CN118968835B - A modeling device for evaluating skin repair efficacy based on machine learning model - Google Patents
A modeling device for evaluating skin repair efficacy based on machine learning model Download PDFInfo
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Abstract
The invention provides a skin repair efficacy evaluation modeling device based on a machine learning model, which relates to the technical field of skin repair, and comprises a friction module, a friction module and a control module, wherein the friction module is used for applying uniform friction force on the surface of skin; the skin safety protection device comprises a human body three-dimensional skin model, a scratch module, a friction frequency counting module, a force control module, a speed control module, an angle control module, a feedback module, a central control module, a safety protection module and a safety protection module, wherein the scratch module is used for manufacturing scratches on the surface of the human body three-dimensional skin model, the friction frequency counting module is used for recording the friction frequency, the force control module is used for controlling the friction force applied to the skin, the speed control module is used for controlling the friction speed on the skin, the scratch control module is used for controlling the length of the scratches generated by friction on the skin, the angle control module is used for adjusting the scratch angle on the skin, the feedback module is used for measuring the erythema index and the heme content of the skin through a multispectral imaging device, and the central control module is used for controlling the force control module, the speed control module, the scratch control module and the position control module.
Description
Technical Field
The invention relates to the technical field of skin repair, in particular to a skin repair efficacy evaluation modeling device based on a machine learning model.
Background
Skin injury modeling is an important tool in skin pathology research, helping scientists understand the physiological and biochemical reactions of skin during injury and repair. By simulating skin lesions of different types and degrees, the healing mechanism, inflammatory response and regeneration process of the skin can be studied.
The existing skin injury modeling is mostly dependent on manual operation. The manual operation is not only low in efficiency, but also easy to produce errors, and the uniformity and consistency of friction times, force, speed, scratch length, depth, angles and distribution are difficult to ensure, so that the accuracy and repeatability of test results are affected.
Disclosure of Invention
In order to solve the technical problems that skin injury modeling is dependent on manual operation in the prior art, the manual operation is low in efficiency, errors are easy to generate, the uniformity and consistency of friction times, force, speed, scratch length, depth, angle and distribution are difficult to ensure, and the accuracy and repeatability of a test result are affected, the invention provides skin repair efficacy evaluation modeling equipment based on a machine learning model.
The technical scheme provided by the embodiment of the invention is as follows:
the embodiment of the invention provides a skin repair efficacy evaluation modeling device based on a machine learning model, which comprises the following components:
the friction module is used for simulating friction damage and applying uniform friction force on the surface of the skin;
the scratch module is used for manufacturing scratches on the surface of the normal human body three-dimensional skin model;
the friction frequency counting module is used for recording the friction frequency;
the force control module is used for controlling the friction force applied to the skin through the mechanical arm and feeding back the friction force on the skin in real time through the force sensor;
The speed control module is used for controlling the friction speed on the skin by controlling the rotating speed of the motor, and feeding back the friction speed on the skin in real time by the speed sensor;
The scratch control module is used for controlling the length of scratches generated by friction on the skin through the sliding rail system, controlling the depth of the scratches generated by friction on the skin through the depth adjusting device, and feeding back the depth of the scratches generated by friction on the skin in real time through the depth sensor;
the angle control module is used for adjusting the scratch angle on the skin through the angle fine adjustment device and controlling the scratch angle on the skin to be kept constant through the fixed angle mechanism;
The position control module is used for controlling the distribution of the multipoint scratches on the skin to be uniform through the multipoint scratching system and feeding back the positions of the multipoint scratches on the skin in real time through the position sensor;
The feedback module is used for measuring the erythema index and heme content of the skin through the multispectral imaging device;
The central control module is used for controlling the force control module, the speed control module, the scratch control module and the position control module according to the data fed back by the force sensor, the speed sensor, the depth sensor and the position sensor in real time and the erythema index and the heme content fed back by the feedback module in real time;
and the safety protection module is used for carrying out safety protection through the emergency stop button and the safety guardrail.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
According to the invention, the force control module, the speed control module, the scratch control module and the position control module can be controlled according to the data fed back by the force sensor, the speed sensor, the depth sensor and the position sensor in real time and the erythema index and heme content fed back by the feedback module in real time, so that the skin injury molding is automatically realized, the skin injury molding efficiency is improved, the uniformity and consistency of friction times, force, speed, scratch length, depth, angle and distribution can be ensured, the standardization and consistency of skin injury molding in skin human efficacy test and skin injury maintenance items are improved, and the accuracy and repeatability of test results are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a skin repair efficacy evaluation modeling apparatus based on a machine learning model according to an embodiment of the present invention.
