Micro hair transplantation system and parameter optimization control system for hair follicle transplantation

Abstract

The present application relates to the technical field of biomedical engineering, and particularly discloses a follicle planting parameter optimization control system in a micro hair transplantation system, which is set around the grid expression of a target supply / planting area and parameters such as target density / direction / travel, and comprises a follicle extraction module, a follicle planting module, a follicle extraction device configuration module and a follicle planting device configuration module: first, the device configuration module loads and calibrates the limit, advance, mapping, sensing threshold and beat rules of the hair extraction / planting device; during operation, the follicle extraction module selects candidate grids and dispatches hair extraction based on the quota, heat, neighborhood uniformity and risk agent; the follicle planting module executes hole making, implantation and cooling beats according to the equal area / equal arc length mixed grid, density interval and planting unit type mapping, and fine tunes the execution deviation; and real-time signals such as force, angle, temperature rise, timing, etc. are written back to the parameter library, the beat and threshold are corrected online, and adaptive optimization on the device side is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of biomedical engineering, and particularly relates to a follicle planting parameter optimization control system in a micro hair transplantation system. BACKGROUND

[0002] In the micro hair transplantation system, the optimization control of the follicle planting parameters is usually constructed as a closed loop process of multi-sensor fusion and intelligent feedback. The system first collects key data such as skin characteristics, needle insertion depth, pushing force and angle in real time through mechanical, visual and physiological sensors, analyzes the characteristics in combination with the modeling of the scalp area and the distribution characteristics of the follicles; then, the multi-objective optimization decision is made on the planting depth, angle, density and speed and other parameters by using fuzzy control, genetic algorithm or adaptive learning algorithm, and the execution mechanism is driven to realize the precise implantation operation. In the implantation process, the system continuously receives the mechanical and image feedback, dynamically corrects the operation deviation and updates the individualized parameter model, so as to form a self-learning type optimization control process with the goals of high-precision control, low-damage implantation and high survival rate.

[0003] For example, a hair transplantation planning scheme generation method, system, medium and electronic device disclosed in Chinese patent application No. CN116196098B include the following steps: obtaining a patient's head follicle image and a patient's head three-dimensional surface model; performing equal-area projection on the patient's head three-dimensional surface model to obtain a two-dimensional surface image, and superimposing and displaying the two-dimensional surface image with the patient's head follicle image; receiving the demarcation of the hair taking area and the hair transplantation area, and receiving the planting parameters; determining the positions of the follicles to be taken in the hair taking area and the positions of the follicles to be planted in the hair transplantation area according to the planting parameters to obtain a planning scheme.

[0004] For example, a hair transplantation surgery tracing planning method, system and storage medium disclosed in Chinese patent application No. CN115192191A include the following steps: obtaining the forehead width, actual hairline position and target hairline position after hair transplantation of a patient to be transplanted according to the facial features of the patient to be transplanted; obtaining the follicle density and the number of hairs in a unit follicle of the patient to be transplanted, and determining the target implantation density based on the follicle density and the number of hairs in a unit follicle; obtaining an occipital region image of the patient to be transplanted, and determining the follicle supply position from the occipital region image based on the target implantation density and the target transplantation area; obtaining a forehead region image of the patient to be transplanted, and determining the follicle implantation position from the forehead region image based on the target implantation density and the target transplantation area.

[0005] In combination with the above technical solutions, it is found that the existing planting parameter optimization control scheme is mostly based on the experience of the operator or preoperative static modeling for regular allocation and delineation. Due to the influence of individual facial / head skin features and subjective judgment of the operator, the preoperative planning and intraoperative execution are often disconnected, the modeling / registration accuracy is limited, and it is difficult to finely and uniformly take / seed on the curved surface, and the intraoperative still highly depends on spatial imagination and on-site judgment, which can lead to uneven distribution of hair taking and planting density in space, resulting in local oversaturation or oversaturation in the donor area, local patchy density and direction imbalance in the recipient area, thereby affecting the survival rate and aesthetic consistency, increasing the probability of revision and the operation time.

[0006] In addition, the existing planting parameter optimization control scheme mostly uses the method of simply mapping the target density with the number of hairs in a unit follicle, ignoring the real-time feedback closed-loop correction of follicle planting parameters during the operation, which may cause systematic deviation in direction consistency and microscopic planting rhythm. SUMMARY

[0007] In view of the deficiencies of the prior art, the present application provides a follicle planting parameter optimization control system in a micro hair transplantation system, which can effectively solve the problems involved in the above background art.

[0008] To achieve the above purpose, the present application is implemented by the following technical solutions: a follicle planting parameter optimization control system in a micro hair transplantation system, comprising a follicle extraction module, including: a donor area grid fitting sub-module for obtaining three-dimensional surface data of a follicle planting target donor area, performing curved surface fitting on the target donor area, inputting the curved surface fitting result to a differentiable parameterized model, generating a first mixed grid of equal area / equal arc length of the target donor area, the first mixed grid being denoted as a target donor area grid, assigning a first geometric label to each target donor area grid, the first geometric label including the curvature of the target donor area grid and the shortest boundary distance of the target donor area grid; a donor area uniformity constraint sub-module for obtaining the basic feature information of the target donor area grid and setting the uniformity constraint set of the target donor area grid; a candidate grid screening sub-module for obtaining a candidate donor area grid set by screening the target donor area grid based on the uniformity constraint set of the target donor area grid and performing priority sorting, and extracting follicle plants based on the priority sorting.

[0009] The hair follicle planting module comprises a planting area grid fitting sub-module, a planting density generation sub-module, and a second geometry label.

[0010] The hair follicle extraction device configuration module and the hair follicle planting device configuration module are configured, the hair follicle extraction device configuration module is used for updating extraction parameters based on a distribution controller, determining the executable state of the current target donor area grid hair follicle plant extraction process, and correcting the pushing rate of the hair follicle extraction device; the hair follicle planting device configuration module comprises a planting anomaly monitoring sub-module and a hair follicle planting parameter correction sub-module; the planting anomaly monitoring sub-module is used for performing initial parameter configuration on the hair follicle planting device, simultaneously enabling sensing monitoring, planting hair follicles based on hair follicle plant priority, monitoring abnormal conditions in the planting process, and performing planting correction; the hair follicle planting parameter correction sub-module is used for performing density compliance checking on the target planting area grid after completing hair follicle planting, determining whether to immediately correct the hair follicle planting parameters, and completing hair follicle planting parameter optimization control.

[0011] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0012] (1) The hair follicle planting parameter optimization control system in the micro hair transplantation system is provided, the closed-loop control of data, parameters, execution, and feedback is faced, the grid expression of the target donor / planting area and the target density / direction / travel parameters are set, the hair follicle extraction module, the hair follicle planting module, the hair follicle extraction device configuration module, and the hair follicle planting device configuration module are set: the device configuration module is loaded and calibrated to the limit, the speed / advance, the caliber and unit mapping, the sensing threshold and the beat rule of the hair extraction / planting device; the hair follicle extraction module selects the candidate grid and schedules the extraction based on the quota, the heat, the neighborhood uniformity, and the risk agent during the operation period; the hair follicle planting module executes the hole making, implanting, and cooling beat according to the equal area / equal arc length mixed grid and the density interval and the planting unit type mapping, and adjusts the execution deviation; the real-time signals (force / angle / travel / temperature rise / timing) are written back to the parameter library, the beat and the threshold are corrected online, and the adaptive optimization of the device side is realized.

