Method for monitoring the repair process of absorbable biomaterials

Abstract

The application relates to the technical field of medical intelligent decision-making, in particular to a repair process monitoring method of absorbable biomaterials; the application realizes real-time collection of state parameters such as temperature, pH value, stress, strain and conductivity of the absorbable biomaterials through skin patch sensors and implantable biocompatible sensors, and can accurately identify different stages of the repair process in combination with data processing and analysis, can effectively optimize the repair process, improve the repair effect, and avoid potential problems caused by inaccurate repair progress; the application adopts multi-sensor data collection and efficient data fusion technology, accurately masters the degradation process of the repair material and the tissue repair condition through multi-dimensional monitoring, timely discovers abnormal degradation or local instability of the repair material, and ensures that the degradation process of the repair material is accurately controlled, and the repair effect is maximally ensured.

Description

Technical Field

[0001] The present invention relates to the field of medical intelligent decision-making technology, and in particular to a method for monitoring the repair process of absorbable biomaterials. Background Art

[0002] In the medical and biomedical fields, absorbable biomaterials are widely used in various repair procedures, particularly in fracture repair, trauma treatment, and soft tissue repair. Absorbable biomaterials gradually degrade during the repair process and are naturally absorbed by the body, avoiding the secondary surgical removal required by traditional materials. Common absorbable biomaterials include absorbable sutures, bone fillers, silicone gel, and absorbable nails.

[0003] However, despite the widespread use of absorbable biomaterials in medical repair, how to accurately monitor their repair process, especially during degradation, absorption, and tissue integration, remains a challenge. Traditional monitoring methods rely mainly on regular X-ray examinations, CT scans, or MRI examinations, and these methods have the following limitations:

[0004] First, traditional monitoring methods cannot monitor the degradation process of materials in real time and can only indirectly reflect the progress of repair through imaging methods.

[0005] Secondly, traditional monitoring methods only focus on the image information after repair, ignoring the biophysical and chemical changes during the material degradation process, such as important parameters such as temperature, pH value, stress and strain.

[0006] Finally, traditional monitoring methods have difficulty in identifying anomalies generated during the material absorption process and lack timely intervention and optimization strategies.

[0007] In view of the above problems, it is necessary to propose a monitoring method for the repair process of absorbable biomaterials. Summary of the Invention

[0008] The purpose of the present invention is to solve the problems existing in the background technology and to provide a method for monitoring the repair process of absorbable biomaterials.

[0009] The purpose of the present invention can be achieved through the following technical solutions:

[0010] A method for monitoring the repair process of absorbable biomaterials comprises the following steps:

[0011] Step 1: Environmental parameter collection and preprocessing;

[0012] The state parameters of the absorbable biomaterial are collected through skin patch sensors and implantable biocompatible sensors, and the state parameters include temperature, pH value, stress, strain and conductivity.

[0013] At preset time intervals, the original data of the state parameters of the skin patch sensor and the implantable biocompatible sensor are collected through wireless transmission, and the collected original data of the state parameters are transmitted to the computer, and the pre-processed state parameters are obtained through noise and signal smoothing processing.

[0014] The noise and signal smoothing process is specifically as follows:

[0015] The original data of temperature, pH value, stress, strain and conductivity collected at each preset time interval t are collected and recorded as the original data set , and input it into the Gaussian filter to balance the signal and remove the high-frequency noise. The core operation formula of the Gaussian filter is ;in is a set of pre-processed state parameters obtained after Gaussian filtering, including pre-processed temperature, pH value, stress, strain and conductivity; σ is the preset filter standard deviation, which is used to control the degree of smoothing; N is the size of the preset sliding window, that is, the number of neighboring data points considered around the current data point t, which is used to control the reference range of the Gaussian filter; i is the original data index, which is used to specify the specific number of the reference value of the original data in the Gaussian filter within the current window; t is the time index of the current data point, that is, the position of the current data point being processed by the Gaussian filter.

[0016] The set of preprocessed state parameters obtained after Gaussian filtering Send it to the data processing center for further data fusion and feature extraction analysis.

[0017] Step 2: Feature extraction, analysis and data fusion;

[0018] Feature extraction and data fusion are performed on the set of preprocessed state parameters, and preset values ​​are matched according to the data fusion results to evaluate the state of the absorbable biomaterial.

[0019] PCA principal component analysis is used to perform data feature extraction operations. The specific process is as follows:

[0020] Extract the set of preprocessed state parameters The state parameters in the equation include temperature, pH value, stress, strain and conductivity, and are input into the standardized formula The standardized temperature X1(t), pH value X2(t), stress X3(t), strain X4(t) and conductivity X5(t) are obtained; is the mean of the state parameter items, including the mean of temperature, pH value, stress, strain and conductivity; is the standard deviation of the state parameter items, including the standard deviation of temperature, pH value, stress, strain and conductivity; is the state parameter item obtained after standardization, where t is the data collection time.

[0021] For the standardized temperature X1(t), pH value X2(t), stress X3(t), strain X4(t) and conductivity X5(t), the formula Calculate the covariance matrix C, where m is the number of samples; and is the data matrix of state parameters and the transposed matrix of the data matrix.

[0022] According to the size of the eigenvalue, the first k are selected as the principal component data, where k is the preset number of features. Among them, the selected principal component data is the state parameter sub-item data that contains the most information. All principal component data form the principal component data matrix .

