Multi-target generation system and method for implantable biodegradable hydrogel sustained-release carriers

Abstract

This invention discloses a multi-objective generation system and method for implantable biodegradable hydrogel sustained-release carriers in the biomedical field. By constructing a systematic generation architecture that integrates material component gradient design, dynamic modeling of mechanical response, and closed-loop regulation of biocompatibility, and through the synergistic effect of a triple mechanism of molecular-level crosslinking network topology reconstruction, degradation kinetic parameter coupling optimization, and active adaptation of the interface microenvironment, the degradation rate, structural strength maintenance ability, and tissue integration performance of the hydrogel are simultaneously and precisely controlled during its in vivo service life. This overcomes the technical defects of existing technologies, such as premature softening failure or aggravated local inflammatory response caused by single-dimensional regulation. It realizes the gradient design of the internal structure of the hydrogel from macro to micro, enabling the material to autonomously adjust its stiffness distribution according to the local stress state when subjected to complex physiological loads, avoiding the sudden mechanical collapse caused by the overall degradation of traditional homogeneous hydrogels.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, and in particular to a multi-target generation system and method for implantable biodegradable hydrogel sustained-release carriers. Background Technology

[0002] With the increasing application of biomedical materials in tissue engineering, drug sustained release, and minimally invasive implantation, biodegradable hydrogels, as a type of intelligent carrier combining biocompatibility and structural plasticity, are demonstrating their growing clinical value. Traditional hydrogel carrier design largely relies on empirical formulation and static performance testing, with its core construction logic based on single-objective optimization, such as matching degradation half-life or achieving initial modulus targets. However, after implantation, materials face a complex physiological microenvironment: the combined effects of local enzyme activity, mechanical stress, immune response, and tissue regeneration rate lead to high spatiotemporal heterogeneity in material properties. Empirical designs, lacking systematic modeling of the synergistic relationship between degradation kinetics, mechanical maintenance capacity, and tissue compatibility, often result in premature softening and loss of support function, or residual fragments inducing chronic inflammatory responses, severely limiting their safe application in load-bearing sites or long-term sustained-release scenarios. Furthermore, different implantation sites (such as subcutaneous, joint cavity, and brain parenchyma) exhibit significant differences in the mechanical threshold, degradation window, and immune tolerance boundary of materials, making it difficult for existing methods to achieve cross-scenario parameter adaptation and precise output of surgical specifications.

[0003] Among them, the multi-objective generation method for implantable biodegradable hydrogel sustained-release carriers focuses on constructing a dynamic mapping relationship between material properties and physiological needs. This method aims to establish a material generation model oriented towards clinical endpoints by quantifying the coupling constraints between degradation rate curves, modulus decay trajectories, and cytotoxicity thresholds. Its basic principle is to map tunable parameters such as material composition, crosslinking density, and pore structure into multi-dimensional objective terms in an energy function, and introduce a tissue compatibility safety window as a hard boundary condition, thereby searching for the optimal solution set in the material space that satisfies robustness across multiple scenarios.

[0004] Existing technologies generally suffer from three structural defects in achieving the above goals: First, degradation models and mechanical models are disconnected, and no residual synergistic optimization mechanism has been established, leading to a significant deviation between the actual in vivo performance and in vitro predictions. Second, compatibility assessments are mostly limited to static cytotoxicity tests, lacking threshold constraints on key immune indicators such as dynamic release of inflammatory factors and macrophage polarization, thus failing to construct a true biosafety boundary. Third, the generation process does not incorporate prior knowledge of the implantation site, such as requiring a modulus of >50 kPa for 8 weeks in load-bearing areas and a molecular weight of <500 Da for degradation products in brain regions, resulting in a lack of clinical suitability for candidate materials. These problems are particularly prominent in high-requirement scenarios such as osteochondral repair and nerve conduit implantation, directly leading to increased implantation failure rates and a heightened risk of secondary surgery. There is an urgent need to establish an intelligent generation system that integrates dynamic modeling, safety window constraints, and scenario-based prior knowledge. Summary of the Invention

[0005] The core of this invention lies in constructing a precise generation system with traceable parameters, programmable structure, and predictable performance to solve key defects in existing technologies, such as premature softening due to uncontrolled degradation curves, stress concentration caused by the lack of mechanical gradients, and local inflammatory reactions induced by surface bioinertness.

[0006] To solve the above problems, the present invention adopts the following technical solution.

[0007] A multi-objective method for generating implantable biodegradable hydrogel sustained-release carriers includes the following steps: Step S1: Obtain the physiological and mechanical boundary condition data of the target implantation site, and based on the physiological and mechanical boundary condition data, establish a non-uniform cross-linking density distribution function in the three-dimensional space of the hydrogel. This function takes the spatial coordinates as the independent variable and outputs the primary cross-linking point density value and the secondary dynamic bonding probability value at the corresponding position. Step S2: Prepare the basic polymer solution based on the non-uniform crosslinking density distribution function; Step S3: Introduce a photoinitiator system into the base polymer solution. The concentration of the photoinitiator system is controlled between 0.3% and 0.5%. Gradient exposure curing is performed under the conditions of a UV light source wavelength of 365 nm and a light intensity of 50 mW per square centimeter. The exposure time varies with the spatial coordinates and follows the time mapping relationship set by the non-uniform crosslinking density distribution function. Step S4: During the curing and molding process, a directional fluid shear field is simultaneously applied, which is generated by a parallel plate rheometer; Step S5: Post-processing modification of the cured hydrogel carrier, including surface grafting of heparin fragments and laminin peptides. Step S6: Place the modified hydrogel carrier in a simulated body fluid environment for accelerated aging test, and collect data on mass loss rate, compression modulus decay curve and cell adhesion density change at different time points. Step S7: Input the data collected in step S6 into the pre-constructed multi-objective feedback optimization model. The multi-objective feedback optimization model is based on the embedded genetic algorithm. The fitness function consists of three weighted indicators: the first is the inverse of the absolute deviation between the degradation half-life and the target service life; the second is the ratio of the measured value to the theoretical value of the modulus retention rate at the mid-term time point; and the third is the proportion of macrophage polarization index M-type. Step S8: According to the parameter adjustment instructions output by the multi-objective feedback optimization model, reverse the spatial gradient coefficient, secondary dynamic bonding ratio factor and surface grafting density value in the non-uniform crosslinking density distribution function, and repeat the process of configuring solution, gradient curing, shear orientation, surface modification and accelerated aging test until all three weighted indicators reach the preset convergence threshold. Step S9: The final output is a three-dimensional entity of the implantable biodegradable hydrogel sustained-release carrier that meets the requirements of multi-objective collaborative optimization, along with its corresponding set of process parameters.

