion. The output layer of the policy network is connected to the intensity-frequency parameters of the optogenetic regulation module to achieve the optimal matching of the control instruction and the biological system.
4. The bio-robot based on a deep learning model according to claim 1, wherein The bio-robot also includes: An optogenetic stimulation module, which converts the digital instructions output by the neural network into light pulses of a specific wavelength to precisely control the contraction / relaxation of the transgenic biological tissue; A microbial fuel cell, which dynamically adjusts the energy supply rhythm according to the metabolic demand predicted by deep learning; A growth factor control module, where the neural network actively regulates the proliferation / apoptosis of biological tissue by analyzing long-term environmental data to achieve macroscopic morphological evolution; A neural plasticity simulation module, when the deep learning model detects a preset danger threshold, it triggers the programmed death of biological tissue.
5. The bio-robot based on the deep learning model according to claim 4, characterized in that: The optogenetic stimulation module integrates a micro-LED array with transgenic biological tissue, where the tissue expresses ChR2 or ArchT photosensitive proteins. The control instructions output by the deep learning model are converted into 470 nm blue light or 590 nm yellow light pulse sequences through a PWM circuit, and the light intensity resolution reaches 0.1 mW / mm 2 , and the stimulation frequency range of 1 - 100 Hz is programmable, achieving sub-millimeter spatial positioning accuracy through an optical fiber light guiding system.
6. The bio-robot based on the deep learning model according to claim 4, wherein: The microbial fuel cell uses the Shewanella oneidensis MR-1 engineered strain as the anode catalyst. Its extracellular electron transfer efficiency is increased by 3 times through growth conditions optimized by deep learning. The cathode adopts a platinum / carbon-biofilm hybrid structure. The system integrates a coulomb counting chip and an LSTM prediction model, dynamically adjusts the nutrient solution flow rate to maintain the optimal power density, and switches to sleep mode during low activity periods to extend the continuous energy supply time.
7. A bio-robot based on a deep learning model according to claim 4, characterized in that: The growth factor control module includes a microfluidic chip and VEGF / BMP-2 sustained-release microspheres. The neural network determines the local growth factor release concentration by analyzing the motion trajectory, mechanical load and metabolic data within a 30-day period; at the same time, it regulates the spatiotemporal distribution of apoptosis signals, so that the biological tissue thickens by 5-20% in the stress concentration area and spontaneously degenerates in the non-essential area, thereby achieving adaptive remodeling of the macroscopic morphology.
8. A bio-robot based on a deep learning model according to claim 4, characterized in that: The neural plasticity simulation module uses a 100×100 memristor cross array at the hardware level, whose resistance changes simulate synaptic strength, and implements a BDNF-like reward and punishment mechanism at the software level. When the action sequence leads to an increase in the task success rate, the Hebbian learning rate of the corresponding neural pathway automatically increases by 300%, and continuous failures trigger synaptic pruning, enabling the robot to accumulate "experience" to avoid dangerous paths.
9. The bio-robot based on the deep learning model according to claim 4, wherein: Multiple biological robots exchange data through a molecular communication channel designed by synthetic biology. The molecular communication channel is based on a quorum sensing mechanism. Each robot secretes AHL molecules as an information carrier, and the receiving end detects the concentration gradient through the LuxR protein receptor. Federated learning uses a model aggregation algorithm with differential privacy protection to synchronize the environmental recognition model parameters of each node every 8 hours. After the global model is updated, the signal molecule synthetic genes of the local flora are reprogrammed through the CRISPR-dCas9 system to achieve self-evolution of the communication protocol.
10. A bio-robot based on a deep learning model according to claim 4, characterized in that: The biorobot is implanted with a programmable apoptosis gene switch, which is controlled by a temperature-sensitive promoter. When the deep learning model detects that the tissue necrosis index exceeds the threshold or the task times out, it triggers local heating at 42°C to activate Caspase-3 expression, while releasing DNase / RNase degraders to clear genetic material, ensuring that the biological components are completely decomposed within 6 hours and permanently disabling the drive circuit through an electronic fuse mechanism.