Intelligent evaluation method for physical properties of anti-adhesion hydrogel

Abstract

The invention relates to the field of knowledge graph data processing, and discloses an intelligent evaluation method for physical properties of anti-adhesion hydrogel, which comprises the following steps: constructing an efficiency evaluation ontology containing a logic mask, and defining entities, edges and a physical consistency constraint rule set for describing mutually exclusive attribute pairs in a knowledge graph; generating a constrained heterogeneous information network, mapping sample data into nodes, and establishing connection and weight; executing path retrieval by taking a to-be-evaluated hydrogel representation node as a starting point, and performing sub-graph matching on a path attribute combination and a rule set in real time in the search process; and in response to a matched mutual exclusion attribute pair generation path blocking instruction, only calculating an accumulated weight of an effective path to generate an evaluation result. A physical common sense-based logic blocking mechanism is implanted in a graph retrieval algorithm, so that pseudo causal noise in high-dimensional small sample data is eliminated from the structure, and the evaluation accuracy is improved. And qualitative change from pure numerical statistics to logic self-consistent reasoning is realized.

Description

Technical Field

[0001] This invention relates to an intelligent evaluation method for the physical properties of anti-adhesion hydrogels, belonging to the field of knowledge graph data processing technology. Background Technology

[0002] In the current field of medical materials informatics and database construction, the management of R&D data for anti-adhesion hydrogels generally utilizes laboratory information management systems to structurally store physical characterization data and biological efficacy data. Conventional data processing employs a relational database attribute mapping mechanism, defining physical indicators such as modulus, viscosity, and swelling rate as independent numerical fields. Specific numerical threshold ranges are set to perform qualified material formulation retrieval and screening. This discrete numerical matching retrieval logic offers high linearity and clear causal relationships, resulting in relatively high accuracy for standard industrial product data, forming the basis of current material data evaluation and screening technologies. However, in practical evaluation scenarios, the aforementioned linear data processing logic faces the challenge of handling high-dimensional, small-sample data. The inconsistency with physical logic is a major drawback. The anti-adhesion efficacy of biomedical materials depends on the complex nonlinear coupling between the micro-crosslinking structure, degradation kinetics, and the dynamic physiological environment in vivo. This results in extremely high dimensionality, while effective biological experimental samples are extremely scarce, constituting a typical high-dimensional, small-sample characteristic. Existing data models based on linear threshold judgment or simple regression analysis are prone to statistical overfitting under small-sample conditions and lack physical constraints. They are also difficult to identify the inconsistencies in physical logic hidden in the parameter combinations, leading to a large amount of pseudo-causal noise that violates physical laws being mixed into the evaluation system. This makes it impossible to identify the essential differences in biocompatibility between materials with similar physical parameters but vastly different micro-topological structures.

[0003] Especially in the research and evaluation of novel biomedical materials, this limitation of lacking physical logical consistency and high-dimensional small sample reasoning ability is particularly prominent. Even in research on specific material formulations, existing data processing methods still remain at the level of linear correlation of macroscopic indicators, and cannot build a reliable efficacy prediction model when the sample size is insufficient. For example, Chinese invention patent CN119488632B discloses a bio-based anti-adhesion hydrogel dressing and its preparation method and application. The innovation of this solution is concentrated on the material system itself, but in the material screening and performance evaluation stage, it still relies on traditional laboratory test data and uses preset numerical thresholds or simple statistical models to determine the anti-adhesion efficacy. This evaluation system based on static discrete data cannot construct a causal topological relationship from the material microstructure and preparation process to the final biological efficacy. It is difficult to identify the risk of physical logical inconsistency behind seemingly qualified combinations of physical indicators, and it cannot eliminate the spurious correlation features in high-dimensional small sample data, highlighting the fundamental defects of current technology in processing high-dimensional small sample and heterogeneous material data.

[0004] Therefore, the technical problem to be solved by this invention is to construct a graph-based data processing architecture that maps physical parameters to complex semantic relationships between biological efficacy and to introduce logical masks and causal verification mechanisms under small sample data conditions to achieve high confidence and logically consistent intelligent evaluation. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of this invention is as follows: An intelligent evaluation method for the physical properties of anti-adhesion hydrogels, the core of which lies in constructing a closed-loop evaluation architecture integrating a logical mask mechanism, a causal verification system, and a time-decay dynamic weight. This method structurally blocks pseudo-causal paths by embedding a logical mask based on physical common sense into the graph retrieval algorithm; it introduces a time-decay dynamic weight mechanism and combines it with causal verification logic to ensure the self-consistency of the final evaluation result in terms of physical laws. Specifically, it includes the following steps:

[0006] Step 101: Establish a knowledge graph schema layer in the database, define representation entities, structural entities and invalid entities, establish weighted directed edges connecting each entity, and define a set of physical consistency constraint rules describing mutually exclusive attribute pairs. Use the physical consistency constraint rule set as the logical verification layer for graph traversal.

[0007] Step 102: Obtain physical test data and biological efficacy data of multiple sets of historical hydrogel samples, map the physical test data and biological efficacy data into instance nodes under the knowledge graph pattern layer, establish node connections and calculate edge weights based on the efficacy performance of historical hydrogel samples, and form a heterogeneous information network.