[0008] Further, step S1 specifically includes the following operations: dividing the target implantation area into three-dimensional Cartesian mesh units, each mesh unit having a side length of 0.5 mm; assigning an initial crosslinking density value to each mesh unit based on the local tissue elastic modulus and periodic stress amplitude; when the elastic modulus is below 5 kPa and the stress amplitude is above 15%, the initial crosslinking density value is set to 300 crosslinking points per cubic micrometer; when the elastic modulus is between 5 kPa and 20 kPa and the stress amplitude is below 10%, the initial crosslinking density value is set to 600 crosslinking points per cubic micrometer; when the elastic modulus is above 20 kPa or the stress amplitude approaches zero, the initial crosslinking density value is set to 1,200 crosslinking points per cubic micrometer; performing finite element stress simulation on each mesh unit; if the cumulative plastic strain exceeds 3% after ten consecutive cycles of loading, the crosslinking density of that unit is increased by 20% and the simulation is repeated until convergence; assigning a mass ratio of borate ester bonds to Schiff base bonds to each mesh unit, with an initial ratio of 7:3, allowing correction within the range of 5:5 to 9:1, with a correction step size of 0.5.

[0009] Further, step S2 specifically includes the following operations: weigh methacrylamide gelatin, sodium alginate oxide and phenylboronic acid functionalized hyaluronic acid, with mass fractions controlled at 8% to 12%, 5% to 9%, and 3% to 6%, respectively; dissolve the three polymers sequentially in phosphate buffer solution, maintain a stirring rate of 300 revolutions per minute, control the temperature at 4 degrees Celsius, and dissolve for no less than two hours; sterilely filter the solution through a 0.22-micron pore size filter membrane; and place the filtered solution in a light-proof container for later use.

[0010] Furthermore, the gradient exposure curing operation in step S3 specifically includes the following: injecting the base polymer solution into the 3D printing mold, with a digital micromirror array integrated at the bottom of the mold; calculating the independent exposure time for each spatial grid cell based on the non-uniform crosslinking density distribution function, with a time range from ten to sixty seconds; activating the ultraviolet light source with a wavelength of 365 nanometers and a light intensity of 50 milliwatts per square centimeter, and precisely projecting the corresponding light spot pattern according to the spatial coordinates by the digital micromirror array to achieve regional differential curing.

[0011] Furthermore, step S4 specifically includes the following operations: installing parallel plate rheometers on both sides of the mold, with the initial plate spacing set to one millimeter; during the first two-thirds of the total curing time, starting the rheometer to apply a shear field, with the shear rate dynamically adjusted within the range of ten to thirty revolutions per second, and the shear direction set according to the principal stress vector of the target implantation site to ensure that the polymer chain segments are oriented along the load transfer path.

[0012] Further, step S5 specifically includes the following operations: transferring the hydrogel carrier to a reaction vessel and washing it three times with phosphate buffer for five minutes each time; preparing a mixed solution of heparin fragment and laminin peptide at concentrations of 0.5 mmol / L and 0.3 mmol / L, respectively; adding carbodiimide activator at a concentration of 2 mmol / L and reacting for four hours at 4 degrees Celsius and pH 7.4; after the reaction, incubating with blocking solution containing 1% bovine serum albumin for two hours to block unreacted active sites.

[0013] Furthermore, step S6 specifically includes the following operations: adopting a dynamic perfusion mode, using Duchenne modified Eagle medium containing 10% fetal bovine serum as the perfusion solution, setting the perfusion flow rate to 0.5 ml per minute, maintaining the temperature at 37 degrees Celsius, and maintaining the carbon dioxide concentration at 5%. Samples are taken on the seventh, fourteenth, and twenty-first days to determine the mass loss rate, compressibility modulus, and cell adhesion density.

[0014] Furthermore, step S7 specifically includes the following operations: constructing a fitness function F = 0.4×W1 + 0.35×W2 + 0.25×W3, where W1 is the reciprocal of the degradation half-life deviation, W2 is the mid-term modulus retention ratio, W3 is the proportion of M type II macrophages, initializing the genetic algorithm population size to 200 individuals, with a crossover probability of 0.8 and a mutation probability of 0.05, performing a maximum of 50 generations of iteration, and retaining the best 10 individuals in each generation to directly enter the next generation.

[0015] Furthermore, the operation of reversely correcting the spatial gradient coefficient, secondary dynamic bonding ratio factor, and surface grafting density value in the non-uniform crosslinking density distribution function in step S8 specifically includes the following: if W1 is lower than the threshold, the spatial gradient coefficient is increased with a correction step of 0.005 per millimeter, ranging from 0.03 to 0.08 per millimeter; if W2 is lower than the threshold, the borate ester bond ratio is increased with a correction step of 0.5, ranging from 5:5 to 9:1; if W3 is lower than the threshold, the heparin fragment grafting density is increased with a correction step of one molecule per square micrometer, ranging from 15 to 25 molecules per square micrometer.

[0016] Furthermore, step S9 specifically includes the following operations: cutting the optimized hydrogel carrier into standard samples, one part for long-term stability tracking and the other part for animal implantation experiments, generating a structured data table with fields including spatial coordinate index, exposure time, shear rate, grafting density, and timestamp mark, outputting the file in standard stereolithography format, and archiving the process parameter set in CSV format to ensure traceability and reproducibility.

[0017] A multi-objective generation system for implantable biodegradable hydrogel sustained-release carriers, applied to the aforementioned multi-objective generation method for implantable biodegradable hydrogel sustained-release carriers, includes a physiological and mechanical boundary condition acquisition module, a non-uniform cross-linking density modeling module, a basic solution intelligent preparation module, a gradient photocuring control module, a shear field induced orientation module, a bioactive surface modification module, an accelerated aging data acquisition module, a multi-objective feedback optimization engine module, a process parameter iterative correction module, and a finished product output and parameter archiving module.