[ Reference numerals ]
The device comprises a 1-friction module, a 2-scratch module, a 3-friction frequency counting module, a 4-force control module, a 5-speed control module, a 6-scratch control module, a 7-angle control module, an 8-position control module, a 9-feedback module, a 10-central control module and an 11-safety protection module.
Detailed Description
The technical scheme of the invention is described below with reference to the accompanying drawings.
In embodiments of the invention, words such as "exemplary," "such as" and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the term use of an example is intended to present concepts in a concrete fashion. Furthermore, in embodiments of the present invention, the meaning of "and/or" may be that of both, or may be that of either, optionally one of both.
In the embodiments of the present invention, "image" and "picture" may be sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized. "of", "corresponding (corresponding, relevant)" and "corresponding (corresponding)" are sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized.
In embodiments of the present invention, sometimes a subscript such as W 1 may be written in a non-subscript form such as W1, the meaning of which is intended to be consistent, without regard to distinction.
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1 of the specification, a schematic structural diagram of a skin repair efficacy evaluation modeling apparatus based on a machine learning model according to an embodiment of the present invention is shown.
The embodiment of the invention provides a skin repair efficacy evaluation modeling device based on a machine learning model, which can enable healthy skin to be subjected to transient recoverable erythema phenomenon within a safe range, only causes slight and temporary skin redness, and the reaction is expected and controllable and does not violate medical ethical ethics.
The skin repair efficacy evaluation modeling device based on the machine learning model comprises a friction module 1, a scratch module 2, a friction number counting module 3, a force control module 4, a speed control module 5, a scratch control module 6, an angle control module 7, a position control module 8, a feedback module 9, a central control module 10 and a safety protection module 11.
The friction module 1 is used for simulating friction damage and applying uniform friction force on the skin surface.
In particular, the friction module 1 is typically a device with friction pads or friction surfaces. The friction pad may be made of various materials (e.g., sandpaper, cloth) with different roughness on the surface for producing different degrees of friction damage. The friction module 1 is fixed at the end of the arm, ensuring that the friction force can be applied uniformly on the target surface.
A scratching module 2 is used for manufacturing scratches on the surface of the normal human body three-dimensional skin model.
Specifically, the scoring module 2 is embodied as a scoring needle or a custom scoring device, capable of producing a sharp score on the target surface. The scratching module 2 is mounted on the mechanical arm through a clamp or a fixing device, and can precisely control parameters (such as length, depth and angle) of the scratch on the target surface.
And the friction number counting module 3 is used for recording the friction number.
Specifically, the friction number counting module 3 is implemented by an electronic counter, and automatically records once after each friction is completed.
Optionally, the electronic counter is mounted on a control panel of the robotic arm or friction device.
Further, the friction number counting module 3 further includes a friction sensor. A friction sensor is mounted adjacent the friction surface, detects each friction action and sends a signal to an electronic counter.
In the invention, the friction frequency counting module 3 realized by the electronic counter not only improves the accuracy and efficiency of the experiment, but also remarkably improves the consistency and repeatability of the experiment operation, and provides a reliable data base for skin repair efficacy evaluation.
The force control module 4 is used for controlling the friction force applied to the skin through the mechanical arm, and can accurately control the force or strength applied to the skin. The friction force on the skin is fed back in real time through the force sensor, so that the consistency of the friction force at each time can be ensured.
Specifically, the robot arm is mounted on the apparatus main body, and the distal end is provided with a force sensor. The force sensor is mounted at the contact point of the end of the mechanical arm, monitors the applied force in real time and feeds back to the central control module 10.
According to the invention, the force control module 4 can accurately control the friction force applied to the skin through the combination of the mechanical arm and the force sensor, so that the consistency of the experimental force of each time is ensured, the accuracy, the efficiency and the repeatability of the experiment are obviously improved, the human error and the sample damage are reduced, and a reliable experimental basis is provided for the evaluation of the skin repairing efficacy.