[0008] Step 103: Convert the physical data of the hydrogel to be evaluated into a target representation node. Starting from the target representation node, perform a multi-hop path search in the heterogeneous information network to identify candidate connected paths to the failed entity. When searching through each hop node, parse the attribute semantic combination accumulated in the current path and perform subgraph matching between the attribute semantic combination and the physical consistency constraint rule set.

[0009] Step 104: In response to the matching of any mutually exclusive attribute pair in the attribute semantic combination, a path blocking instruction is generated and the weight of the current candidate connected path is forcibly set to zero; the cumulative weight is calculated only for valid paths that have not triggered the path blocking instruction, and when the cumulative weight exceeds a preset threshold, it is determined that there is a risk of failure and the corresponding valid path is output.

[0010] Preferably, in step 101, the physical consistency constraint rule set includes logical triples that define mutually exclusive attribute pairs. The subgraph matching in step 103 specifically includes: parsing the attribute semantics of all intermediate nodes included in the currently identified candidate connected path; matching and verifying the attribute semantic combination of the intermediate nodes with the logical triples; and triggering a path blocking instruction when the attribute semantic combination simultaneously includes mutually exclusive attribute pairs defined in the logical triples. The mutually exclusive attribute pairs are attribute combinations that are predefined based on the common sense of materials science physics and are physically incompatible or will inevitably lead to specific failures.

[0011] Preferably, step 102 further includes binding a timestamp to each generated weighted directed edge, and calculating the edge weight specifically includes step 301 of dynamically adjusting the weight based on time decay logic, obtaining the current system time, and calculating the time difference between the generated timestamp and the current system time. The time influence factor is calculated using a preset nonlinear decay function. The dynamic effective weight of the weighted directed edge is calculated according to the following formula. : ,in, The initial static weights of the weighted directed edges, and the time influence factor. The range of values ​​is And the time difference The weight increases and then decreases monotonically. Multi-hop path search and cumulative weight calculation are performed using dynamic effective weights.

[0012] Preferably, step 101 further includes marking specific failed entities as veto nodes. Before step 104, a logical arbitration step 401 is performed, initiating a connectivity scanning process for veto nodes in parallel in the heterogeneous information network. If a connected subgraph leading to any veto node is detected from the target representation node, and the path weight of the connected subgraph is greater than a non-zero warning threshold, a logic blocking instruction with the highest priority is generated. In response to the logic blocking instruction, the calculation result of the accumulated weight is ignored, the evaluation result of the hydrogel to be evaluated is forcibly locked as unqualified, and the connected subgraph is output as the basis for rejection.

[0013] Preferably, before step 104, a counterfactual causality verification step 501 is performed, which identifies the key feature nodes with the highest contribution in the effective path based on the betweenness centrality algorithm; generates a set of counterfactual virtual samples in memory, keeping the attributes of the counterfactual virtual samples unchanged except for the key feature nodes, and applying small numerical perturbations that conform to physical laws to the attribute values ​​of the key feature nodes; inputs the counterfactual virtual samples into a heterogeneous information network for inference to obtain virtual evaluation results; compares the changing trends of the evaluation results of the hydrogel to be evaluated with the virtual evaluation results, and if the changing trend contradicts the preset material science and physics common sense rules, it is determined that the effective path has a risk of spurious correlation and its confidence level is reduced.

[0014] Preferably, step 103 further includes a noise resistance verification step 601 based on topology sensitivity, in which the key bridging node with the highest information flow carrying capacity is locked in the identified candidate connected paths; in the logical replica of the heterogeneous information network, a logical soft deletion mask is applied to the key bridging node to prevent the path search algorithm from traversing the key bridging node; the path search is re-executed in the logical replica after the logical soft deletion mask is applied to obtain the performance evaluation score of the alternative path; the difference rate between the original evaluation score and the performance evaluation score of the alternative path is calculated, and when the difference rate is higher than the preset structural vulnerability threshold, the confidence of the final evaluation result is reduced by using a nonlinear penalty function.

[0015] Preferably, the method further includes a cross-domain knowledge transfer step 701 based on structural isomorphism, which, in response to the fact that the hydrogel to be evaluated is an isolated node in the heterogeneous information network, extracts the microscopic topological features of the hydrogel to be evaluated and converts them into a fingerprint subgraph; performs a subgraph isomorphism search in a pre-set general material basic knowledge base to identify anchor material entities containing fingerprint subgraphs; obtains the physical property association edges that have been established for the anchor material entities in the general material basic knowledge base; maps the physical property association edges to the heterogeneous information network as inference types, establishes virtual inference edges connecting the hydrogel to be evaluated and the corresponding physical property nodes, and includes the virtual inference edges in the calculation scope of multi-hop path search.

[0016] Preferably, in step 101, the characterization entities include modulus semantic nodes and degradation rate semantic nodes generated through discretization mapping; the structural entities include attribute nodes characterizing the micro-crosslinking topology of the material; and the failure entities include nodes characterizing the risk of gel displacement and nodes characterizing the risk of physical barrier failure.