[0018] Compared with the prior art, the advantages of this invention are: This scheme achieves a gradient design of the internal structure of hydrogel from macro to micro by constructing a non-uniform cross-linking density distribution function. This enables the material to autonomously adjust its stiffness distribution according to the local stress state when subjected to complex physiological loads, thus avoiding the sudden mechanical collapse caused by the overall degradation of traditional homogeneous hydrogels. By introducing a dual dynamic bonding system composed of borate ester bonds and Schiff base bonds, the material is endowed with self-healing ability and controllable fracture characteristics while maintaining the initial structural integrity, which significantly extends the effective load-bearing period. Through the dual biosignaling molecules of heparin fragments and laminin peptides grafted onto the surface, it actively guides the orderly adhesion and differentiation of host cells, and inhibits fibrous encapsulation and chronic inflammatory response. By establishing a multi-objective feedback optimization model with embedded genetic algorithm as the core, the three key performance indicators of degradation behavior, mechanical evolution and immune response are incorporated into a unified evaluation framework, realizing closed-loop automatic tuning of process parameters and significantly improving the consistency between product batches and the accuracy of clinical compatibility. The implantable biodegradable hydrogel sustained-release carrier obtained by this invention can maintain structural support of no less than 60% of the initial modulus during its in vivo service life of up to twelve months, with the degradation half-life error controlled within ±15 days. At the same time, it induces a stable proportion of M2 macrophages of more than 70%, thus comprehensively solving the problems of excessively rapid softening and local irritation in the prior art. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall technical architecture of the multi-objective generation method for implantable biodegradable hydrogel sustained-release carriers proposed in this invention. Figure 2 This is a schematic diagram of the core principle framework of the synergistic regulation of non-uniform crosslinking density distribution function and dynamic bonding in this invention; Figure 3 This is a flowchart illustrating the logical flow of gradient photocuring and shear field-induced orientation co-forming in this invention. Figure 4 This is a flowchart illustrating the logical flow framework of bioactive surface modification and immune microenvironment adaptation in this invention. Figure 5 This is a schematic diagram of the closed-loop parameter iteration framework driven by the multi-objective feedback optimization model in this invention; Figure 6 This is a schematic diagram of the multi-level data flow and control flow from the acquisition of physiological and mechanical boundary conditions to the archiving of finished product parameters in this invention. Detailed Implementation

[0020] The technical solutions will now be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.

[0021] Example:

[0022] Please see Figure 1 A multi-objective method for generating implantable biodegradable hydrogel sustained-release carriers includes the following steps: Step S1: Obtain the physiological and mechanical boundary condition data of the target implantation site. The physiological and mechanical boundary condition data includes the local tissue elastic modulus distribution map, periodic deformation frequency and amplitude, body fluid flow rate and direction, concentration gradient of adjacent extracellular matrix components, body fluid flow rate threshold, and expected service time. Based on the physiological and mechanical boundary condition data, establish a non-uniform cross-linking density distribution function in the three-dimensional space of the hydrogel. This function takes spatial coordinates as independent variables and outputs the density value of primary cross-linking points and the probability value of secondary dynamic bonding at the corresponding positions. The primary cross-linking points are composed of bifunctional polyethylene glycol diacrylate, and the secondary dynamic bonding is formed by mixing borate ester bonds and Schiff base bonds in a preset ratio. Step S2: Based on the non-uniform crosslinking density distribution function, prepare a basic polymer solution. The basic polymer solution contains 8% to 12% by mass of methacrylamide gelatin, 5% to 9% by mass of sodium alginate oxide, 3% to 6% by mass of phenylboronic acid-functionalized hyaluronic acid, and the remainder is phosphate buffer. Step S3: Introduce a photoinitiator system into the base polymer solution. The photoinitiator system consists of camphorquinone and tetramethylethylenediamine in a mass ratio of 1:0.8, with the total concentration controlled between 0.3% and 0.5%. Gradient exposure curing is performed under ultraviolet light source conditions of 365 nm wavelength and 50 mW / cm² intensity. The exposure time varies with spatial coordinates and follows the time mapping relationship set by the non-uniform crosslinking density distribution function. Step S4: During the curing process, a directional fluid shear field is applied simultaneously. The directional fluid shear field is generated by a parallel plate rheometer. The shear rate is set to 10 to 30 revolutions per second and the duration is the first two-thirds of the total curing time. This is used to induce the polymer chain segments to align along the principal stress direction and improve the creep resistance of the local area. Step S5: Post-treatment modification of the solidified hydrogel carrier, including surface grafting of heparin fragments and laminin peptides, with grafting densities of 20 molecules per square micrometer and 15 molecules per square micrometer, respectively. The grafting reaction is carried out at 4 degrees Celsius and pH 7.4 for 4 hours. Covalent coupling is achieved by using the carbodiimide activated carboxyl group method. Step S6: Place the modified hydrogel carrier in a simulated body fluid environment for accelerated aging test, and collect data on mass loss rate, compression modulus decay curve and cell adhesion density change at different time points. Step S7: Input the data collected in step S6 into the pre-constructed multi-objective feedback optimization model. The multi-objective feedback optimization model is based on the embedded genetic algorithm. The fitness function consists of three weighted indicators: the first is the inverse of the absolute deviation between the degradation half-life and the target service life; the second is the ratio of the measured value to the theoretical value of the modulus retention rate at the mid-term time point; and the third is the proportion of macrophage polarization index M-type. Step S8: According to the parameter adjustment instructions output by the multi-objective feedback optimization model, reverse the spatial gradient coefficient, secondary dynamic bonding ratio factor and surface grafting density value in the non-uniform crosslinking density distribution function, and repeat the process of configuring solution, gradient curing, shear orientation, surface modification and accelerated aging test until all three weighted indicators reach the preset convergence threshold. Step S9: The final output is a three-dimensional entity of an implantable biodegradable hydrogel sustained-release carrier that meets the requirements of multi-objective collaborative optimization, and its corresponding set of process parameters. The set of process parameters includes the exposure time series, shear rate time series curves, and surface grafting reaction condition combinations for each spatial region.

[0023] This application also provides a multi-objective generation system for implantable biodegradable hydrogel sustained-release carriers, applied to the aforementioned multi-objective generation method for implantable biodegradable hydrogel sustained-release carriers. The system includes a physiological and mechanical boundary condition acquisition module (used to acquire physiological and mechanical boundary condition data of the target implantation site in step S1), a non-uniform cross-linking density modeling module (used to establish a non-uniform cross-linking density distribution function in the three-dimensional space of the hydrogel in step S1), a basic solution intelligent preparation module (used in step S2), a gradient photocuring control module (used in step S3), a shear field-induced orientation module (used in step S4), a bioactive surface modification module (used in step S5), an accelerated aging data acquisition module (used in step S6), a multi-objective feedback optimization engine module (used in step S7), a process parameter iterative correction module (used in step S8), and a finished product output and parameter archiving module (used in step S9).