The speed control module 5 is used for controlling the friction speed on the skin by controlling the rotation speed of the motor, so that the uniformity of the friction speed can be realized. The friction speed on the skin is fed back in real time by the speed sensor, so that the friction speed can be ensured to be constant.
In particular, the motor is used to drive a friction device, installed in the drive system of the friction apparatus. The speed sensor is mounted on the motor shaft, monitors the rotational speed in real time and feeds back to the central control module 10.
In the invention, the speed control module 5 can precisely control the friction speed on the skin by controlling the combination of the motor rotating speed and the speed sensor, and ensure constant experiment speed each time, thereby obviously improving the accuracy, the efficiency and the repeatability of the experiment, reducing human errors and sample damage and providing a reliable experiment foundation for skin repair efficacy evaluation.
The scratch control module 6 is used for controlling the length of scratches generated by friction on the skin through the sliding rail system, and can accurately set the length and depth of the scratches. The depth of the scratch generated by friction on the skin is controlled by the depth adjusting device, and the depth of the scratch generated by friction on the skin is fed back in real time by the depth sensor, so that the consistency of the scratch depths can be ensured.
Specifically, the sliding rail system is arranged in an operation area of the equipment and is provided with a high-precision electric sliding rail, the length of the sliding rail is adjustable, and precise scales are arranged. The electric lifting platform is arranged on the sliding rail system and used for adjusting the scratch depth. The depth sensor is installed on the electric lifting platform, and the scratch depth is monitored in real time and fed back to the central control module 10.
In one possible embodiment, the rail system is configured with a high-precision electric rail, the length of which is adjustable, and the electric rail is provided with a precision scale, so that accurate control of the scratch length can be ensured.
In one possible embodiment, the depth adjusting device comprises an electric lifting platform, and the depth of the scratch generated by friction on the skin is controlled by controlling the height of the electric lifting platform, so that the consistency of the depth of the scratch can be ensured each time.
According to the invention, through the combination of the high-precision electric sliding rail, the electric lifting platform and the depth sensor, the scratch control module 6 can accurately control the length and the depth of the friction scratch on the skin, and ensure that the length and the depth of the scratch in each experiment are consistent, so that the accuracy, the efficiency and the repeatability of the experiment are obviously improved, the human error and the sample damage are reduced, and a reliable experiment foundation is provided for the skin repair efficacy evaluation.
The angle control module 7 is used for adjusting the scratch angle on the skin through the angle fine adjustment device, and controlling the scratch angle on the skin to be constant through the fixed angle mechanism. The angle fine adjustment device allows fine adjustment of the scratch angle in a preset range, and flexibility and precision of experiments can be ensured.
Specifically, the fixed angle mechanism is mounted at the end of the mechanical arm, and ensures that the scratch angle is constant. The angle fine adjustment device is installed on the fixed angle mechanism, and allows fine adjustment of the scratch angle within a preset range.
In one possible embodiment, the fixed angle mechanism is disposed at the end of the mechanical arm, which can ensure that the scoring angle is constant.
According to the invention, through the combination of the angle fine adjustment device and the fixed angle mechanism, the angle control module 7 can accurately adjust and maintain the angle of the scratch on the skin, and ensure the consistency and flexibility of the angle in the experiment, so that the accuracy, the efficiency and the repeatability of the experiment are remarkably improved, the human error is reduced, and a reliable experiment foundation is provided for the evaluation of the skin repair efficacy.
The position control module 8 is used for controlling the distribution of the multi-point scratches on the skin to be uniform through the multi-point scratch system, and feeding back the positions of the multi-point scratches on the skin in real time through the position sensor.
Specifically, the position control module 8 presets a plurality of scribing positions by a computer program, and the robot arm sequentially performs scribing operations according to the preset positions. Meanwhile, the position control module 8 further comprises position calibration means. The position calibration device comprises a laser positioning device and a camera, is arranged above or around the operation area, and is used for monitoring the scratch position in real time and ensuring the position to be accurate.
In one possible embodiment, the multi-point scoring system presets a plurality of scoring positions by a computer program, and the mechanical arm sequentially performs a rubbing operation according to the preset plurality of scoring positions.