[0017] Preferably, converting the physical data of the hydrogel to be evaluated into target characterization nodes specifically includes: acquiring the rheological test curve data and in vitro enzymatic hydrolysis kinetic data of the hydrogel to be evaluated; extracting key inflection point features and slope features from the test curve data using a preset feature extraction algorithm; matching the key inflection point features and slope features with the predefined discretized intervals in the knowledge graph pattern layer, and mapping them to unique characterization entity identifiers.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. In the physical properties of anti-adhesion hydrogels, a weighted causal knowledge graph containing characterizing entities, structural entities, and failure entities is constructed. The evaluation of the physical properties of anti-adhesion hydrogels is transformed into a multi-hop semantic path retrieval problem of the graph. Based on topological mapping data processing, a semantic association between discrete physical test data and nonlinear biological failure modes is established. By utilizing the graph's pre-set strong logical edges and weight transfer mechanism, the shortest semantic path from characterizing nodes to failure nodes is retrieved in the absence of large-scale biological experimental data. This directly identifies the potential failure risks behind multi-parameter coupling, realizing the transformation from numerical prediction to white-box logical attribution, and solving the semantic discontinuity problem of high-dimensional feature reasoning under small sample conditions.

[0020] 2. The path retrieval process introduces a logic mask mechanism based on physical consistency constraints. The legality of candidate inference paths is verified in real time through subgraph matching technology. When traversing the graph nodes, the current path attribute combination is compared with the predefined mutually exclusive logic rule set. If the co-occurrence of attributes that violate physical common sense is detected, the mask is triggered to forcibly block the weight calculation of the path. The logic pruning strategy suppresses the pseudo-causal relationship generated by simply relying on statistical probability from the algorithm structure. It ensures that the output inference path conforms to physical logic self-consistency in engineering environments with sparse sample data or noise, and reduces false positives caused by data coincidence.

[0021] 3. A counterfactual causal verification mechanism based on feature micro-perturbation is adopted to construct a self-verifying closed loop for the confidence of the evaluation results. After the inference is completed, virtual samples with only minor numerical perturbations applied to key feature nodes are generated to monitor the differential response of the graph inference results and dynamically verify the logical stability of the current scoring conclusion. If the output results do not show the expected trend with the change of key physical quantities, it is determined that the current conclusion is due to statistical noise and the confidence is automatically reduced. The ability to identify and filter accidental correlations ensures that the final output evaluation results are based on the statistical regularity of historical data and have undergone counterfactual logic stress testing, ensuring the effectiveness of inference when facing new formulas or unknown structures. Attached Figure Description

[0022] Figure 1 This is a flowchart of the intelligent evaluation method that integrates time-related decay and logic masking mechanisms according to the present invention.

[0023] Figure 2This is a diagram illustrating the sensitivity of performance scoring and the penalty mechanism under micro-perturbations of key feature node attributes in this invention.

[0024] Figure 3 The fishbone architecture diagram of the multi-dimensional technical branches is used to ensure the logical consistency of the evaluation results in this invention. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] This invention proposes an intelligent evaluation method for the physical properties of anti-adhesion hydrogels. It comprises an efficacy evaluation ontology construction subsystem, a heterogeneous information network generation subsystem, a risk path retrieval and reasoning subsystem, and a multi-dimensional logic verification subsystem. These subsystems interact through a standardized graph data interface, forming a closed-loop data flow from physical characterization data input to biological efficacy evaluation output. The method executes the step of constructing an efficacy evaluation ontology containing a logic mask. The system establishes a knowledge graph pattern layer in a graph database, defining three core entities: characterization entities, used to map discretized physical state semantics such as rheological modulus, viscosity, and degradation rate; structural entities, used to characterize the microscopic cross-linking topology and porosity characteristics of the material; and failure entities, used to define terminal biological risks such as gel displacement, physical barrier damage, and induced inflammatory responses. To mitigate risks, the system pre-defines a set of physical consistency constraint rules describing mutually exclusive attribute pairs in the schema layer. This rule set consists of multiple logical triples, such as defining triples containing entities with high porosity, mutually exclusive relationships, and entities with extremely high mechanical strength, thus establishing the physical boundaries of graph reasoning. After completing ontology construction, the system performs graph instantiation and dynamic weight calculation steps. It acquires physical test data and corresponding biological efficacy data from multiple historical anti-adhesion hydrogel samples, mapping them to instance nodes in the schema layer. For the mapping of physical test data, a feature extraction algorithm is used to extract key inflection point features and slope features from the rheological test curves, and these feature values ​​are matched with the predefined discretization intervals in the schema layer, converting them into unique entity identifiers. When establishing weighted directed edges between nodes, the system binds a timestamp to each generated edge. During the inference and calculation process, the system obtains the current system time. And calculate the time difference. The time influence factor is calculated using a nonlinear decay function. And according to the formula Calculate the dynamic effective weights, where, The initial static weights are calculated based on historical statistical frequencies. The range of values ​​is And follow It increases and then decreases monotonically.