[0024] Step S1 specifically includes the following operations: Dividing the target implantation area into three-dimensional Cartesian mesh units, each mesh unit having a side length of 0.5 mm; Assigning an initial crosslinking density value to each mesh unit based on the local tissue elastic modulus and periodic stress amplitude; When the elastic modulus is below 5 kPa and the stress amplitude is above 15%, the initial crosslinking density value is set to 300 crosslinking points per cubic micrometer; When the elastic modulus is between 5 kPa and 20 kPa and the stress amplitude is below 10%, the initial crosslinking density value is set to 600 crosslinking points per cubic micrometer; When the elastic modulus is above 20 kPa or the stress amplitude approaches zero, the initial crosslinking density value is set to 1,200 crosslinking points per cubic micrometer; Performing finite element stress simulation on each mesh unit; If the cumulative plastic strain exceeds 3% after ten consecutive cycles of loading, the crosslinking density of that unit is increased by 20% and the simulation is repeated until convergence; Assigning the mass ratio of borate ester bonds to Schiff base bonds to each mesh unit, with an initial ratio of 7:3, allowing correction within the range of 5:5 to 9:1, with a correction step size of 0.5.

[0025] In step S1, the data acquisition process is completed collaboratively by a minimally invasive probe array and a microfluidic sensing unit. The minimally invasive probe array consists of a piezoresistive strain sensor integrated on a flexible silicon substrate, with a spatial resolution of 0.5 mm and a sampling frequency of 100 Hz, used to capture the three-dimensional deformation trajectory of tissue during respiration, heartbeat, or muscle contraction. The microfluidic sensing unit consists of fluorescently labeled probes embedded in polydimethylsiloxane channels and electrochemical impedance detection electrodes, used to measure the concentration of protease, ionic strength, and redox potential in local body fluids in real time. All acquired data are converted into digital signals by an analog-to-digital converter and then input to the central data processing unit for spatiotemporal alignment and noise filtering, ultimately forming a three-dimensional data cube containing mechanical load spectrum, biochemical microenvironment parameters, and dynamic boundary constraints, which serves as the initial input conditions for the subsequent generation of structural parameters.

[0026] Furthermore, a dynamic bonding synergistic regulation model can be constructed based on physiological and mechanical boundary condition data. This model consists of three parts: a spatial coordinate mapping layer, a mechanical response prediction layer, and a chemical bond lifetime regulation layer. The spatial coordinate mapping layer divides the implantation area into several micron-sized voxel units. Each voxel unit is assigned an independent initial crosslinking density value, which is determined by the local elastic modulus and the periodic deformation amplitude. The mechanical response prediction layer uses a finite element simulation engine to iteratively calculate the stress-strain response of each voxel element under a preset load spectrum. If the cumulative plastic strain of a voxel element exceeds 3% after ten consecutive cycles of loading, the crosslinking density adaptive enhancement mechanism is triggered, increasing the number of crosslinking points by 20% and recalculating until convergence. The chemical bond lifetime regulation layer introduces a mixed bonding system of dynamic covalent bonds and metal coordination bonds. The dynamic covalent bonds are phenylboronic acid ester bonds, whose half-life is regulated by the local glucose concentration. When the glucose concentration is higher than 5 mmol / L, the half-life is shortened to 72 hours, and when it is lower than 1 mmol / L, it is extended to 360 hours. The metal coordination bonds are coordination structures of zinc ions and histidine side chains. Their dissociation constant changes with the local pH value. At a pH value of 7.4, the dissociation constant is 10 to the power of -6 mol / L, and when the pH value drops to 6.0, the dissociation constant increases to 10 to the power of -4 mol / L.

[0027] Through the coupling operation of the above three-layer structure, the crosslinking density value, dynamic bond type ratio and bond lifetime control parameter corresponding to each voxel unit are finally output, forming a complete non-uniform crosslinking density distribution function.

[0028] In the above method, the construction process of the non-uniform crosslinking density distribution function is further refined into three sub-steps: First, based on the elastic modulus distribution map in the biomechanical boundary condition data, an initial crosslinking density spatial mapping matrix is ​​generated. The row and column indices of this matrix correspond to the spatial coordinates of the implantation area, and the matrix element values ​​represent the initial set value of the crosslinking density at that coordinate point. The values ​​are obtained from a preset elastic modulus-crosslinking density reference table. The reference table is established by the compression test of the previous ex vivo tissue specimen and the swelling rate determination of the corresponding hydrogel sample. It covers the continuous range of elastic modulus from 1 kPa to 50 kPa and the corresponding relationship of crosslinking density from 100 crosslinking points per cubic micrometer to 2,000 crosslinking points. Secondly, finite element simulation of the mechanical response prediction layer was introduced. The initial crosslinking density spatial mapping matrix was imported into commercial simulation software, and displacement boundary conditions obtained by converting the periodic deformation frequency and amplitude were applied. The hyperelastic Mooney-Rivlin model was selected as the material constitutive model, and the parameters were obtained by fitting the previous tensile test. The simulation step size was set to 0.1 seconds, and the total duration was 600 seconds, covering ten complete deformation cycles. During the simulation, the Von Mises stress value of each voxel element was monitored in real time. If the stress value of a certain element at any time exceeded 80% of its material yield strength, it was marked as a high-risk area, triggering the crosslinking density local enhancement subroutine, which increased the crosslinking density of the element and its eight neighboring elements by 15%, and the simulation was re-executed until the stress values ​​of all elements were below the safety threshold. Finally, by superimposing the dynamic bonding parameters of the chemical bond lifetime regulation layer, the ratio of dynamic covalent bonds to metal coordination bonds is assigned to each voxel unit. The ratio allocation rules are as follows: in regions where the glucose concentration is higher than 3 mmol / L and the pH value is lower than 7.0, the proportion of dynamic covalent bonds is set to 70%, and the proportion of metal coordination bonds is set to 30%; in regions where the glucose concentration is lower than 1 mmol / L and the pH value is higher than 7.2, the proportion of dynamic covalent bonds is set to 30%, and the proportion of metal coordination bonds is set to 70%; in the intermediate transition region, the ratio is determined by linear interpolation. Through the above three-step refinement operation, it is ensured that the non-uniform crosslinking density distribution function meets both mechanical stability requirements and environmentally responsive degradation capabilities.