In one possible embodiment, the position sensor specifically includes a laser positioning sensor and a camera, and the laser positioning sensor and the camera feed back the positions of the multiple scratches on the skin in real time, so that the accurate positions of the scratches can be ensured, and error accumulation can be avoided.
In the invention, by combining the multi-point scratching system and the position sensor, the position control module 8 can accurately control the multi-point scratching distribution on the skin, ensure the accuracy and consistency of the scratching position in each experiment, thereby obviously improving the accuracy, efficiency and repeatability of the experiment, reducing human errors and providing a reliable experiment foundation for the evaluation of the skin repairing efficacy.
A feedback module 9 for measuring the erythema index and the heme content of the skin by means of a multispectral imaging device.
The multispectral imaging device collects reflected light information of different depths of the skin through a plurality of spectral channels and analyzes the change of erythema and heme content of the skin.
Among them, the erythema index (Erythema Index, EI) is an index for quantifying the degree of skin erythema (skin redness phenomenon usually caused by inflammation, irritation or trauma).
Wherein the heme content (Hemoglobin Content) is an indicator of the measurement of the concentration of hemoglobin (heme) in the skin for assessing skin blood flow and blood supply.
In one possible implementation, the feedback module 9 is specifically configured to:
the erythema index and heme content of the skin were measured by multispectral imaging equipment.
And predicting the skin state according to the erythema index and the heme content by using a data fusion technology.
Optionally, the data from different sensors are comprehensively processed through data fusion technologies such as weighted average, bayesian inference and the like, so that the accuracy and reliability of the data are improved.
Specifically, the data fusion technique specifically includes:
Erythema index (RI) and heme content (Hb) data of the skin surface are collected in real time by a multispectral imaging device.
Preprocessing the collected erythema index and haemoglobin data to remove noise and abnormal values. Common methods include median filtering and normalization.
Weighted averaging of the preprocessed data to obtain more stable and reliable results:
Wherein RI avg represents the weighted average erythema index, hb avg represents the weighted average heme content, RI i and Hb i are the erythema index and heme content of the ith harvest, respectively, and ω i is the weight at the ith harvest.
And combining the data acquired for multiple times with priori knowledge, and obtaining more accurate estimation by using a Bayesian inference method. The Bayes formula is as follows:
where P (θ|data) is a posterior probability, P (data|θ) is a likelihood function, P (θ) is an a priori probability, and P (data) is a normalization constant.
Calculating proper operation parameter adjustment amount according to the fused erythema index and heme content:
Fadjust＝Fcurrent+k1(RItarget-RIavg)+k2(Hbtarget-Hbavg)
Vadjust＝Vcurrent+k3(RItarget-RIavg)+k4(Hbtarget-Hbavg)
Dadjust＝Dcurrent+k5(RItarget-RIavg)+k6(Hbtarget-Hbavg)
Wherein, F adjust、Vadjust and D adjust are respectively the adjustment amounts of friction force, speed and depth, F current、Vcurrent and D current are respectively the current friction force, speed and depth, k 1 to k 6 are adjustment coefficients, RI target represents the target erythema index, and Hb arget represents the target heme content.
In the invention, by using multispectral imaging equipment and a data fusion technology, the feedback module 9 can provide accurate and reliable skin state evaluation data, support various application scenes and improve the efficiency and quality of experiments and researches.
The central control module 10 is configured to control the force control module 4, the speed control module 5, the scratch control module 6 and the position control module 8 according to the data fed back in real time by the force sensor, the speed sensor, the depth sensor and the position sensor and the erythema index and the heme content fed back in real time by the feedback module 9 by using a machine learning algorithm. Ensuring the accuracy and consistency of the operation process.
Optionally, a force sensor is mounted at the contact point of the end of the mechanical arm for monitoring the applied force. And the depth sensor is arranged on the electric lifting platform and is used for monitoring the scratch depth. And the speed sensor is arranged on the motor shaft and used for monitoring the friction speed. And the laser positioning and camera is arranged above or around the operation area and is used for monitoring the scratch position in real time and ensuring the position to be accurate.
Specifically, the central control module 10 is composed of a high-performance processor and embedded control software, and monitors and controls the operations of the respective modules in real time.
In one possible implementation, the central control module 10 is specifically configured to:
A model of the relationship between skin state and operating parameters is established using a machine learning algorithm.