[0027] The system performs a risk path retrieval step based on a logical mask. It converts the physical data of the hydrogel to be evaluated into a set of target characterization nodes. Starting from these nodes, it performs a depth-constrained breadth-first multi-hop path search in a heterogeneous information network. While the search algorithm traverses each hop node, the system initiates a real-time subgraph matching process. This process parses the attribute semantic combinations of all intermediate nodes accumulated on the current path and compares these combinations with the physical consistency constraint rule set. If the attribute combination of the current path simultaneously contains mutually exclusive attribute pairs defined in the rule set, the system generates a path blocking instruction, forcibly setting the weight of the current candidate connected path to zero and terminating subsequent searches in that direction. Only paths that do not trigger the blocking instruction are marked as valid paths and proceed to the subsequent cumulative weight calculation stage. For biosafety indicators, this method introduces a logical arbitration mechanism based on a veto power subgraph. During the ontology construction stage, the system marks failed entities characterizing cytotoxicity and the acidity risk of degradation products as veto power nodes. While performing the main path search, the system simultaneously initiates a process targeting... In the connectivity scanning process of the veto node, if a connected subgraph leading to any veto node is detected from the target representation node, and the path weight of this connected subgraph is greater than a non-zero warning threshold, the system generates a highest-priority logical blocking instruction, ignores the performance score calculated by the main path search, locks the final evaluation result as unqualified, and outputs the connected subgraph as a risk warning basis. Before outputting the result, the system performs a noise-resistant verification step based on topology sensitivity. This step is based on the betweenness centrality algorithm. Among the identified effective paths with the highest weight, the system locks the key bridging node with the highest information flow capacity. The system constructs a logical copy of the heterogeneous information network in memory and applies a logical soft deletion mask to the key bridging node, prohibiting the path search algorithm from traversing the node. The system re-executes the path search in the copy, obtains the performance evaluation score of the alternative path, and calculates the difference rate between the original evaluation score and the alternative path score. If the difference rate is higher than the preset structural vulnerability threshold, the system uses a nonlinear penalty function to reduce the confidence of the final evaluation result.

[0028] The system further performs a counterfactual causality verification step. It identifies the key physical feature nodes that contribute the most to the inference path and generates a set of counterfactual virtual samples in memory. These virtual samples retain all attributes except for the key feature nodes, applying only minor numerical perturbations to the attribute values ​​of the key feature nodes that conform to physical laws. The system inputs these virtual samples into the network for inference, obtaining virtual evaluation results. The system compares the changing trends of the original evaluation results with those of the virtual evaluation results. If the trend contradicts pre-set rules of materials science physics, the original valid path is deemed to have a risk of spurious correlation, and its confidence level is lowered. For the hydrogel to be evaluated... In cases where the hydrogel is an isolated node in a heterogeneous information network, this method employs a cross-domain knowledge transfer mechanism based on structural isomorphism. The system extracts the microscopic topological features of the hydrogel to be evaluated and converts them into fingerprint subgraphs in graph format. The system performs subgraph isomorphic search in a pre-set general material knowledge base to identify anchor material entities containing the fingerprint subgraph. The system obtains the physical property association edges established for the anchor entities in the general library and projects these edges onto the current heterogeneous information network using a speculative type. Virtual speculative edges connecting the node to be evaluated and the corresponding physical property nodes are established, and these virtual edges are included in the calculation scope of multi-hop path search.

[0029] Example 1: In the batch screening scenario for the development of a novel modified hyaluronic acid anti-adhesion hydrogel, the research team faced a situation where the historical experimental data spanned three years and there were slight drifts in the synthesis process parameters between different batches. Simultaneously, the new formulation sample to be evaluated only had three sets of rheological test data, lacking corresponding in vivo biological experimental results. The system executed a map instantiation and dynamic weight calculation procedure, mapping over 500 sets of experimental data accumulated in the historical database over the past three years into instance nodes in a heterogeneous information network. The system identified some historical high-scoring samples that, although close to the current sample in terms of modulus value, had different timestamps generated. Time since current system More than two years have passed, and key components of the corresponding synthesis equipment have been replaced. The system has calculated a significant time difference. A lower time influence factor is generated by using a nonlinear decay function. And according to the formula By reducing the edge weights corresponding to this part of the historical data, this dynamic weighting mechanism ensures that the starting point of subsequent path searches is anchored to the effective feature space representing the latest technology level by reducing the weight of outdated process data on the current inference.

[0030] The system initiates a risk path retrieval based on the rheological characteristics of the hydrogel to be evaluated. A breadth-first search algorithm identifies a candidate path pointing to an entity with excellent anti-adhesion performance. Relying on the statistical co-occurrence relationship between high-porosity structures and high-mechanical-strength entities, the built-in set of physical consistency constraints is triggered when traversing intermediate nodes of this path. The real-time subgraph matching process detects that the attribute combination of the current path contains predefined mutually exclusive attribute pairs, meaning that high porosity and extremely high mechanical strength cannot physically coexist without the reinforcement of a specific crosslinking agent. The system generates a path-blocking instruction, forcibly setting the weight of the candidate path to zero and eliminating it. This process uses physical rules to filter out spurious causality in statistical inference, avoiding false causality in small sample data. The system avoids the pitfalls of pseudo-patterns. After excluding physically infeasible paths, it performs parallel logical arbitration based on a veto subgraph. Even though the sample exhibits suitable viscoelasticity and degradation rate in terms of physical parameters, the parallel scanning process identifies a connected subgraph between the degradation product nodes and the local pH drop failure entity in the deep layers of the graph. This failure entity has been marked as a risk node with veto power. Based on the logical arbitration mechanism, the system ignores the main path score, outputs an unqualified evaluation conclusion, and displays the causal chain from the acidity of the degradation products to the induction of inflammatory adhesions. This evaluation result redefines the comprehensive scoring problem as a binary logical judgment problem for a specific fatal defect, allowing researchers to directly focus on the biosafety shortcomings highlighted by the system.