[0029] Step S2 specifically includes the following operations: Weigh methacrylamide gelatin, sodium alginate oxide, and phenylboronic acid-functionalized hyaluronic acid, with mass fractions controlled at 8% to 12%, 5% to 9%, and 3% to 6%, respectively. Dissolve the three polymers sequentially in phosphate buffer solution, maintaining a stirring rate of 300 revolutions per minute, controlling the temperature at 4 degrees Celsius, and dissolving for no less than two hours. Perform sterile filtration of the solution through a 0.22-micron pore size filter membrane, and place the filtered solution in a light-proof container for later use.

[0030] The gradient exposure curing operation in step S3 specifically includes the following: injecting the base polymer solution into the 3D printing mold, with a digital micromirror array integrated at the bottom of the mold; calculating the independent exposure time for each spatial grid cell based on the non-uniform crosslinking density distribution function, with a time range from ten to sixty seconds; activating the ultraviolet light source with a wavelength of 365 nanometers and a light intensity of 50 milliwatts per square centimeter, and precisely projecting the corresponding light spot pattern according to the spatial coordinates by the digital micromirror array to achieve regional differential curing.

[0031] Step S4 specifically includes the following operations: Install parallel plate rheometers on both sides of the mold, with the initial plate spacing set to one millimeter; in the first two-thirds of the total curing time, start the rheometer to apply a shear field, with the shear rate dynamically adjusted within the range of ten to thirty revolutions per second, and the shear direction set according to the principal stress vector of the target implantation site to ensure that the polymer chain segments are oriented along the load transfer path.

[0032] The gradient exposure curing and shear field induced orientation co-forming process formed by combining steps S3 and S4 is completed in a customized 3D printing platform, which includes a programmable ultraviolet light source array, a microfluidic shear control module and a real-time morphology monitoring camera. The programmable ultraviolet light source array consists of four hundred independently controlled light-emitting diode units, each with a wavelength of 365 nanometers. The light intensity can be continuously adjusted within the range of 0 to 50 milliwatts per square centimeter. Its spatial arrangement corresponds one-to-one with the voxel grid of the hydrogel to be formed. The irradiation intensity and duration of each unit are set according to the value in the crosslinking density distribution function. High-intensity irradiation areas correspond to voxels with high crosslinking density, and low-intensity irradiation areas correspond to voxels with low crosslinking density. The irradiation time ranges from ten seconds to sixty seconds to ensure that the photoinitiator achieves differentiated polymerization degree in different areas. The microfluidic shear control module consists of an annular microchannel and a piezoelectric driven pump. It synchronously applies a directional shear flow field during the photocuring process. The shear rate is set in the range of 10 to 500 seconds to the power of -1. The shear direction is consistent with the direction of the principal stress of the local tissue. It is used to induce the polymer chains to oriented along the direction of mechanical load, thereby forming an anisotropic mechanical structure at the microscale. A real-time morphology monitoring camera acquires images of the gel morphology evolution during the curing process at a rate of 30 frames per second. An edge detection algorithm identifies interface shrinkage and internal pore formation. Once a local shrinkage rate exceeding 5% or a pore diameter greater than 20 micrometers is detected, a compensation mechanism is immediately triggered: the irradiation intensity in the shrinkage area is reduced by 10% and the irradiation time is extended by 5 seconds; the shear rate in the pore area is increased by 20% and maintained for 10 seconds. Through this synergistic regulation, a pre-formed hydrogel carrier with a spatially gradient cross-linked structure and molecular chain orientation is finally obtained.

[0033] In the gradient photocuring and shear field-induced orientation co-molding process, precise control of photocuring parameters relies on feedback data from a real-time morphology monitoring camera. Images acquired by the camera are preprocessed with grayscale and Gaussian filtering before being input into a convolutional neural network segmentation model. This model, pre-trained on 5,000 labeled images of the hydrogel curing process, accurately identifies the gel-liquid interface location and internal pore contours. The segmentation results are used to calculate local shrinkage rate and pore feature parameters. The shrinkage rate is defined as the absolute value of the area difference between the corresponding regions in the current frame and the initial frame, divided by the area of ​​the initial frame. Pore feature parameters include equivalent diameter, roundness, and mean edge gradient. When the shrinkage rate exceeds 5%, the system automatically reduces the irradiation intensity of the corresponding ultraviolet light source unit. The reduction is calculated linearly based on the percentage of shrinkage exceeding the threshold, decreasing by 1% for every 1% exceeding the threshold, down to a minimum of 50% of the original set value. Simultaneously, the irradiation time is extended, calculated as the square root of the threshold exceeding the threshold, extending by 0.5 seconds for every 1% exceeding the threshold, up to a maximum of twice the original set time. When the equivalent diameter of a pore is detected to be greater than 20 micrometers, the system increases the shear rate of the corresponding microfluidic shear control module in that region. The increase is calculated linearly proportional to the pore diameter exceeding the threshold, with the shear rate increasing by 5% for every micrometer exceeding the threshold, up to a maximum of twice the original set value. This high-shear state is maintained for 10 seconds, prompting unpolymerized monomers to migrate and fill the pore area. This feedback control mechanism ensures that structural defects during the molding process are corrected in real time, avoiding the accumulation of macroscopic defects.

[0034] Step S5 specifically includes the following operations: transferring the hydrogel carrier to a reaction vessel and washing it three times with phosphate buffer for five minutes each time; preparing a mixed solution of heparin fragment and laminin peptide at concentrations of 0.5 mmol / L and 0.3 mmol / L, respectively; adding carbodiimide activator at a concentration of 2 mmol / L and reacting at 4°C and pH 7.4 for four hours; after the reaction, incubating with blocking solution containing 1% bovine serum albumin for two hours to block unreacted active sites.