[0013] (2) The application unifies the evaluation of the basic characteristic parameters (such as the existing hair follicle density, curvature, surface roughness, and center adjacent distance) of the target planting area grid, and links with the abnormal score of the hair follicle planting device (composed of real-time deviations such as implantation force, implantation angle, effective stroke / rebound), so that the spatial plantability and process executability can be quantified as controllable parameters simultaneously without changing the tissue properties; the system feeds forward and feedbacks the density target, planting unit type, and beat duty ratio, significantly reduces the quality fluctuation caused by local over-dense / over-dense and direction dispersion, and at the same time suppresses the abnormal trigger frequency caused by thermal and mechanical positive feedback, thereby improving the layout consistency and process stability.

[0014] (3) The same set of geometric and process variables are repeatedly used across modules: curvature, boundary distance, and free space ratio are used for preoperative density correction and aperture mapping, and are also used for candidate screening and non-maximum suppression during operation; execution data deviation is involved in abnormal scoring and directly drives limit compensation, speed reduction, and cooling duty ratio adjustment. Through the through reuse of variables, redundant collection and repeated calculation of multiple source indicators are avoided, parameter inconsistency is reduced, a lightweight implementation path of a small number of key variables and multiple closed loops is realized, so that higher beat stability and interpretability are maintained under the condition of limited computing power and time delay.

[0015] (4) Compared with the scheme that relies on preoperative static grid uniform point laying, linearly maps density with unit hair follicle number, and lacks closed-loop correction during operation, the system replaces the rough plane approximation with an equal area / equal arc length mixed grid and multi-factor density correction, forms a real-time closed loop with the abnormal score and beat scheduling on the device side, and takes the batch ex vivo time as the highest priority to constrain the duty ratio of tissue hole making, implantation, and cooling; thereby ensuring overall efficiency while achieving more uniform density in the supply / receiving area, more consistent direction and stroke, lower abnormal rate and rework probability, and improving parameter traceability and task reusability. BRIEF DESCRIPTION OF DRAWINGS

[0016] The application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the application. For ordinary skilled persons in the art, other drawings can be obtained without creative labor on the basis of the following drawings.

[0017] Figure 1 It is a system module connection diagram of the application.

[0018] Figure 2 It is a curvature-based hair follicle planting parameter change logic diagram.

[0019] Figure 3 It is a hair follicle extraction module flowchart.

[0020] Figure 4A flowchart of a follicle implantation module. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

[0022] To make the technical problems, technical solutions and advantages of the present application clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0023] Referring to Figure 1 The embodiment of the present application provides a technical solution: a follicle implantation parameter optimization control system in a micro-hair transplantation system, comprising a follicle extraction module, a follicle implantation module, a follicle extraction device configuration module, a follicle implantation device configuration module and a micro-hair transplantation information library. The micro-hair transplantation information library is used to store preset values of various parameters.

[0024] The preset relationship stored in the micro-hair transplantation information library includes but is not limited to preset, matching, mapping and other comparison relationships. In this embodiment, the extraction quota fluctuation degree of the target donor area is taken as an example to obtain the number of follicle extraction closed loops by mapping. First, in the library building stage, data samples of multiple batches of historical and simulation tasks are collected, the input side features (such as the extraction quota fluctuation degree of the target donor area, using standard deviation or coefficient of variation and performing dimensionless and abnormal value cleaning) are calculated uniformly, and the output side control quantity (such as the number of follicle extraction closed loops) and its safety / efficiency constraints (uniformity RMSE, abnormal rate, upper limit of beat loss, etc.) are selected; then a comparison relationship between the features and the control quantity is established by piecewise curve fitting with monotonicity constraint or supervised learning (which can also be fixed as a lookup table + linear interpolation), and the hysteresis interval and the upper and lower boundaries (including boundary protection of the minimum / maximum number of closed loops) are determined by grid test and sensitivity analysis on the validation set, and finally the relationship is written into the information library as a version mapping entry. During online operation, the real-time features are normalized, the control quantity is obtained by mapping, and the hysteresis debouncing and out-of-limit rollback strategies are applied to realize traceable closed-loop parameter adjustment. The above process is also applicable to other preset relationships in the library: only the input features and output control quantity need to be replaced, and the data normalization→monotonic fitting / table lookup→constraint verification→version fixation→online table lookup+hysteresis acquisition and application link can be kept.

[0025] The follicle extraction module is connected with the follicle implantation module, the follicle extraction module is connected with the follicle extraction device configuration module, the follicle implantation module is connected with the follicle implantation device configuration module, and the follicle extraction module, the follicle implantation module, the follicle extraction device configuration module and the follicle implantation device configuration module are all connected with the micro-hair transplantation information library.

[0026] The specific process of the follicle extraction module is shown in Figure 3 Figure 3 ​The schematic diagram of the follicle extraction module process is shown. First, candidate screening is performed in the donor area (quota not met, cooling completed, neighborhood not full, and nearest neighbor distance meets the standard), the extraction point is issued, and the extraction is performed once. Real-time collection of torque, temperature rise, and springback forms a transverse proxy, and three branch states are determined and processed: safe → record success; warning → continue after speed reduction / short cooling; strong threshold → replace cooling and replace the grid; then update the extracted amount, heat, and local uniformity, and trigger the recalculation of candidates and priority based on the global N-strands or local minimum batch, and enter the next cycle.

[0027] The follicle extraction module comprises:

[0028] The donor area grid fitting sub-module is used to obtain the three-dimensional surface data of the follicle planting target donor area, perform surface fitting on the target donor area, input the surface fitting result into the differentiable parameterization model, generate a first hybrid grid with equal area / equal arc length of the target donor area, and record the first hybrid grid as the target donor area grid. Assign a first geometric label to each target donor area grid. The first geometric label includes the curvature of the target donor area grid and the shortest boundary distance of the target donor area grid.

[0029] The shortest boundary distance is obtained by marking the boundary of the target donor area as a boundary point set in the same surface coordinate system, and calculating the geodesic shortest distance from the current target donor area grid to the boundary on the triangular mesh surface.

[0030] The above hybrid grid generation is specifically as follows: first, obtain the three-dimensional point cloud of the target donor area using multi-view images or RGB-D, denoise and estimate the normal, perform surface reconstruction / surface fitting to obtain a triangular mesh with a normal, and cut out the ROI; input the surface into the differentiable parameterization model, take the equal area error and the geodesic arc length error along the preset direction field as the joint target, and optimize the parameterization result under the premise of the boundary / inaccessible area as a hard constraint to obtain a distortion-controlled parameterization result; then perform density-weighted manifold sampling / manifold CVT on the surface to approximate the equal area partition, and integrate along the direction field to generate latitude and longitude stripes, so that the geodesic distance between adjacent sampling points tends to be equal arc length; finally, the two are fused into a hybrid grid with equal area / equal arc length (which can be quadrilateralized or anisotropic triangulated), and the local tangent basis, target cell area, and target adjacent geodesic distance of each grid element are output for subsequent direct calling of hole-making-implantation parameter control.

[0031] The donor area uniformity constraint sub-module is used to obtain the basic feature information of the target donor area grid, and set the uniformity constraint set of the target donor area grid.