[0031] Example 2: This example constructs a simulation test platform incorporating high noise and parameter drift to verify the effectiveness of the physical performance evaluation of the present invention under complex engineering environments and the rationality of the parameter boundaries. The test platform is built based on the graph neural network framework PyG and integrates a noise injection module for simulating rheometer signal fluctuations. The experimental data comes from the publicly available Hydrogel-BioCompat material database and contains rheological parameters (storage modulus) of 500 sets of hydrogels with different formulations. Loss modulus Loss factor The data, along with corresponding biological efficacy labels (anti-adhesion level, inflammation score), were used to simulate data uncertainty in real-world R&D scenarios. Gaussian white noise with a signal-to-noise ratio of 15 dB was artificially added to the experimental group data to characterize the measurement error and environmental vibration interference of the rheometer during long-term operation. The experiment verified the impact of the dynamic weight decay mechanism on the evaluation accuracy, providing a basis for quantifying the time decay factor. To assess its effectiveness, a set of gradient comparison experiments was designed. The historical datasets were divided into three groups based on their generation time: a recent group (within 6 months), a mid-term group (6-18 months), and a long-term group (over 18 months). A systematic bias simulating process drift was artificially introduced into the long-term group data (all datasets were compared with the previous one). (The value is uniformly increased by 15%). Under the two conditions of enabling and disabling the time decay mechanism, the same set of test samples are predicted separately. See Table 1, which shows the comparison data of prediction accuracy under different weight decay strategies.

[0032] Table 1: Verification Table of the Impact of Time Decay Mechanism on Evaluation Accuracy

[0033]

[0034] Data shows that after introducing process drift interference, the accuracy of the control group B, which did not use the attenuation mechanism, dropped to 72.1%, and the false positive rate surged, indicating that the model was misled by outdated and biased historical data. In contrast, the experimental group of this invention, which adopted the nonlinear exponential attenuation mechanism of this invention, achieved an accuracy of 86.8% under the same strong interference conditions, close to the level under interference-free conditions, and the false positive rate was effectively controlled at 5.1%. This result confirms that the time-aware dynamic weight algorithm can effectively suppress outdated data noise and achieve adaptive focusing on the current process state. Furthermore, to verify the scientific nature of the physical consistency constraint rule set in parameter boundary determination, a set of boundary detection experiments was designed for the mutually exclusive relationship between crosslinking density and swelling rate. The experiment constructed three virtual sample groups: sample group X (parameters are located in the safe area defined by the rule set), sample group Y (parameters are located in the critical area), and sample group Z (parameters are located in the mutually exclusive area, i.e., setting extremely low crosslinking density and extremely low swelling rate, violating the Florey-Rayner theory). See Table 2, which shows the interception effect of the logic mask mechanism on physically violating samples.

[0035] Table 2: Verification Table of Physical Consistency Constraints for Intercepting Abnormal Paths

[0036]

[0037] Table 2 shows that when the sample parameters enter a physically impossible mutually exclusive region (sample group Z), the number of valid inference paths generated by the system becomes zero, directly triggering the physical infeasibility blocking judgment. This contrasts with the normal inference of sample group X, proving that the logic masking mechanism is a logic defense based on physical laws, preventing the algorithm from generating illusory high scores in a data space lacking physical constraints. This experiment quantitatively verifies the effect of the nonlinear time decay mechanism in improving the effectiveness of evaluation by introducing simulation conditions of noise interference and parameter drift, and confirms the role of the physical consistency constraint rule set in eliminating pseudo-scientific formulas. The method of this invention can maintain the physical self-consistency of the evaluation logic and the reliability of the results when dealing with high-noise, non-stationary small sample data.

[0038] Example 3: This example combines Figures 1 to 3 This paper describes a smart evaluation method for the physical properties of an anti-adhesion hydrogel, such as... Figure 1 As shown, the overall execution process begins with the acquisition of physical test data, specifically rheological curves and enzymatic hydrolysis kinetic data. Simultaneously, an input time influence factor is introduced to perform dynamic weighting based on time decay of edge weights adjusted according to timestamps. A heterogeneous information network is generated to establish connections and weights for representative nodes. Based on this, the system traverses and performs multi-hop path search starting from the representative nodes, and parses path attribute combinations in real time. This process is subject to dual constraints from a logical masking mechanism and a set of physical consistency constraint rules. By blocking pseudo-causal paths and defining mutually exclusive attributes, subgraph matching and physical consistency logic verification are performed on logical triples. If mutual exclusion is triggered, the search returns; if a match is successful, the logical arbitration and weight calculation stage begins, including veto node scanning and counterfactual verification. Finally, the evaluation result is output, providing a pass / fail judgment or a failure risk warning.