Specifically, the machine learning algorithm may input the erythema index RI, heme content Hb, friction force F, speed V, depth D. The adjustment amounts of the friction force F adjust, the speed V adjust and the depth D adjust are outputted.
A large amount of experimental data was collected and recorded for erythema index, heme content and corresponding operating parameters (intensity, speed, depth, etc.).
Important features are selected as model inputs such as erythema index, heme content, intensity, speed and depth.
Training is performed using regression analysis or neural network models. For example, using a multiple linear regression model:
RI=β0+β1F+β2V+β3D+ε
Hb=γ0+γ1F+γ2V+γ3D+η
wherein RI and Hb are respectively erythema index and heme content, F, V, D are respectively dynamics, speed and depth, beta 0、β1、β2、β3、γ0、γ1、γ2 and gamma 3 are model coefficients, epsilon and eta are error terms.
And using methods such as cross-validation and the like to evaluate the accuracy and stability of the model, and selecting the optimal model.
The safety protection module 11 is used for carrying out safety protection through an emergency stop button and a safety guardrail.
In the operation process, the model predicts according to the real-time monitoring data and adjusts the operation parameters. The method comprises the following specific steps:
(1) And (3) collecting real-time data, namely collecting the erythema index, the heme content and the current operation parameters in real time through a sensor.
(2) And the data input model is used for inputting the data acquired in real time into the machine learning model.
(3) And predicting the adjustment amount, namely predicting the operation parameters to be adjusted by the model according to the input data.
(4) And adjusting operation parameters, namely adjusting friction force, speed and depth according to a model prediction result.
Optionally, the machine learning model is trained through a large amount of experimental data, so that the relation model parameters are continuously optimized, and the accuracy and stability of prediction are improved.
The skin state fed back in real time by the feedback module 9 is acquired.
The operation parameters are determined according to the skin state fed back in real time by the feedback module 9 by using a relation model between the skin state and the operation parameters.
Optionally, the operating parameters include friction force, friction speed, scratch depth, and scratch angle.
According to the operation parameters, the control strength control module 4, the speed control module 5, the scratch control module 6 and the position control module 8.
In the invention, through the real-time monitoring and intelligent adjustment of the central control module 10, the accuracy and consistency of the operation process can be ensured, the personal error is reduced, the experimental efficiency and the data quality are improved, a reliable experimental foundation is provided for the skin repair efficacy evaluation, and the promotion of the scientific research level is promoted.
In one possible embodiment, the operation flow of the skin repair efficacy evaluation modeling apparatus based on the machine learning model specifically includes:
S1, setting operation parameters.
Optionally, the operating parameter comprises a preset number of rubs.
S2, fixing the skin sample on a workbench manufacturer.
And S3, starting the equipment, and performing friction operation on the mechanical arm according to the operation parameters.
And S4, recording the friction times in real time, and monitoring and adjusting the friction force, the friction speed, the scratch depth and the scratch angle.
And S5, automatically stopping operation when the preset friction times are reached, and recording parameter data.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
According to the invention, according to the real-time feedback data of the force sensor, the speed sensor, the depth sensor and the position sensor and the real-time feedback erythema index and heme content of the feedback module 9, the force control module 4, the speed control module 5, the scratch control module 6 and the position control module 8 are controlled, the skin injury molding is automatically realized, the skin injury molding efficiency is improved, the uniformity and consistency of friction times, force, speed, scratch length, depth, angle and distribution can be ensured, the standardization and consistency of skin injury molding in skin human efficacy test and repair projects are improved, and the accuracy and repeatability of test results are improved.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
The following points need to be described:
(1) The drawings of the embodiments of the present invention relate only to the structures related to the embodiments of the present invention, and other structures may refer to the general designs.
(2) In the drawings for describing embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced for clarity, i.e., the drawings are not drawn to actual scale. It will be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, it can be "directly on" or "under" the other element or intervening elements may be present.
(3) The embodiments of the invention and the features of the embodiments can be combined with each other to give new embodiments without conflict.
The present invention is not limited to the above embodiments, but the scope of the invention is defined by the claims.