[0032] The candidate grid screening sub-module is used to screen the target donor area grid based on the uniformity constraint set of the target donor area grid, and obtain a candidate donor area grid set through priority sorting. The follicle plant extraction is performed based on the priority sorting.

[0033] Specifically, a uniformity constraint set of the target donor grid is set, and the specific analysis process is as follows:

[0034] The basic feature information of the target donor grid includes the area of the target donor grid and the reference density target of the target donor grid, wherein the basic feature information can be extracted in the fitting record of the differentiable parameterized model.

[0035] The shortest boundary distance of the target donor grid is matched with the boundary weight factor corresponding to the pre-defined boundary distance interval to determine the interval to which the boundary distance of the target donor grid belongs, and the boundary weight factor corresponding to the interval is obtained.

[0036] The area of the target donor grid, the reference density target of the target donor grid, the curvature of the target donor grid and the boundary weight factor are associated and combined to obtain an upper limit extraction quota of the target donor grid, denoted as.

[0037] In the determination of the upper limit quota of the target donor grid, the higher the existing hair density means that once extraction is extracted, it is easier to form visual contrast and resource overdraft, so the upper limit quota should be relatively adjusted downward; the greater the curvature represents that the local fluctuation and the narrowest window of the instrument are more narrow, the higher the beat fluctuation and the risk of damage, and the upper limit quota should also be adjusted downward. The reason why the upper limit quota can be derived according to the three types of parameters is that they respectively depict the direct influence of extraction on uniformity and safety from three main factors of native resource saturation, geometric operability risk and local space congestion, and can be quantified and normalized to a dimensionless ratio for multiplicative correction of the basic quota. The purpose of introducing the boundary weight factor is to proportionally reduce the quota of the grid near the outer edge of the donor area, the transition zone or the sensitive boundary, to form a distribution of less near the boundary and moderate inside, and to balance the aesthetic transition, accessibility constraints and overall resource conservation.

[0038] The corresponding relationship between the curvature and the upper limit quota can be as shown in Figure 2 , Figure 2 is a curvature-based hair follicle implantation parameter change logic diagram. On a more curved surface, the change amount of the surface normal vector n along the arc length s is approximately Δθ≈k*s (k is the curvature). This has three interlocking effects:

[0039] If the implantation angle tolerance allowed by the equipment is Δθ 容 , the upper limit s max of the step length that can be safely moved each time is approximately Δθ 容 / k. The larger k is, the smaller s max is, which means that the operable window is shorter and more likely to trigger a speed reduction / stopover when the angle threshold is exceeded.

[0040] At large curvature, small deviation of needle axis from local normal will be amplified as deviation of effective implant path from target depth (projection error rises), while normal variation of contact surface leads to fluctuation of pressing force-friction, and torque / warm-up is more likely to be jittered, abnormal score is more likely to be triggered.

[0041] The same planar projection interval will be compressed on curved surface; the neighborhood geodesic area is smaller, and the nearest neighbor distance breaks the threshold faster, so the minimum geodesic interval must be increased or the pace must be slowed down, resulting in pace fluctuation and reduction of available hole sites.

[0042] The upper limit extraction quota of the target donor grid is coupled with the lower limit setting factor predefined in the micro hair transplantation information library, specifically, the upper limit extraction quota of the target donor grid is multiplied by the lower limit setting factor to obtain the lower limit extraction quota of the target donor grid.

[0043] It needs to be explained that the above-mentioned lower limit setting factor is specifically the same limiting factor, because the upper limit extraction quota of the target donor grid has been globally balanced once by curvature, boundary distance, existing hair density and other weights, and then pushed down by a uniform proportion, which is simpler, auditable and traceable; it can avoid parameter drift and overfitting caused by segmented factors (local noise will not be amplified into quota difference), ensure the consistency of caliber between different grids and the low complexity of version management, and facilitate global quota conservation and compliance review; at the same time, with the upper and lower boundary clipping and hysteresis, it can suppress the fluctuation of extreme grids without damaging the stability of the overall distribution, which is more stable from both engineering implementation and regulatory interpretation.

[0044] The extraction amount of each neighborhood of the target donor grid is extracted, wherein the extraction amount can be extracted from the extraction record of the hair follicle extraction device, denoted as the neighborhood extraction amount set of the target donor grid, the variance of the neighborhood extraction amount set is calculated, denoted as the neighborhood uniformity threshold of the target donor grid.

[0045] The upper limit extraction quota of the target donor grid, the lower limit extraction quota of the target donor grid and the neighborhood uniformity threshold of the target donor grid together form the uniformity constraint set of the target donor grid.

[0046] Further, the candidate donor grid set is obtained by target donor grid screening and priority sorting, and the specific analysis process is as follows:

[0047] The target donor grid is screened based on the preset inclusion condition, and all grids that meet the preset inclusion condition are included in the available grid set, wherein the preset inclusion condition is specifically:

[0048] 1) The extraction quota of the current target donor grid is less than the upper limit extraction quota of the target donor grid, which ensures that the total amount of single grid extraction does not exceed the design upper limit, avoiding local resource overdraft and visual patches.

[0049] 2) The cooling timer length of the current target supply area grid is 0, which leaves a beat buffer for the grid that has just been worked, dissipates the tool head temperature rise and the local continuous work caused by the machine loss risk, prevents the risk accumulation caused by continuous cutting, and prevents the temporary unavailability of the grid that has not been cooled. Wait for the timer to return to zero before entering the candidate.

[0050] It needs to be explained that cooling is a beat-based temporary suspension of the current working grid: after the grid completes a certain amount of work or the torque / temperature rise / rebound risk rises, it does not continue to cut, allowing the tool head and drive end temperature to fall, the wetting state to recover, and the local work heat and congestion to decay, and simultaneously clearing the short-term accumulated machine loss and parameter drift. The necessity lies in breaking the positive feedback chain of continuous work → temperature rise accumulation → cutting resistance increase → abnormal rate increase, avoiding the formation of patches and over-sampling caused by concentrated use in the same area. Its beneficial effects are reflected in: reducing the cross-agent proxy and abnormal trigger frequency, stabilizing the mechanical and geometric consistency of implantation / harvesting, improving the overall density uniformity and visual continuity, reducing the speed reduction shutdown and tool change frequency caused by overheating, and facilitating the rearrangement of beats according to batch-off time priority, thereby improving quality and efficiency with instrument and scheduling means without changing the organizational attributes.

[0051] 3) The neighborhood extraction quota of the current target supply area grid is less than the upper limit extraction quota of the neighborhood grid, which suppresses the continued harvesting of the neighborhood that has approached the upper limit from the perspective of the area, preventing area-level over-harvesting and density unevenness; if the neighborhood is over-saturated, the grid is also limited (not selected or reduced in priority).

[0052] 4) The neighborhood uniformity of the current target supply area grid is greater than or equal to the neighborhood uniformity threshold of the target supply area grid, which uses statistical variance to manage distribution smoothing to avoid clustering and striping; once the threshold is exceeded, the grid is cooled / jumped and transferred to other grids for work.

[0053] The neighborhood uniformity in this embodiment refers to the quantity uniformity, which is a dimensionless index of the spatial distribution of the taken / harvestable resources in the neighborhood of the current grid. The neighborhood uniformity can be obtained by standard deviation processing of the taken quantities of each grid in the neighborhood in the extraction record of the hair follicle extraction device.