[0035] In addition, the hydrogel carrier underwent bioactive surface modification and immune microenvironment adaptation treatment. This treatment consisted of two sub-steps: surface functional group grafting and microenvironment-responsive coating deposition. Surface functional group grafting was performed using a plasma-activated immersion method. First, the pre-formed hydrogel was placed in an argon plasma chamber for 30 seconds at a power of 50 watts to generate hydroxyl and carboxyl active sites on the surface. Subsequently, it was immersed in a phosphate buffer solution containing an arginine-glycine-aspartic acid tripeptide sequence at a concentration of 0.5 mg / mL at a temperature of 37°C for a reaction time of 2 hours. This allowed the tripeptide sequence to be covalently linked to the surface active sites via amide bonds, forming a bioactive interface that promotes cell adhesion. The microenvironment-responsive coating deposition employs a layer-by-layer self-assembly technique, using polylysine and hyaluronic acid as basic building blocks. Eight layers are alternately deposited on the surface, with each layer rinsed with deionized water and dried with nitrogen after deposition. The concentration of the polylysine solution is 1 mg / mL, and the concentration of the hyaluronic acid solution is 0.8 mg / mL. The pH of the solution is maintained at 7.4 during the deposition process. The coating remains stable under normal physiological conditions. When the concentration of local inflammatory factors, such as tumor necrosis factor alpha, exceeds 10 picograms / mL, the polylysine segments in the coating undergo a conformational change, exposing the internally encapsulated interleukin-10 mimic peptide. The release rate is positively correlated with the concentration of inflammatory factors, with the highest release rate reaching 0.2 micrograms per square centimeter per hour, thereby achieving active adaptation to the local immune microenvironment and inhibition of inflammation.

[0036] In the bioactive surface modification step, the grafting density of the arginine-glycine-aspartic acid tripeptide sequence was jointly regulated by plasma treatment parameters and impregnation conditions. The plasma treatment power was adjustable from 30 watts to 80 watts, and the treatment time was adjustable from 10 seconds to 60 seconds. Experiments showed that the grafting density was positively correlated with the treatment power and exhibited a saturation growth relationship with the treatment time. Under the condition of 50 watts and 30 seconds of treatment, the grafting density reached 0.8 tripeptide molecules per square micrometer, which was the optimal process point. During the impregnation process, the solution temperature was maintained at 37 degrees Celsius and controlled by a water bath circulation system, with temperature fluctuations not exceeding ±0.5 degrees Celsius. The reaction time was two hours, which was the equilibrium time. Extending the reaction time to four hours only increased the grafting density by 5%, so a longer reaction time was not adopted. After grafting, the grafting effect was verified by fluorescent labeling: the sample was immersed in a solution of anti-arginine-glycine-aspartic acid antibody labeled with fluorescein isothiocyanate, incubated for one hour, washed, and the fluorescence intensity distribution was observed under a confocal microscope. The fluorescence intensity was linearly related to the grafting density. The actual grafting density value was obtained by conversion through a standard curve. If it was less than 0.6 molecules per square micrometer, the plasma treatment and immersion steps had to be repeated until the standard was met.

[0037] During the deposition of the immune microenvironment-adaptive coating, the number of self-assembled layers is closely related to the sensitivity of the inflammatory factor response. Experimental results show that when the number of deposition layers is six, after the concentration of tumor necrosis factor alpha exceeds 10 picograms per milliliter, the release delay time of interleukin-10 mimic peptide is four hours, and the cumulative release amount over 24 hours is 3.5 micrograms per square centimeter. When the number of deposition layers is eight, the delay time is shortened to two hours, and the cumulative release amount increases to 4.8 micrograms per square centimeter. When the number of deposition layers is ten, the delay time is further shortened to one hour, but the cumulative release amount only increases to 5.2 micrograms, and the mechanical stability of the coating decreases, making it prone to peeling during cyclic compression testing. Therefore, eight layers were selected as the optimal number of deposition layers to maintain the integrity of the coating structure while ensuring the response speed and release amount. The coating deposition uniformity was verified by measuring the thickness distribution using an ellipsometry, requiring a standard deviation of less than ±5 nanometers. If the deviation exceeds this requirement, the polyelectrolyte solution concentration or deposition time was adjusted until the requirement was met.

[0038] Step S6 specifically includes the following operations: a dynamic perfusion mode is adopted, the perfusion medium is DuPont modified Eagle medium containing 10% fetal bovine serum, the perfusion flow rate is set to 0.5 ml per minute, the temperature is kept constant at 37 degrees Celsius, the carbon dioxide concentration is maintained at 5%, and samples are taken on the seventh, fourteenth and twenty-first days to determine the mass loss rate, compressibility modulus and cell adhesion density.

[0039] Step S7 specifically includes the following operations: constructing the objective function using a weighted summation method, with weight coefficients set according to clinical priority, such as constructing the fitness function F = 0.4×W1 + 0.35×W2 + 0.25×W3, where W1 is the reciprocal of the degradation half-life deviation, W2 is the mid-term modulus retention rate ratio, and W3 is the proportion of M type II macrophages. The initial genetic algorithm population size is 200 individuals, with a crossover probability of 0.8 and a mutation probability of 0.05. A maximum of 50 generations of iterations are performed, with the best 10 individuals retained in each generation to directly enter the next generation.

[0040] The multi-objective feedback optimization model in step S7 uses degradation rate deviation, mechanical property decay rate, and inflammatory factor concentration as optimization objectives, and crosslinking density distribution function parameters, dynamic bond ratio, photocuring intensity gradient, and coating thickness as control variables. The model input consists of the physiological and mechanical boundary condition data collected in step S1 and the performance test data in the in vitro simulation environment in step S6, and the output is the correction amount of each control variable in the next iteration cycle.

[0041] Performance test data were obtained through in vitro degradation experiments, cyclic compression tests, and macrophage co-culture experiments. In vitro degradation experiments were conducted in simulated body fluid at 37 degrees Celsius, with daily sampling to measure mass loss rate and molecular weight distribution. Cyclic compression tests applied the same load spectrum as the target implantation site, recording the compressive modulus retention rate after every 100 cycles. Macrophage co-culture experiments used mouse peritoneal macrophages, and the concentrations of interleukin-6 and tumor necrosis factor-alpha in the culture supernatant were measured after 24 hours of co-culture. The multi-objective feedback optimization model used a weighted summation method to construct the objective function, with weight coefficients set according to clinical priority. The optimization algorithm used was a modified particle swarm optimization algorithm with a population size of 50, a maximum number of iterations of 100, and inertia weights linearly decreasing from 0.9 to 0.4. After each iteration, the model outputs the correction coefficients of each voxel unit in the crosslinking density distribution function, the dynamic bond ratio adjustment range, the photocuring intensity gradient scaling factor, and the increase or decrease in the number of coating deposition layers. These corrections are fed back to the corresponding process parameters in steps S2 to S7, and the generation process is re-executed until all three optimization objectives converge to the preset threshold range: degradation rate deviation less than 5%, mechanical property decay rate less than 10%, and inflammatory factor concentration less than 5 picograms per milliliter.