Claims (10)
1. A machine learning model-based skin repair efficacy assessment modeling apparatus, comprising:
the friction module is used for simulating friction damage and applying uniform friction force on the surface of the skin;
the scratch module is used for manufacturing scratches on the surface of the normal human body three-dimensional skin model;
the friction frequency counting module is used for recording the friction frequency;
the force control module is used for controlling the friction force applied to the skin through the mechanical arm and feeding back the friction force on the skin in real time through the force sensor;
The speed control module is used for controlling the friction speed on the skin by controlling the rotating speed of the motor, and feeding back the friction speed on the skin in real time by the speed sensor;
The scratch control module is used for controlling the length of scratches generated by friction on the skin through the sliding rail system, controlling the depth of the scratches generated by friction on the skin through the depth adjusting device, and feeding back the depth of the scratches generated by friction on the skin in real time through the depth sensor;
the angle control module is used for adjusting the scratch angle on the skin through the angle fine adjustment device and controlling the scratch angle on the skin to be kept constant through the fixed angle mechanism;
The position control module is used for controlling the distribution of the multipoint scratches on the skin to be uniform through the multipoint scratching system and feeding back the positions of the multipoint scratches on the skin in real time through the position sensor;
The feedback module is used for measuring the erythema index and heme content of the skin through the multispectral imaging device;
The central control module is used for controlling the force control module, the speed control module, the scratch control module and the position control module by utilizing a machine learning algorithm according to the force sensor, the speed sensor, the depth sensor and the position sensor and the data fed back in real time and the erythema index and the heme content fed back in real time by the feedback module;
and the safety protection module is used for carrying out safety protection through the emergency stop button and the safety guardrail.
2. The machine learning model based skin care efficacy assessment modeling apparatus of claim 1, wherein the rail system is configured with a high precision motorized rail having an adjustable length with precision graduations thereon.
3. The machine learning model based skin repair efficacy assessment modeling apparatus of claim 1, wherein the depth adjustment device comprises an electrically powered lift platform, the height of which is controlled to control the depth of scratches generated by friction on the skin.
4. The machine learning model based skin repair efficacy assessment modeling apparatus of claim 1, wherein the fixed angle mechanism is disposed at a distal end of the robotic arm.
5. The machine learning model-based skin repair efficacy assessment modeling apparatus of claim 1, wherein the multi-point scoring system presets a plurality of scoring positions via a computer program, and the robotic arm sequentially performs a rubbing operation according to the preset plurality of scoring positions.
6. The machine learning model based skin repair efficacy assessment modeling apparatus of claim 1, wherein the position sensor comprises in particular a laser positioning sensor and a camera by which the location of the multi-point scribe on the skin is fed back in real time.
7. The machine learning model based skin rejuvenation efficacy assessment modeling apparatus as defined in claim 1, wherein the feedback module is specifically configured to:
Measuring the erythema index and heme content of the skin by a multispectral imaging device;
and predicting the skin state according to the erythema index and the heme content by using a data fusion technology.
8. The machine learning model based skin repair efficacy assessment modeling apparatus of claim 1, wherein the central control module is specifically configured to:
Establishing a relation model between the skin state and the operation parameters by using a machine learning algorithm;
Acquiring the skin state fed back by the feedback module in real time;
determining an operation parameter by utilizing a relation model between the skin state and the operation parameter according to the skin state fed back by the feedback module in real time;
And controlling the dynamics control module, the speed control module, the scratch control module and the position control module according to the operation parameters.
9. The machine learning model based skin repair efficacy assessment modeling apparatus of claim 8, wherein the operating parameters include friction force, friction speed, scratch depth, and scratch angle.
10. The machine learning model based skin repair efficacy assessment modeling apparatus of claim 9, wherein the machine learning model based skin repair efficacy assessment modeling apparatus operational flow specifically comprises:
S1, setting operation parameters;
s2, fixing the skin sample on a workbench manufacturer;
s3, starting equipment, and performing friction operation on the mechanical arm according to the operation parameters;
s4, recording the friction times in real time, and monitoring and adjusting the friction force, the friction speed, the scratch depth and the scratch angle;
and S5, automatically stopping operation when the preset friction times are reached, and recording parameter data.
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| CN108478192A (en) * | 2018-04-11 | 2018-09-04 | 西安交通大学 | A measurement system for estimating the depth of microvessels in skin tissue |
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| CN108478192A (en) * | 2018-04-11 | 2018-09-04 | 西安交通大学 | A measurement system for estimating the depth of microvessels in skin tissue |
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