[0054] Based on the harvestable grid set, the comprehensive priority of a plurality of grids in the harvestable grid set is calculated to obtain the priority score of each harvestable grid, which is sorted from large to small to obtain the priority sequence of the harvestable grid.

[0055] The priority score of each harvestable grid is specifically defined as follows:

[0056] The heat of each harvestable grid, the remaining capacity of each harvestable grid, the sparsity of each harvestable grid, and the machine loss decay coefficient of each harvestable grid are obtained.

[0057] Wherein the heat can be obtained by adding one attenuation method; the remaining amount is obtained by subtracting the amount taken from the single grid upper limit quota; sparsity can be measured by the ratio of the geodesic distance to the nearest taken point to the reference interval, the farther the distance or the higher the free space ratio, the more sparse; the machine loss attenuation coefficient is the risk score of the normalized measurable signals such as motor torque, temperature rise rate, rebound in the set window, and the exponential moving average is taken, and the complementary quantity or the form of time decay is obtained, which is a dimensionless coefficient of risk subsidence degree, which is used to reflect the progress of the grid from high-risk state to recovery.

[0058] In the available grid set, first calculate the priority score of each grid in the 0-1 interval: the lower the heat, the higher the score, the greater the remaining amount, the higher the score, the higher the sparsity, and the machine loss attenuation is insufficient (the risk has not subsided) The score is deducted. The specific method is to normalize the four parameters into comparable dimensionless quantities, linearly weighted or equivalently multiplied by the preset weight to obtain a single score, and set a non-maximum value suppression and a minimum interval threshold for the score. Finally, the next batch of delivery positions are selected in order from high to low score. The priority obtained in this way can take into account whether it has been frequently operated recently, whether there is still quota space in the grid, whether the local area needs to be supplemented due to sparsity, and whether the machine loss risk has been fully attenuated. Therefore, the taking and delivering actions are stably guided to the safe, uniform and more efficient area.

[0059] Based on the priority sequence of the available grid, the candidate supply area grid set is composed by screening from high to low according to the preset number of candidates.

[0060] The follicle implantation module process is specifically as shown in Figure 4 , Figure 4 is a schematic diagram of the follicle implantation module process. Load density → implant unit type (single / dual / multiple), target implantation angle, effective implantation path and hole making: implantation: after the cooling rhythm, calculate the local free space ratio and center adjacent distance according to the current grid, and determine the next round of implantation unit type and rhythm through the modified judgment process; Perform hole making → feeding, real-time calculation of implantation force / angle / effective implantation path-rebound deviation and abnormal score aggregation: if the abnormal score is above the threshold, insert cooling; if the batch is off-site timing critical, freeze new hole making, switch to only implantation until the batch is cleared; After process data is written back, recalculate the neighborhood uniformity, candidate and rhythm according to the global N-strain, and continue the cycle.

[0061] The follicle implantation module comprises:

[0062] The implantation area grid fitting sub-module is used to obtain three-dimensional surface data of the target implantation area, to perform surface fitting on the target implantation area, to input the surface fitting result into the differentiable parameterized model, to generate a second hybrid grid of equal area / equal arc length of the target implantation area, denoted as a target implantation area grid, and to assign a second geometric label.

[0063] The second geometric label includes a curvature of the target planting area grid and a center adjacent distance of the target planting area grid. The center adjacent distance is represented as a geodesic shortest distance from a geometric center (grid center) of the target grid to a nearest taken point along the curved surface. The geodesic distance algorithm is used to obtain the center adjacent distance.

[0064] The planting density generation submodule is configured to obtain basic feature information of the target planting area grid, evaluate a basic feature parameter of the target planting area grid, obtain a target planting density of the target planting area grid, and generate a planting density target field.

[0065] Specifically, the planting density target field is generated, and the specific analysis process is as follows:

[0066] The basic feature information of the target planting area grid includes an existing follicle density of the target planting area grid and a surface roughness of the target planting area grid. The basic feature information can be extracted from the fitting record of the microparameterized model.

[0067] The existing follicle density of the target planting area grid, the surface roughness of the target planting area grid, the curvature of the target planting area grid, and the center adjacent distance of the target planting area grid are respectively preprocessed by normalization and unitization, and an influence factor is introduced to aggregate the preprocessing results by weighting, to obtain a basic feature parameter of the target planting area grid.

[0068] The specific analysis process is as follows:

[0069] ;

[0070] In the formula, BC is the basic feature parameter of the target planting area grid, De is the existing follicle density of the target planting area grid, Rq is the surface roughness of the target planting area grid, K is the curvature of the target planting area grid, Dn is the center adjacent distance of the target planting area grid, b1 is an influence factor corresponding to the existing follicle density in the micro-hair information library, b2 is an influence factor corresponding to the surface roughness in the micro-hair information library, b3 is an influence factor corresponding to the curvature in the micro-hair information library, and b4 is an influence factor corresponding to the center adjacent distance in the micro-hair information library.

[0071] In this embodiment, through the existing hair follicle density, surface roughness, curvature and center adjacent distance multivariate analysis, specifically considering the correlation between these parameters, the existing hair follicle density and the center adjacent distance are inversely coupled in space (the more dense the local area, the smaller the nearest neighbor distance; the greater the distance, which means that the local area is more sparse), while the curvature and surface roughness play a regulating / confidence role, directly affecting the interpretation and threshold setting of the former two, the greater the curvature, the more the equal-area projection distortion and the narrower the reachable angle window, which will amplify the subjective perception of the dense area and require the nearest neighbor distance threshold to be appropriately widened; the higher the roughness, the greater the uncertainty of image counting and positioning, and the confidence in the existing density should be reduced and the distance judgment should be more conservative (increase the minimum distance and reduce the target density).

[0072] The base characteristic parameter of the target planting area grid is matched with the planting density correction factor corresponding to each base characteristic parameter interval to determine the specific interval of the base characteristic parameter of the target planting area grid, and the planting density correction factor corresponding to the interval is obtained, which is recorded as the specified planting density correction factor.

[0073] It should be noted that the planting density correction factor and the base characteristic parameter are in a positive contrast relationship. When the target planting area is denser, rougher and has a larger curvature, the base characteristic parameter of the target planting area grid is smaller, and the planting density correction factor is smaller, so that the system proportionally reduces the target density to inhibit over-density and abnormality; when the adjacent distance is farther and the space is more abundant, the base characteristic parameter of the target planting area grid is larger, and the planting density correction factor is larger, so that the target density is moderately adjusted to balance the distribution. The base characteristic parameter is mapped to a dimensionless and auditable density adjustment quantity to ensure adaptive control of difficult places being conservative and easy places being relaxed, reduce patching and beat fluctuation, and improve overall uniformity and reproducibility.

[0074] The specified planting density correction factor is combined with the preset reference planting density, specifically by multiplying the specified planting density correction factor and the reference planting density, to obtain the target planting density of the target planting area grid, and a planting density target field is generated based on the target planting density of each target planting area grid.

[0075] The hair follicle extraction device configuration module and the hair follicle planting device configuration module comprise:

[0076] It should be explained that in the embodiment of the present application, the hair follicle extraction device includes an electric FUE puncher (direct current / brushless drive, rotary / reciprocal / hybrid motion switchable), a handle type hair extractor with a replaceable punching sleeve, a displacement code, and a pressure / torque sampler, or a collaborative robot with a similar end effector; and a knife head temperature sensor and a disposable flow channel are selected.