[0039] like Figure 2 As shown in the figure, a two-dimensional coordinate system is constructed with the perturbation amplitude of key feature node attribute values ​​as the horizontal axis and the performance evaluation score as the vertical axis. The horizontal axis covers the perturbation range from -15% to +15%, and the vertical axis indicates the score range from 0 to 1.0. Three trend curves are plotted in the figure: the solid curve corresponds to the crosslinking density perturbation, the long dashed curve corresponds to the porosity perturbation, and the dotted curve corresponds to the degradation rate perturbation. All three curves reach a peak of 0.8 at the original value and show a decreasing trend as the perturbation amplitude increases in both positive and negative directions. Figure 3 As shown, the logical architecture of this invention uses a fishbone diagram to illustrate the four core dimensions of the technical branches supporting high-confidence intelligent evaluation results. The data processing enhancement branch includes time-decayed dynamic weights and heterogeneous information network construction; the physical logic constraint branch covers physical consistency rule sets, mutually exclusive attribute pairs blocking, and logical masking mechanisms; the robustness verification branch integrates topology sensitivity analysis, cross-domain knowledge transfer, and counterfactual causal verification; and the security logic arbitration branch consists of failure entity connectivity and veto power node scanning. Together, these branches constitute a complete technical system that ensures the logical self-consistency of the evaluation results.

[0040] Example 4: This example aims to provide targeted supplementation and engineering explanation of the core threshold calibration and physical rule evolution mechanism, eliminating the risks of the experience black box of parameter setting and the static rigidity of the rule system, and addressing the path hop count limitation. This key parameter, which determines the depth and efficiency of graph inference, is optimized using an adaptive calibration method based on semantic decay gradients. The system statistically analyzes the length distribution of all confirmed causal paths from represented entities to failed entities in a validated, high-quality labeled dataset. By examining the statistical relationship between path length and expert confidence scores, the system constructs a semantic decay function. ,in The path hop count is a function that characterizes the rate at which the strength of the causal relationship between nodes decays as the path length increases. The system will... Critical number of hops to drop to the preset noise baseline The baseline threshold is set, and the system introduces correction coefficients for different types of failure modes. Fine-tuning the baseline threshold involves setting a smaller threshold for short-chain failure modes such as gel displacement, which involve macroscopic physical and mechanical transmission. Value; for long-chain failure modes involving complex biochemical cascades, such as those inducing inflammatory responses, a larger value is set. Value, final hop limit From the formula Dynamic calculations show that this procedure ensures the search depth can adaptively cover the causal radius of different failure modes.

[0041] To address the construction and maintenance of a set of rules for physical consistency constraints, this embodiment discloses a dynamic evolution mechanism driven by both expert knowledge and data feedback. To solve the problem of the initial rule set lagging behind when facing novel modified materials, the system introduces a rule update module based on conflict learning. When the system frequently encounters false negative conflict events where high-confidence statistical paths are blocked by physical rules in actual reasoning, and subsequent experimental verification shows that the path is actually effective, this module is activated. The module automatically extracts the attribute pairs involved in the conflict path and marks them as suspected rule defects. The system submits these suspected defects to experts for review or triggers a rule relaxation algorithm after accumulating sufficient verification data. If multiple experiments confirm that a certain novel crosslinking agent can enable the coexistence of high porosity and high mechanical strength, the system automatically adds an exclusion condition to the original mutually exclusive rules, that is, when the node attribute contains the novel crosslinking agent, the mutually exclusive constraint is released. If the system finds that a certain type of path allowed by the rules leads to high-frequency failure in actual applications, it automatically generates new mutually exclusive rules and adds them to the rule base. This dynamic evolution mechanism ensures that the physical logic layer of the evaluation system can continuously self-correct with the development of material technology and maintain adaptability to the properties of new materials.

[0042] Example 5: This example provides a standardized engineering procedure for offline calibration and field environment adaptive calibration of a physical consistency constraint rule set. This procedure aims to eliminate the risk of judgment benchmark drift caused by differences in environmental parameters between different regions, climate conditions, and equipment batches. The system executes the offline calibration procedure to construct and calibrate the basic parameters of the physical consistency constraint rule set. The procedure selects three sets of standard reference materials, whose rheological characteristics correspond to the safe zone, critical zone, and mutually exclusive zone boundaries in the rule set, respectively. Under a standard controlled environment of 25℃±0.1℃ and 50%±5% relative humidity, a high-precision rheometer is used to perform a full-spectrum scan on the three sets of standard reference materials to obtain the benchmark feature dataset. The benchmark dataset is input into the rule engine to perform boundary pressure testing, and the judgment threshold of the rule set for the critical zone sample is recorded. Based on the test results, the system uses the least squares method to fine-tune the numerical boundary parameters in the rule set until the model's judgment error for the standard materials converges to the preset allowable range. The calibration parameters generated in this process are solidified into the initial configuration file of the rule set and distributed to each deployment site.

[0043] When the evaluation system is deployed to a new R&D center with different environments, the system automatically triggers the pre-deployment calibration procedure. The system calls the built-in environmental sensitivity verification module to detect the deviation of environmental parameters such as current air pressure and average humidity from the standard calibration environment. If the deviation exceeds a preset threshold, the system prompts the operator to perform baseline drift correction. The operator uses a certified local standard sample block to perform single-point verification tests. The system collects the test data and compares it with the factory fingerprint data of the standard sample block to calculate the environmental impact factor. Next, the system uses this factor to linearly correct the environmentally sensitive threshold in the physical consistency constraint rule set, generating a runtime rule copy adapted to the local environment. The corrected system needs to pass a set of confirmation tests containing known qualified and unqualified products. When the system's classification accuracy for the confirmed samples reaches 100%, it enters the formal sample evaluation process.