[0042] In the operation of the multi-objective feedback optimization model, the parameter settings of the improved particle swarm optimization algorithm have a decisive impact on the convergence speed and accuracy. The design of linearly decreasing the inertia weight from 0.9 to 0.4 aims to maintain a large search step size in the early stage to avoid getting trapped in local optima, and reduce the step size in the later stage to finely adjust the parameters; the learning factors c1 and c2 are both set to 2.0 to balance the influence of individual experience and group experience; the particle velocity limit is set to 10% of the parameter search range to prevent the search process from diverging. The degradation rate deviation in the objective function is calculated based on in vitro degradation experimental data and is defined as the root mean square error between the actual mass loss rate curve and the target curve at the seven-day, fourteen-day, and twenty-one-day time points; the mechanical property decay rate is defined as the ratio of the compressive modulus of the 500th cycle to the 1000th cycle in the cyclic compression test; the inflammatory factor concentration is taken as the geometric mean of the concentrations of interleukin-6 and tumor necrosis factor alpha in the macrophage co-culture experiment. After each iteration, the system automatically records the position and fitness value of the current best particle, generates a convergence curve, and terminates the optimization when the fitness value changes by less than 0.001 for 20 consecutive iterations or when the maximum number of iterations is reached, and outputs the final parameter set.

[0043] The operation of reverse correction of the spatial gradient coefficient, secondary dynamic bonding ratio factor and surface grafting density value in the non-uniform crosslinking density distribution function in step S8 specifically includes the following: If W1 is lower than the threshold, the spatial gradient coefficient is increased, with a correction step of 0.005 per millimeter, ranging from 0.03 to 0.08 per millimeter; if W2 is lower than the threshold, the borate ester bond ratio is increased, with a correction step of 0.5, ranging from 5:5 to 9:1; if W3 is lower than the threshold, the heparin fragment grafting density is increased, with a correction step of one molecule per square micrometer, ranging from 15 to 25 molecules per square micrometer.

[0044] Step S9 specifically includes the following operations: cutting the optimized hydrogel carrier into standard samples, one part for long-term stability tracking and the other part for animal implantation experiments, generating a structured data table with fields including spatial coordinate index, exposure time, shear rate, grafting density, and timestamp mark, outputting the file in standard stereolithography format, and archiving the process parameter set in CSV format to ensure traceability and reproducibility.

[0045] Step S9 involves archiving the final parameter set and finished product performance data after closed-loop optimization, forming a traceable generation record. The archived content includes the original physiological and mechanical boundary condition data cube, the final non-uniform crosslinking density distribution function matrix, the gradient photocuring parameter configuration table, the shear field-induced orientation parameter log, bioactive surface modification process parameters, immune microenvironment adaptation coating parameters, and raw in vitro performance test data and optimized convergence curves. All data is stored in a structured database, with each data item labeled with a timestamp, operator identifier, and equipment serial number to ensure the auditability and reproducibility of the generation process. Simultaneously, the final product is cut into standard samples. One portion is used for long-term stability tracking, placed in 37°C phosphate buffer for continuous monitoring of degradation behavior and mechanical property evolution; the other portion is used for in vivo animal implantation experiments. After subcutaneous implantation in rats or intra-articular implantation in rabbits, samples are periodically collected. Tissue section staining and immunohistochemical analysis are used to assess tissue integration and inflammatory response levels. The obtained in vivo data is fed back into the database as supplementary input for subsequent batch optimization.

[0046] Through the detailed breakdown of the above-described methodological steps and sub-steps, this invention achieves precise control over the microstructure, macroscopic properties, and biological functions of implantable biodegradable hydrogel sustained-release carriers. Non-uniform cross-linking density distribution ensures spatial matching between mechanical support and degradation behavior; dynamic bonding system imparts environmental responsiveness to the material; gradient photocuring and shear orientation processes achieve real-time suppression of structural defects; bioactive modification and immunoadaptive coating enhance tissue integration capabilities; and a multi-objective optimization model ensures a systemic balance of overall performance. The hydrogel carrier generated by this method exhibits excellent comprehensive performance in animal experiments: after 28 days of subcutaneous implantation in rats, the mass retention rate is 78%, the compression modulus retention rate is 85%, and the depth of inflammatory cell infiltration in surrounding tissue is less than 50 micrometers, significantly superior to the traditional homogeneous hydrogel control group's 45% mass retention rate, 60% modulus retention rate, and 200 micrometer inflammatory infiltration depth. This method provides a complete technical pathway for the precise manufacture of personalized implantable materials and has clear clinical translational value.

[0047] The above description is merely a preferred embodiment of the present invention; it encompasses all the protection scope of the present invention. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solutions and improved concepts of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A multi-target generation method for implantable biodegradable hydrogel sustained-release carriers, characterized in that: Includes the following steps: Step S1: Obtain the physiological and mechanical boundary condition data of the target implantation site, and based on the physiological and mechanical boundary condition data, establish a non-uniform cross-linking density distribution function in the three-dimensional space of the hydrogel. This function takes the spatial coordinates as the independent variable and outputs the primary cross-linking point density value and the secondary dynamic bonding probability value at the corresponding position. Step S2: Prepare a basic polymer solution based on the non-uniform crosslinking density distribution function; Step S3: Introduce a photoinitiator system into the base polymer solution. The concentration of the photoinitiator system is controlled between 0.3% and 0.5%. Gradient exposure curing is performed under the conditions of a UV light source wavelength of 365 nm and a light intensity of 50 mW per square centimeter. The exposure time varies with the spatial coordinates and follows the time mapping relationship set by the non-uniform crosslinking density distribution function. Step S4: During the curing and molding process, a directional fluid shear field is simultaneously applied, which is generated by a parallel plate rheometer; Step S5: Perform post-treatment modification on the cured hydrogel carrier, the post-treatment modification including surface grafting of heparin fragments and laminin peptides; Step S6: Place the modified hydrogel carrier in a simulated body fluid environment for accelerated aging test, and collect data on mass loss rate, compression modulus decay curve and cell adhesion density change at different time points. Step S7: Input the data collected in step S6 into the pre-constructed multi-objective feedback optimization model. The multi-objective feedback optimization model is based on the embedded genetic algorithm. The fitness function consists of three weighted indicators: the first is the inverse of the absolute deviation between the degradation half-life and the target service life; the second is the ratio of the measured value to the theoretical value of the modulus retention rate at the mid-term time point; and the third is the proportion of macrophage polarization index M-type II. Step S8: According to the parameter adjustment instructions output by the multi-objective feedback optimization model, reverse the spatial gradient coefficient, secondary dynamic bonding ratio factor and surface grafting density values ​​in the non-uniform crosslinking density distribution function, and repeat the process of configuring solution, gradient curing, shear orientation, surface modification and accelerated aging test until all three weighted indicators reach the preset convergence threshold. Step S9: The final output is a three-dimensional entity of the implantable biodegradable hydrogel sustained-release carrier that meets the requirements of multi-objective collaborative optimization, along with its corresponding set of process parameters.

2. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 1, characterized in that: Step S1 specifically includes the following operations: dividing the target implantation area into three-dimensional Cartesian mesh units, each mesh unit having a side length of 0.5 mm; assigning an initial crosslinking density value to each mesh unit based on the local tissue elastic modulus and periodic stress amplitude; setting the initial crosslinking density value to 300 crosslinking points per cubic micrometer when the elastic modulus is below 5 kPa and the stress amplitude is above 15%; setting the initial crosslinking density value to 600 crosslinking points per cubic micrometer when the elastic modulus is between 5 kPa and 20 kPa and the stress amplitude is below 10%; and setting the initial crosslinking density value to 1,200 crosslinking points per cubic micrometer when the elastic modulus is above 20 kPa or the stress amplitude is close to zero; performing finite element stress simulation on each mesh unit; if the cumulative plastic strain exceeds 3% after ten consecutive cycles of loading, increasing the crosslinking density of the unit by 20% and resimulating until convergence; assigning a mass ratio of borate ester bonds to Schiff base bonds to each mesh unit, with an initial ratio of 7:3, allowing correction within the range of 5:5 to 9:1, with a correction step size of 0.

5.

3. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 2, characterized in that: Step S2 specifically includes the following operations: Weigh methacrylamide gelatin, sodium alginate oxide, and phenylboronic acid-functionalized hyaluronic acid, with mass fractions controlled at 8% to 12%, 5% to 9%, and 3% to 6%, respectively. Dissolve the three polymers sequentially in phosphate buffer solution, maintain a stirring rate of 300 revolutions per minute, control the temperature at 4 degrees Celsius, and dissolve for no less than two hours. Perform sterile filtration of the solution through a 0.22-micron pore size filter membrane, and place the filtered solution in a light-proof container for later use.

4. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 3, characterized in that: The gradient exposure curing operation in step S3 specifically includes the following: injecting the base polymer solution into the 3D printing mold, with a digital micromirror array integrated at the bottom of the mold; calculating the independent exposure time for each spatial grid cell based on the non-uniform crosslinking density distribution function, with a time range from ten to sixty seconds; activating the ultraviolet light source with a wavelength of 365 nanometers and a light intensity of 50 milliwatts per square centimeter, and precisely projecting the corresponding light spot pattern according to the spatial coordinates by the digital micromirror array to achieve regional differential curing.

5. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 4, characterized in that: Step S4 specifically includes the following operations: installing parallel plate rheometers on both sides of the mold, with the initial plate spacing set to one millimeter; in the first two-thirds of the total curing time, starting the rheometer to apply a shear field, with the shear rate dynamically adjusted within the range of ten to thirty revolutions per second, and the shear direction set according to the principal stress vector of the target implantation site to ensure that the polymer chain segments are oriented along the load transfer path.

6. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 5, characterized in that: Step S5 specifically includes the following operations: transferring the hydrogel carrier to a reaction vessel and washing it three times with phosphate buffer for five minutes each time; preparing a mixed solution of heparin fragment and laminin peptide at concentrations of 0.5 mmol / L and 0.3 mmol / L, respectively; adding carbodiimide activator at a concentration of 2 mmol / L and reacting for four hours at 4 degrees Celsius and pH 7.4; after the reaction, incubating with a blocking solution containing 1% bovine serum albumin for two hours to block unreacted active sites.

7. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 6, characterized in that: Step S6 specifically includes the following operations: adopting a dynamic perfusion mode, the perfusion medium is Duchenne modified Eagle medium containing 10% fetal bovine serum, the perfusion flow rate is set to 0.5 ml per minute, the temperature is kept constant at 37 degrees Celsius, the carbon dioxide concentration is maintained at 5%, and samples are taken on the seventh day, the fourteenth day, and the twenty-first day to determine the mass loss rate, compressibility modulus and cell adhesion density.

8. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 7, characterized in that: Step S7 specifically includes the following operations: constructing a fitness function F = 0.4×W1 + 0.35×W2 + 0.25×W3, where W1 is the reciprocal of the degradation half-life deviation, W2 is the mid-term modulus retention ratio, W3 is the proportion of M type II macrophages, initializing the genetic algorithm population size to 200 individuals, with a crossover probability of 0.8 and a mutation probability of 0.05, performing a maximum of 50 generations of iteration, and retaining the best 10 individuals in each generation to directly enter the next generation.

9. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 8, characterized in that: The operation of reversely correcting the spatial gradient coefficient, secondary dynamic bonding ratio factor, and surface grafting density values ​​in the non-uniform crosslinking density distribution function in step S8 specifically includes the following: If W1 is lower than the threshold, the spatial gradient coefficient is increased with a correction step of 0.005 per millimeter, ranging from 0.03 to 0.08 per millimeter; if W2 is lower than the threshold, the borate ester bond ratio is increased with a correction step of 0.5, ranging from 5:5 to 9:1; if W3 is lower than the threshold, the heparin fragment grafting density is increased with a correction step of one molecule per square micrometer, ranging from 15 to 25 molecules per square micrometer.

10. The multi-target generation method for the implantable biodegradable hydrogel sustained-release carrier according to claim 9, characterized in that: Step S9 specifically includes the following operations: cutting the optimized hydrogel carrier into standard samples, one part for long-term stability tracking and the other part for animal implantation experiments, generating a structured data table with fields including spatial coordinate index, exposure time, shear rate, grafting density, and timestamp mark, outputting the file in standard stereolithography format, and archiving the process parameter set in CSV format to ensure traceability and reproducibility.

11. A multi-target generation system for implantable biodegradable hydrogel sustained-release carriers, applied to the multi-target generation method for implantable biodegradable hydrogel sustained-release carriers as described in claim 1, characterized in that: It includes a physiological and mechanical boundary condition acquisition module, a non-uniform cross-linking density modeling module, a basic solution intelligent preparation module, a gradient photocuring control module, a shear field induced orientation module, a bioactive surface modification module, an accelerated aging data acquisition module, a multi-objective feedback optimization engine module, a process parameter iterative correction module, and a finished product output and parameter archiving module.

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