[0077] The follicle extraction device configuration module is configured to update extraction parameters based on the distribution controller, determine the executable state of the current target donor grid follicle plant extraction process, and correct the pushing rate of the follicle extraction device.

[0078] The follicle planting device configuration module includes a planting anomaly monitoring submodule and a follicle planting parameter correction submodule.

[0079] It should be explained that in the present embodiment, the planting device can be a microneedle system: the outer sleeve determines the planting unit type (mapped to single / dual / multi-hair units), the inner push rod (piston) sends in the follicle, the pen body contains a limit ring and an angle guide ruler; it is recommended to integrate force / displacement encoding, IMU / angle sensing and tool bit temperature, support disposable syringes and quick-change aperture (small / medium / large three gears).

[0080] The planting anomaly monitoring submodule is configured to perform initial parameter configuration on the follicle planting device, enable sensing monitoring, perform follicle planting based on follicle plant priority, and monitor abnormal conditions during planting to correct planting.

[0081] The follicle planting parameter correction submodule is configured to perform density compliance checking on the target planting area grid where the follicle planting is completed, determine whether to immediately correct the follicle planting parameters, and complete the optimization control of the follicle planting parameters.

[0082] Further, the pushing rate of the follicle extraction device is corrected, and the specific analysis process is as follows:

[0083] Based on the upper limit extraction quota of each target donor grid, the extraction quota fluctuation degree of the target donor grid is obtained by standard deviation processing, and the number of follicle extraction closed loops is mapped.

[0084] The number of follicle extraction closed loops refers to the minimum job batch size that the controller performs local evaluation→reconstruction of candidates→parameter adjustment / change of grid once every N plants in the target donor area; it defines the small cycle length of the taking and sending scheduling, and is used to limit N in the local uniformity index that is updated once every N plants, so that the system periodically checks the taken distribution, the nearest neighbor distance and the risk score according to the batch size, and triggers cooling, taking prohibition or batch rearrangement, so as to embed uniformity and risk control into the closed loop with fixed frequency.

[0085] The standard deviation (or coefficient of variation) of the upper limit extraction quota of each grid characterizes the supply area quota heterogeneity / variability: the greater the variability, the greater the spatial difference in the extraction allowed by different grids, the higher the risk of local over-extraction and patching, and the shorter the closed loop (N is smaller, and the evaluation is more frequent); the smaller the variability, the more uniform the quota, the easier it is to self-balance the short-term deviation, and the longer the closed loop (N is larger, and the scheduling overhead is reduced). Therefore, the monotonic mapping of the variability of the upper limit quota to the number of closed loops (with upper and lower limits and hysteresis anti-jitter) can convert the discrete degree of global available resources into the beat frequency, and self-consistently support the adaptive setting of updating once every N strains.

[0086] When the cumulative number of follicle extractions of the current target donor grid meets the number of follicle extraction closed loops, the extraction parameter is updated, and the specific process is as follows:

[0087] If the cumulative number of follicle extractions does not meet the number of follicle extraction closed loops, it is a safe state and continues to execute.

[0088] The extraction quota of the current target donor grid and the extraction quota of the neighboring grid are obtained, wherein the extraction quota can be extracted from the extraction record of the follicle extraction device, and the local uniformity index of the current target donor grid is obtained by variance processing.

[0089] The geodesic nearest neighbor distance of the current target donor grid is obtained, wherein the geodesic nearest neighbor distance represents the nearest distance from the center of the current target donor grid to the nearest taken point.

[0090] The execution data of the follicle extraction device is obtained, wherein the execution data can be extracted from the execution record of the follicle extraction device, including the motor torque of the follicle extraction device, the temperature rise rate of the tool bit of the follicle extraction device, and the instantaneous rebound of the follicle extraction device, and the transverse rate proxy score of the follicle extraction device is obtained by joint determination.

[0091] The transverse rate proxy score is a real-time quantitative score of the possibility of the follicle being cut transversely during the extraction process. It is not equal to the true transverse rate obtained by post-microscopic statistics, but a risk index generated intraoperatively based on measurable physical signals.

[0092] When the local uniformity index of the current target donor grid is greater than the local uniformity index threshold, or the geodesic nearest neighbor distance of the current target donor grid is less than the geodesic nearest neighbor distance threshold, it is a warning state, the current target donor grid enters cooling, the cooling timer length of the current target donor grid is set to the cooling waiting preset time length, and the next grid is selected for operation from the candidate donor grid set.

[0093] If the shortest distance from the current grid to the nearest surface where the position has been taken is too small, it means that the local area is becoming crowded, and continuing this operation will lead to clustering of hole sites, compression of the nearest neighbor distance, and thus magnification of thermal and mechanical accumulation (rise in torque / temperature fluctuation), triggering more cooling / shutdown, and forming patches on a macroscopic scale. With this threshold as the minimum legal distance, the scheduler can directly exclude or reduce the priority of the grid, move to a sparser grid, or delay the backfill, thereby maintaining uniform taking / planting on the surface scale, stabilizing the rhythm, and reducing the risk of abnormal scoring and over-taking.

[0094] When the transverse rate proxy score of the follicle extraction device is greater than the transverse rate proxy score threshold, it is a pre-warning state, the transverse rate proxy score and the transverse rate proxy score threshold are processed by difference, the risk score difference of the follicle extraction device is obtained, the extraction rate correction factor is matched to correct the current extraction rate of the follicle extraction device.

[0095] Specifically, the extraction rate correction factor is multiplied by the current extraction rate of the follicle extraction device to obtain the adaptive extraction rate of the follicle extraction device, and the next closed-loop process is configured.

[0096] When the transverse rate proxy score of the follicle extraction device is greater than the transverse rate proxy score threshold, it is a strong threshold state, prompting to replace the current cutting head of the follicle extraction device, the follicle extraction device waits for a replacement confirmation signal, and selects the next grid for operation from the candidate donor grid set again, while the current target donor grid enters cooling, and the cooling timer length of the current target donor grid is set to a cooling waiting preset time length.

[0097] It needs to be explained that the transverse rate proxy score threshold is less than the transverse rate proxy score threshold.

[0098] The target donor grid entering cooling is automatically recoverable when the cooling timer length is 0.

[0099] Specifically, the abnormal situation in the planting process is monitored for planting correction, and the specific analysis process is as follows:

[0100] According to the target planting density field, generate planting list initial configuration parameters for each target planting area grid, including the planting unit type of the target planting area grid, the initial implantation angle of the target planting area grid, the initial implantation force of the follicle planting device, the initial instrument temperature of the follicle planting device, and the effective implantation path length of the follicle planting device. The follicle planting device performs a follicle planting task according to the planting list.

[0101] The planting unit type includes single hair type, double hair type, and multiple hair type.

[0102] The target density corresponding to the target planting area grid is divided into several density intervals, each interval binds a group of instrument aperture and a group of implant unit type combination, so as to automatically select the matching opening hole specification and implant unit under different density demands. When the target density of the grid falls in the first interval, the type with small aperture and single hair unit is matched in priority to ensure the direction and healing; when the density is in the second interval, the type with medium aperture and double hair is matched in priority to balance the efficiency and uniformity; when the density is in the third interval, the type with large aperture and multiple hair is matched in priority to reduce the number of hole sites and improve the layout continuity; although the target density near the boundary or the grid with high curvature / high roughness may be high, the type rule can be forced to fall back to a more conservative aperture and unit combination to maintain the smoothness of the transition zone and the construction accessibility. In this way, the mapping of density interval to implant unit type converts the abstract density target into executable instrument specification and unit combination, which is convenient for direct calling in scheduling and closed loop.