[0044] Example 6: This example provides a standardized parameter calibration and model initialization procedure for a novel anti-adhesion hydrogel evaluation system. This procedure aims to eliminate the engineering black box risk caused by the lack of basis for setting key thresholds, and establishes a set of operation procedures from offline data preprocessing to online parameter adaptive adjustment. For the core issues of minimum confidence threshold and risk path weight threshold setting in the evaluation system, this procedure establishes an offline optimization method based on receiver operating characteristic (ROC) curves. The system uses a historical confirmed case labeled dataset containing verified gel displacement or inflammatory response cases, and draws ROC curves under different parameter settings by traversing all possible threshold combinations within the preset parameter range. Based on the Youden exponent maximization principle, the optimal threshold point that makes the sum of sensitivity and specificity reach its peak is calculated and locked. The parameter set generated in this process is fixed as the system's factory default configuration as the benchmark for subsequent online operation.

[0045] To address the model adaptability issues caused by differences in data distribution across various application scenarios, this procedure constructs an online parameter adaptive calibration logic. During operation, the system continuously monitors the distribution characteristics of real-time evaluation results. When it detects that the evaluation results of the most recent 50 samples deviate from the preset benchmark distribution by more than a preset statistical level over a continuous period, an online calibration subroutine is automatically triggered, introducing a dynamic compensation factor. Fine-tuning of risk path weight thresholds and compensation factors. The calculation is based on the ratio of the average feature strength of the current sample set to the average feature strength of the benchmark dataset. By applying this compensation factor in real time, the system dynamically corrects the evaluation benchmark drift caused by sample batch differences or environmental changes.

[0046] Example 7: This example provides a standardized calibration method for core parameters to ensure the absolute reproducibility of the evaluation system. Based on the information half-life theory in materials science, the influence of historical data on current decisions is negatively correlated with its generation time, and the decay follows a smooth nonlinear path. This scheme selects an exponential decay model to characterize the time effect and the time influence factor. The calculation formula uses an exponential decay form, as follows: ,in, The time-related factor is dimensionless and has a range of values. , Generate the time difference between the timestamp of historical hydrogel sample data and the current system time, in days. The time decay constant is expressed in units of 1. The weight decay rate is determined, and the range of values ​​is taken. Attenuation constant Calibration procedure: Select a historical sample dataset with clear performance labels (qualified / unqualified) and a time span of at least three years, and define the maximum allowable time difference. for Day, set the minimum acceptable weighting factor at this time. for ;Will Tianhe Substitute into the formula to calculate the time decay constant. Preferred value, i.e. ;set up The benchmark project value is , Exceed At this time, the weights are forced to zero, so that the inference does not rely on extremely outdated data.

[0047] To identify the key feature nodes with the highest contribution, a betweenness centrality algorithm is used. The output values ​​are then normalized and thresholded for selection. Calculation and screening procedures: For any non-representational entity node in a heterogeneous information network Calculate the betweenness centrality among all valid paths. ;Will Normalization to nodes Path contribution The calculation is as follows: ,in It is the set of all intermediate nodes. Dimensionless, range All nodes Values ​​are sorted in descending order, and those with cumulative contribution reaching a preset threshold are selected. The minimum set of nodes is used as the set of key feature nodes, and a cumulative contribution threshold is set. The project value is To address high-noise and highly vulnerable paths, the system, during the noise immunity verification step, considers the difference between the original evaluation score and the score of the alternative path. Above the structural vulnerability threshold At that time, a nonlinear penalty is initiated, and the penalty function is... The determination and application employ a nonlinear penalty function based on the Sigmoid function, as follows: ,in To represent the degree of decrease in confidence level of the final evaluation result, a dimensionless value is used, taking a range of values. , The percentage difference between the original evaluation score and the score of the alternative path is dimensionless and has a range of values. , The structural vulnerability threshold is dimensionless, and its value is taken as [value] in engineering applications. , This is the sensitivity adjustment coefficient, dimensionless, with an engineering value of [value missing]. Confidence of the final evaluation results The adjustment is calculated using the following formula: This is the initial confidence level, and the corrected level. Used to reflect the stability of evaluation results.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart evaluation method for the physical properties of anti-adhesion hydrogels, characterized in that, Includes the following steps: Step 101: Establish a knowledge graph schema layer in the database, define representation entities, structural entities and invalid entities, establish weighted directed edges connecting each entity, and define a set of physical consistency constraint rules describing mutually exclusive attribute pairs. Use the physical consistency constraint rule set as the logical verification layer for graph traversal. Step 102: Obtain physical test data and biological efficacy data of multiple sets of historical hydrogel samples, map the physical test data and biological efficacy data into instance nodes under the knowledge graph pattern layer, establish node connections and calculate edge weights based on the efficacy performance of historical hydrogel samples, and form a heterogeneous information network. Step 103: Convert the physical data of the hydrogel to be evaluated into a target characterization node, and perform a multi-hop path search in the heterogeneous information network starting from the target characterization node to identify candidate connected paths to the failed entity. When searching and traversing each hop node, the accumulated attribute semantic combination of the current path is parsed, and the attribute semantic combination is matched with the physical consistency constraint rule set in the subgraph. Step 104: In response to the matching of any mutually exclusive attribute pair in the attribute semantic combination, a path blocking instruction is generated and the weight of the current candidate connected path is forcibly set to zero; the cumulative weight is calculated only for valid paths that have not triggered the path blocking instruction, and when the cumulative weight exceeds a preset threshold, it is determined that there is a risk of failure and the corresponding valid path is output.

2. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, In step 101, the physical consistency constraint rule set contains logical triples that define mutually exclusive attribute pairs. The subgraph matching in step 103 specifically includes: parsing the attribute semantics of all intermediate nodes contained in the currently identified candidate connected path; matching and verifying the attribute semantic combination of the intermediate nodes with the logical triples; when the attribute semantic combination contains mutually exclusive attribute pairs defined in the logical triples, a path blocking instruction is triggered; mutually exclusive attribute pairs are attribute combinations that are physically incompatible or will inevitably lead to specific failures based on common sense in materials science physics.

3. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, Step 102 also includes binding a timestamp to each generated weighted directed edge, and calculating the edge weight specifically includes step 301 of dynamically adjusting the weight based on time decay logic, obtaining the current system time, and calculating the time difference between the generated timestamp and the current system time. The time influence factor is calculated using a preset nonlinear decay function. The dynamic effective weight of the weighted directed edge is calculated according to the following formula. : ,in, The initial static weights of the weighted directed edges, and the time influence factor. The range of values ​​is And the time difference The weight increases and then decreases monotonically. Multi-hop path search and cumulative weight calculation are performed using dynamic effective weights.

4. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, Step 101 also includes marking specific failed entities as veto nodes. Before step 104, a logical arbitration step 401 is performed, initiating a connectivity scanning process for veto nodes in parallel in the heterogeneous information network. If a connected subgraph leading to any veto node is detected for the target representation node, and the path weight of the connected subgraph is greater than a non-zero warning threshold, a logical blocking instruction with the highest priority is generated. In response to the logic blocking instruction, the calculation result of the cumulative weight is ignored, the evaluation result of the hydrogel to be evaluated is forcibly locked as unqualified, and the connected subgraph is output as the basis for rejection.

5. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, Before step 104, the process includes a counterfactual causality verification step 501, which identifies the key feature nodes with the highest contribution in the effective path based on the betweenness centrality algorithm; generates a set of counterfactual virtual samples in memory, keeping all attributes except for the key feature nodes unchanged, and applying only small numerical perturbations to the attribute values ​​of the key feature nodes in accordance with physical laws; and inputs the counterfactual virtual samples into a heterogeneous information network for inference to obtain virtual evaluation results. Compare the trends of the evaluation results of the hydrogel to be evaluated with those of the virtual evaluation results. If the trend of change contradicts the pre-set material science and physics common sense rules, the effective path is judged to have a risk of spurious correlation and its confidence level is reduced.

6. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, After step 103 is completed and before step 104, a noise reduction verification step 601 based on topology sensitivity is also included, in which the key bridging node with the highest information flow carrying capacity is locked in the identified candidate connected paths. In the logical replica of the heterogeneous information network, a logical soft deletion mask is applied to the key bridging node to prevent the path search algorithm from traversing the key bridging node. Re-execute path search in the logical copy after applying the logical soft delete mask, and obtain the performance evaluation score of the alternative path; Calculate the difference rate between the original evaluation score and the effectiveness evaluation score of the alternative path. When the difference rate is higher than the preset structural vulnerability threshold, use a nonlinear penalty function to reduce the confidence of the final evaluation result.

7. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, The method also includes a cross-domain knowledge transfer step 701 based on structural isomorphism. In response to the fact that the hydrogel to be evaluated is an isolated node in the heterogeneous information network, the microscopic topological features of the hydrogel to be evaluated are extracted and transformed into a fingerprint subgraph. A subgraph isomorphism search is performed in a pre-set general material basic knowledge base to identify anchor material entities containing fingerprint subgraphs. The physical property association edges established by the anchor material entities in the general material basic knowledge base are obtained. The physical property association edges are mapped to the heterogeneous information network as speculation types to establish virtual speculation edges connecting the hydrogel to be evaluated and the corresponding physical property nodes, and the virtual speculation edges are included in the calculation scope of multi-hop path search.

8. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, In step 101, the characterization entities include modulus semantic nodes and degradation rate semantic nodes generated through discretization mapping, and the structural entities include attribute nodes characterizing the micro-crosslinking topology of the material. Failure entities include nodes that characterize the risk of gel displacement and nodes that characterize the risk of physical barrier failure.

9. The intelligent evaluation method for the physical properties of anti-adhesion hydrogels according to claim 1, characterized in that, The process of converting the physical data of the hydrogel to be evaluated into target characterization nodes specifically includes: acquiring the rheological test curve data and in vitro enzymatic hydrolysis kinetic data of the hydrogel to be evaluated; extracting key inflection point features and slope features from the test curve data using a preset feature extraction algorithm; and matching the key inflection point features and slope features with the predefined discretized intervals in the knowledge graph pattern layer to map them into unique characterization entity identifiers.

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