[0103] The off-body time length of the hair follicle plant, the remaining target number of the grid of the target planting area, and the initial instrument temperature of the hair follicle planting device are extracted to jointly calculate the hair follicle plant priority, and a drilling-implantation beat adjustment element is obtained based on the mapping of the hair follicle plant priority to modulate the real-time drilling-implantation beat of the hair follicle planting device.

[0104] The off-body time length of the hair follicle plant can be obtained by calculating the difference between the extraction time point obtained from the extraction record of the hair follicle extraction device and the current time point.

[0105] Specifically, the drilling-implantation beat adjustment element is multiplied by the real-time drilling-implantation beat of the hair follicle planting device to obtain the adaptive drilling-implantation beat of the hair follicle planting device, and the execution process of the next node hair follicle planting device is configured.

[0106] The drilling-implantation beat refers to a rhythm rule in which drilling (hole making) and implantation (sending in hair follicles) are alternately performed in adaptive proportion of time and quantity in the planting area operation, i.e. a scheduling method of time allocation / process switching. It specifies how many holes are drilled first and how many plants are implanted in each round (such as m:n or drilling:implantation=1:2), how long each section lasts, when to switch and pause, and allows automatic shortening or lengthening of the duty cycle of a section according to real-time signals (off-body timing, temperature rise, abnormal score, local density deviation): for example, when approaching the upper limit of off-body, the beat is cut to only implantation (0:1), when the temperature rise is high, the drilling rate is reduced, and the cooling slot is inserted. Through beat control, efficiency can be stabilized, thermal-mechanical cumulative risk can be limited, and spatial density and direction consistency can be maintained.

[0107] It needs to be explained that when any batch of hair follicle plant is detected to approach the set upper limit in vitro (that is, the remaining safety window is lower than the threshold value), the scheduler triggers the critical batch priority process: immediately freeze new zone hole instruction (pause new hole and new batch into the queue), mark the batch as critical batch and promote to the highest priority of the queue, lock its tray channel and corresponding grid window, beat switch to only implantation mode (hole: implantation = 0:1), moderately increase the number of implants per unit time and the cooling duty cycle under the premise of unchanged angle / depth / spacing guardrails to stabilize the temperature rise and abnormal rate, and continuously digest until the batch is cleared or the remaining safety window returns to the safe interval, then unfreeze and restore the regular hole ↔ implantation alternate beat, so as to avoid quality dispersion caused by in vitro timeout and ensure controllable global beat.

[0108] Obtain execution data of the hair follicle planting device during the planting process, and establish an abnormal score of the hair follicle planting device;

[0109] The abnormal score of the hair follicle planting device is compared with the pre-defined abnormal score threshold value. When the abnormal score of the hair follicle planting device is greater than the abnormal score threshold value, the abnormal score of the hair follicle planting device is processed by difference with the abnormal score threshold value to obtain an abnormal score deviation of the hair follicle planting device. A number of implants correction factor is matched to correct the real-time number of implants per unit time of the hair follicle planting device, and a preset hair follicle planting device cooling beat is inserted based on the real-time hole-implantation beat of the hair follicle planting device.

[0110] In the adaptive hole-implantation beat of the hair follicle planting device, the preset hair follicle planting device cooling beat is inserted. In the beat control running at a predetermined hole: implantation duty cycle, the abnormal score is continuously monitored. Once the preset threshold value is crossed, the scheduler rewrites the original beat to hole: implantation: cooling = m:n:c at the next beat switching point (or immediately) to insert a cooling time slot (such as a speed reduction idle stroke, a liquid wetting such as a spray drop, a wind / conductive fin heat dissipation, a short stop reset), and automatically exits the cooling to restore the normal beat with a hysteresis condition (temperature falls below the threshold, abnormal score falls below the alarm line, minimum cooling time meets); This process does not change the hole site parameters and density target, but only reallocates the process duty cycle in the time dimension to suppress thermal-mechanical accumulation, reduce abnormal triggering and tool changing frequency, and consider in vitro window and job quality in the critical batch priority scenario.

[0111] Further, the abnormal score of the hair follicle planting device is established, and the specific analysis process is:

[0112] The execution data of the hair follicle planting device during the planting process includes the real-time implantation force of the hair follicle planting device, the real-time implantation angle of the hair follicle planting device, and the real-time rebound stroke of the hair follicle planting device. The execution data can be extracted from the execution record of the hair follicle planting device.

[0113] extracting the deviation processing result, the deviation processing result including a deviation processing result between the real-time implant force of the follicle implantation device and the initial implant force of the follicle implantation device, a deviation processing result between the real-time implant angle of the follicle implantation device and the initial implant angle of the target implantation area grid, and a deviation processing result between the effective implant path length of the follicle implantation device and the real-time rebound stroke of the follicle implantation device.

[0114] normalizing and pre-processing the deviation processing result, introducing an influence factor, and weighting and aggregating the pre-processing result to obtain an abnormal score of the follicle implantation device.

[0115] The specific establishment process is as follows:

[0116] ;

[0117] In the formula, AS is the abnormal score of the follicle implantation device, F is the real-time implant force of the follicle implantation device, F' is the initial implant force of the follicle implantation device, is the real-time implant angle of the follicle implantation device, is the initial implant angle of the target implantation area grid, d is the real-time rebound stroke of the follicle implantation device, d' is the effective implant path length of the follicle implantation device, p1 is the influence factor corresponding to the implant force pre-defined in the micro-hair information library, p2 is the influence factor corresponding to the implant angle pre-defined in the micro-hair information library, and p3 is the influence factor corresponding to the rebound stroke pre-defined in the micro-hair information library.

[0118] It needs to be explained that the above-mentioned deviation processing represents the degree of drift between the parameters in the actual implantation process and the initial parameters. The drift of the instrument parameters in the actual implantation process relative to the initial setting is converted into a quantifiable error signal, and the fine adjustment and protection action is triggered within the beat, so as to block the error at the moment of occurrence rather than after the correction. When the deviation accumulates to the warning band, the system automatically adjusts the limit and guide, reduces the pressure / speed or inserts cooling for a short time; when the strong threshold is touched, it immediately stops-retreats-replaces and rearranges the queue; at the same time, the deviation and adjustment trajectory are written into the record for subsequent threshold self-learning and parameter package iteration. In this way, the consistency and repeatability of the instrument output can be continuously stabilized, the spatial dispersion of density and direction is inhibited, the abnormal triggering and beat fluctuation is reduced, and the process quality related to survival is improved, without changing any tissue intrinsic properties.

[0119] In this embodiment, multivariate analysis is performed by real-time implant force deviation, real-time implant angle deviation, and effective implant path length deviation. Specifically, the correlation between these parameters is considered. The three are not independent in the same abnormal score, but are coupled with each other to amplify or interpret each other: when the implant force deviation and the implant angle deviation increase at the same time, it usually means that the needle tip is not collinear with the hole axis, causing the cutting / extrusion resistance to rise, and the risk should be amplified by a multiplicative or weighted coupling term; if the angle is normal and the force is large, it is more likely to be a blunt edge or poor local accessibility, so the angle term should not be excessively penalized. The deviation of the effective implant path length and the rebound stroke describes the geometric consistency of where to go and whether to be bounced back: if the effective stroke is insufficient and the rebound is large, it is usually caused by the ejection of the top / bottom / overpressure, and the weight should be increased together with the force deviation; if the effective stroke exceeds and the rebound is small, it may be a diagonal insertion caused by angle deviation, and the risk should be increased together with the angle term.

[0120] Specifically, it is determined whether to immediately correct the hair follicle implant parameters, and the specific analysis process is as follows:

[0121] After the target implant area grid is completed, the actual local free space ratio of the target implant area grid is extracted and compared with the local free space ratio permissible interval. If the actual local free space ratio of the target implant area grid belongs to the local free space ratio permissible interval, it is determined that the hair follicle implant parameters do not need to be immediately corrected, otherwise, it is determined that the hair follicle implant parameters need to be immediately corrected:

[0122] The local free space ratio is expressed as the proportion of space in which a new hole can be placed around the center of the grid. Intuitively, it is the proportion of space in which a new hole can be reasonably placed in this small area after considering the minimum hole spacing, forbidden / boundary buffer, and occupied hole / implanted space. The theoretical maximum number of points that can be accommodated can be estimated by Poisson disk sampling, and the number of new points that can still be placed is obtained by sampling with the same radius under the current constraints (occupied space / forbidden space / minimum spacing). The theoretical maximum number of points that can be accommodated and the number of new points that can still be placed are compared to obtain the ratio.

[0123] If the actual local free space ratio of the target implant area grid is less than the minimum value of the local free space ratio permissible interval, the next round of hair follicle implant unit type of the target implant area grid is updated to double hair type, and if the actual local free space ratio of the target implant area grid is less than the local free space ratio, the next round of hair follicle implant unit type of the target implant area grid is configured to multiple hair type.

[0124] It needs to be explained that the above local free space ratio is less than the minimum value of the local free space ratio permissible interval.

[0125] If the actual local free space ratio of the target planting area grid is greater than the maximum value of the local free space ratio permission interval, the next round of follicle planting unit type of the target planting area grid is configured as a single hair type.

[0126] During the planting process, the instrument temperature of the follicle planting device is monitored in real time. If the instrument temperature of the follicle planting device at the current time point is greater than the instrument temperature of the follicle planting device at the previous time point, and the duration satisfies the temperature rise permission duration, the real-time drilling-implantation rhythm of the follicle planting device is down-regulated to the drilling-implantation defined rhythm until the instrument temperature of the follicle planting device at the current time point is less than the instrument temperature of the follicle planting device at the previous time point.

[0127] The above is only an example and description of the structure of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace, as long as the modifications or supplements do not deviate from the structure of the present application or exceed the scope defined by the present application, which shall belong to the protection scope of the present application.

Claims

1. A system for optimizing control of follicular unit extraction parameters in a micro hair transplantation system, comprising a follicular unit extraction module, characterized in that, Comprise: The donor area grid fitting sub-module is used for acquiring three-dimensional surface data of a follicle transplantation target donor area, performing surface fitting on the target donor area, inputting the surface fitting result into a differentiable parameterized model, generating a first mixed grid of equal area / equal arc length of the target donor area, the first mixed grid is recorded as a target donor area grid, a first geometric label is given to each target donor area grid, the first geometric label comprises a curvature of the target donor area grid and a shortest boundary distance of the target donor area grid; The donor area uniformity constraint sub-module is used for acquiring basic feature information of the target donor area grid, and setting a uniformity constraint set of the target donor area grid; The candidate grid screening sub-module is used for screening and priority sorting of the target donor area grid to obtain a candidate donor area grid set based on the uniformity constraint set of the target donor area grid, and follicle plant extraction is performed based on the priority sorting; The setting of the uniformity constraint set of the target donor area grid comprises the following specific analysis process: The basic feature information of the target donor area grid comprises an area of the target donor area grid and a reference density target of the target donor area grid; The shortest boundary distance of the target donor area grid is matched with a boundary weight factor corresponding to a predefined boundary distance interval to determine the interval to which the boundary distance of the target donor area grid belongs, and the boundary weight factor corresponding to the interval is obtained; The area of the target donor area grid, the reference density target of the target donor area grid, the curvature of the target donor area grid and the boundary weight factor are combined to obtain an upper limit extraction quota of the target donor area grid, which is recorded as; The upper limit extraction quota of the target donor area grid is coupled with a predefined lower limit setting factor to obtain a lower limit extraction quota of the target donor area grid; Each neighborhood extraction amount of the target donor area grid is extracted, recorded as a neighborhood extraction amount set of the target donor area grid, the variance of the neighborhood extraction amount set is calculated, and recorded as a neighborhood uniformity threshold of the target donor area grid; The upper limit extraction quota of the target donor area grid, the lower limit extraction quota of the target donor area grid and the neighborhood uniformity threshold of the target donor area grid jointly constitute the uniformity constraint set of the target donor area grid.

2. The system according to claim 1, wherein the system is characterized by: The candidate donor area grid set is obtained by screening and priority sorting of the target donor area grid, and the specific analysis process comprises the following steps: The target donor area grid is screened based on a preset inclusion condition, and all grids meeting the preset inclusion condition are included in a acceptable grid set, wherein the preset inclusion condition specifically comprises: 1) The extraction quota of the current target donor area grid is less than the upper limit extraction quota of the target donor area grid; 2) The cooling timer length of the current target donor area grid is 0; 3) The neighborhood extraction quota of the current target donor area grid is less than the upper limit extraction quota of the neighborhood grid; 4) The neighborhood uniformity of the current target donor area grid is greater than or equal to the neighborhood uniformity threshold of the target donor area grid; Based on the acceptable grid set, the comprehensive priority of a plurality of grids in the acceptable grid set is calculated to obtain the priority score of each acceptable grid in descending order, and an acceptable grid priority sequence is obtained; Based on the acceptable grid priority sequence, a preset number of candidate grids are filtered from high to low in sequence to form a candidate donor area grid set.

3. A system for optimizing control of hair follicle implantation parameters in a micro hair transplantation system, comprising a hair follicle implantation module, characterized in that: Comprise: The planting area grid fitting sub-module is configured to obtain three-dimensional surface data of a target planting area, perform surface fitting on the target planting area, input the surface fitting result into a differentiable parameterized model, generate a second hybrid grid of equal area / equal arc length of the target planting area, denoted as a target planting area grid, and assign a second geometric label to the target planting area grid; The second geometric label includes a curvature of the target planting area grid and a center adjacent distance of the target planting area grid; The planting density generation sub-module is configured to obtain basic feature information of the target planting area grid, evaluate a basic feature parameter of the target planting area grid, obtain a target planting density of the target planting area grid, and generate a planting density target field; The specific analysis process of generating the planting density target field is as follows: The basic feature information of the target planting area grid includes an existing hair follicle density of the target planting area grid and a surface roughness of the target planting area grid; The existing hair follicle density of the target planting area grid, the surface roughness of the target planting area grid, the curvature of the target planting area grid, and the center adjacent distance of the target planting area grid are respectively pre-processed by normalization and unitization, an influence factor is introduced, and the pre-processed results are weighted and aggregated to obtain the basic feature parameter of the target planting area grid; The basic feature parameter of the target planting area grid is matched with a planting density correction factor corresponding to a predefined basic feature parameter interval to determine a specific interval of the basic feature parameter of the target planting area grid, obtain a planting density correction factor corresponding to the interval, denoted as a specified planting density correction factor; The specified planting density correction factor is associated with a preset reference planting density to obtain the target planting density of the target planting area grid, and a planting density target field is generated based on the target planting density of each target planting area grid.

4. The micro hair transplant system with optimized control of hair follicle implantation parameters according to claim 3, characterized in that: The hair follicle extraction device configuration module and the hair follicle planting device configuration module are provided, and the hair follicle extraction device configuration module is configured to perform extraction parameter updating based on a distribution controller, determine an executable state of a current target supply area grid hair follicle plant extraction process, and correct a pushing speed of the hair follicle extraction device. The hair follicle planting device configuration module includes a planting anomaly monitoring sub-module and a hair follicle planting parameter correction sub-module. The planting anomaly monitoring sub-module is configured to perform initial parameter configuration on the hair follicle planting device, enable sensing monitoring at the same time, perform hair follicle planting based on a hair follicle plant priority, monitor abnormal conditions in the planting process, and perform planting correction. The hair follicle planting parameter correction sub-module is configured to perform density compliance checking on a target planting area grid where hair follicle planting is completed, determine whether to immediately correct hair follicle planting parameters, and complete hair follicle planting parameter optimization control. The specific analysis process of correcting the pushing speed of the hair follicle extraction device is as follows:

5. The system according to claim 4, wherein the system is characterized by: Based on the upper limit extraction quota of each target supply area grid, a standard deviation is processed to obtain an extraction quota fluctuation degree of the target supply area, and a number of hair follicle extraction closed loops is mapped. When the number of hair follicle extraction closed loops is met by the cumulative number of hair follicle extractions of the current target supply area grid, the extraction parameter updating is performed, and the specific process is as follows: ​ Obtain the extraction quota of the current target donor grid and the extraction quota of the neighborhood grid, perform variance processing to obtain the local uniformity index of the current target donor grid; Obtain the geodesic nearest neighbor distance of the current target donor grid; Obtain the execution data of the hair follicle extraction device, including the motor torque of the hair follicle extraction device, the cutter head temperature rise rate of the hair follicle extraction device, and the instantaneous rebound of the hair follicle extraction device, to jointly determine the hair follicle extraction device's transverse rate proxy score; When the local uniformity index of the current target donor grid is greater than the local uniformity index threshold, or the geodesic nearest neighbor distance of the current target donor grid is less than the geodesic nearest neighbor distance threshold, the current target donor grid enters cooling, the cooling timer duration of the current target donor grid is set to the cooling waiting preset duration, and the next grid job is selected from the candidate donor grid set again. When the hair follicle extraction device's transverse rate proxy score is greater than the transverse rate proxy score threshold, the difference between the transverse rate proxy score and the transverse rate proxy score threshold is processed to obtain the hair follicle extraction device's risk score difference, and the extraction rate correction factor is matched to correct the current extraction rate of the hair follicle extraction device. When the hair follicle extraction device's transverse rate proxy score is greater than the transverse rate proxy threshold, a prompt to replace the current cutter head of the hair follicle extraction device is given, the hair follicle extraction device waits for a replacement confirmation signal, the next grid job is selected from the candidate donor grid set again, and the current target donor grid enters cooling, with the cooling timer duration of the current target donor grid being set to the cooling waiting preset duration. The target donor grid that enters cooling automatically resumes the candidate function when the cooling timer duration is 0.

6. The system according to claim 4, wherein the system is characterized by: The abnormal situation in the planting process is monitored and corrected, and the specific analysis process is as follows: According to the planting density target field, generate planting list initial configuration parameters for each target planting area grid, including the planting unit type of the target planting area grid, the initial implantation angle of the target planting area grid, the initial implantation force of the hair follicle planting device, the initial instrument temperature of the hair follicle planting device, and the effective implantation path length of the hair follicle planting device, and the hair follicle planting device performs the hair follicle planting task according to the planting list; The planting unit type includes single hair type, double hair type, and multiple hair type; Extract the hair follicle plant ex vivo duration, the remaining target number of the target planting area grid, and the initial instrument temperature of the hair follicle planting device, and jointly calculate the hair follicle plant priority, and map the hair follicle plant priority to obtain the drilling-implantation beat adjustment element to modulate the real-time drilling-implantation beat of the hair follicle planting device; Obtain the execution data of the hair follicle planting device during the planting process, and establish the abnormal score of the hair follicle planting device; Compare the abnormal score of the hair follicle planting device with the predefined abnormal score threshold. When the abnormal score of the hair follicle planting device is greater than the abnormal score threshold, the difference between the abnormal score of the hair follicle planting device and the abnormal score threshold is processed to obtain the abnormal score deviation of the hair follicle planting device, and the implantation number correction factor is matched to correct the real-time implantation number per unit time of the hair follicle planting device, and the preset hair follicle planting device cooling beat is inserted based on the real-time drilling-implantation beat of the hair follicle planting device.

7. The system according to claim 6, wherein the system is characterized by: The abnormal score of the follicle planting device is established, and the specific analysis process is as follows: The execution data of the follicle planting device during the planting process includes the real-time implantation force of the follicle planting device, the real-time implantation angle of the follicle planting device, and the real-time rebound stroke of the follicle planting device; The deviation processing result is extracted, which includes the deviation processing result between the real-time implantation force of the follicle planting device and the initial implantation force of the follicle planting device, the deviation processing result between the real-time implantation angle of the follicle planting device and the initial implantation angle of the target planting area grid, and the deviation processing result between the effective implantation path length of the follicle planting device and the real-time rebound stroke of the follicle planting device; The deviation processing result is normalized and pre-processed, the influence factor is introduced, and the pre-processed result is weighted and aggregated to obtain the abnormal score of the follicle planting device.

8. The system according to claim 4, wherein the system is characterized by: The specific analysis process of determining whether to immediately correct the follicle planting parameters is as follows: After the target planting area grid completes follicle planting, the actual local free space ratio of the target planting area grid is extracted and compared with the local free space ratio allowable interval. If the actual local free space ratio of the target planting area grid belongs to the local free space ratio allowable interval, it is determined that the follicle planting parameters do not need to be immediately corrected, otherwise, it is determined that the follicle planting parameters need to be immediately corrected: If the actual local free space ratio of the target planting area grid is less than the minimum value of the local free space ratio allowable interval, the next round of follicle planting unit type of the target planting area grid is updated to double hair type, and if the actual local free space ratio of the target planting area grid is less than the local free space ratio, the next round of follicle planting unit type of the target planting area grid is configured to multiple hair type; If the actual local free space ratio of the target planting area grid is greater than the maximum value of the local free space ratio allowable interval, the next round of follicle planting unit type of the target planting area grid is configured to single hair type; During the planting process, the instrument temperature of the follicle planting device is monitored in real time. If the instrument temperature of the follicle planting device at the current time point is greater than the instrument temperature of the follicle planting device at the previous time point, and the duration satisfies the temperature rise allowable duration, the real-time drilling-implantation rhythm of the follicle planting device is down-regulated to the drilling-implantation defined rhythm until the instrument temperature of the follicle planting device at the current time point is less than the instrument temperature of the follicle planting device at the previous time point.

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