[0023] As a preferred embodiment of the present invention, the objective function is established based on the GP Bayesian optimization model, and the principal component data matrix is ​​combined with the Perform data fusion and matching operations on the extracted feature parameters. The specific process is as follows:

[0024] A set of repair process stages of absorbable biomaterials are preset, including p=1: physical support stage; p=2: fibrosis stage; p=3: local degradation stage; p=4: tissue integration stage; p=5: complete absorption stage; p=6: long-term stability stage, which respectively correspond to the absorbable biomaterials adaptation process of the patient in the repair process from complete wrapping, initial healing, local healing, complete healing, postoperative recovery and complete recovery.

[0025] Record the characteristic data of temperature, pH value, stress, strain and conductivity of absorbable biomaterials at each stage of the repair process to obtain the preset values ​​under their respective ideal conditions .

[0026] Constructing the objective function in the GP Bayesian optimization model: ,measures the quality of the current restoration process;

[0027] Where fp(x) is the difference measure between the current repair state and the process stage p, is the reference weight of each preset parameter; where xj(t) is the state parameter item data in the principal component data matrix, including temperature, pH value, stress, strain and conductivity; where is the preset value of xj(t) under ideal conditions. is the original data index; n is the principal component data matrix the total number of original data in the data set.

[0028] After each time the sensor collects new data, calculate its principal component data matrix and into the objective function in the GP Bayesian optimization model, get the difference measure about each repair process stage, select the minimum value minfp(x) in it, and extract its corresponding process stage index p. If minfp(x) is less than the preset threshold, it is determined that the repair process stage represented by stage index p is matched.

[0029] In the repair process, after each time the sensor collects new data, the Bayesian optimization model will adjust the repair strategy according to the current data, and output the repair process stage p matched.

[0030] Step three, repair process monitoring based on repair process stage identification;

[0031] According to the repair process stage matched, carry out targeted process monitoring.

[0032] For p=1: physical support stage; p=2: fibrosis stage; p=3: local degradation stage; in the repair process of absorbable biological material, calculate the stress-strain relationship, draw the stress-strain distribution diagram of the absorbable biological material, and simulate the repair process of the material through the finite element analysis model. The stress-strain distribution diagram is visually outputted.

[0033] Draw the stress-strain relationship diagram of the micro-unit.

[0034] When it is identified that the stress or strain relationship appearing in the stress-strain relationship diagram is in the elastic strain region, and the strain is greater than the preset threshold, it is determined that the absorbable biological material will soon appear cracks or damage, and the cracking warning signal is outputted.

[0035] When it is identified that the stress or strain appearing in the stress-strain relationship diagram exits the elastic strain region and enters the plastic strain, it is determined that the absorbable biological material has appeared cracks or damage, the plastic region volume is calculated, the damage degree is judged, and the position and size of the damage region are displayed on the computer.

[0036] For p=4: tissue integration stage; p=5 complete absorption stage; p=6: long-term stable stage.

[0037] Temperature and pH value are analyzed, if the temperature or pH value exceeds the preset range, the absorption warning signal is outputted, prompting possible abnormal situation, and judging the fast degradation or local instability.

[0038] Step four, data storage and feedback;

[0039] All sensor data, results of the repair process, and the time, type, and corresponding location of the generated warning signals are stored in the database in real time to facilitate subsequent analysis and research.

[0040] Based on the comparison of current repair results with historical data, the system generates a report and sends it to the user to provide data support and repair effect evaluation.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This invention uses skin patch sensors and implantable biocompatible sensors to collect real-time state parameters of absorbable biomaterials, such as temperature, pH, stress, strain, and conductivity. Combined with data processing and analysis, it can accurately identify the different stages of the repair process (e.g., physical support, fibrosis, degradation, etc.). By fusing and matching data using a Bayesian optimization model and principal component analysis (PCA), it not only accurately locates the repair process but also enables real-time adjustment of repair strategies to ensure an efficient and smooth repair process. This real-time monitoring and precise matching capability can effectively optimize the repair process, enhance repair effectiveness, and avoid potential problems caused by inaccurate repair processes.

[0043] 2. The present invention adopts multi-sensor data acquisition and efficient data fusion technology, and accurately grasps the degradation process of the repair material and the tissue repair situation through multi-dimensional monitoring of temperature, pH value, stress, strain and conductivity. By using the GP Bayesian optimization model and data fusion analysis, the repair process can be dynamically adjusted according to real-time data, and abnormal degradation or local instability of the repair material can be discovered in time. For example, by monitoring the changing trends of temperature and pH values, it is possible to effectively evaluate whether the material is degrading as expected, and avoid the problem of repair failure caused by too fast or too slow degradation of the material. This technology can ensure that the degradation process of the repair material is precisely controlled and the repair effect is guaranteed to the maximum extent;

[0044] 3. At different stages of the repair process, the present invention can combine stress-strain distribution diagrams and finite element analysis models to conduct real-time evaluation of the mechanical properties of the material. When it is found that the stress or strain of the material exceeds the preset safety range, the system can automatically trigger an early warning and provide a specific damage assessment. For example, when the material enters the plastic strain zone, the system can promptly calculate the volume of the damaged area, identify the risk of potential cracks or breakage, and visualize it on the computer. This intelligent early warning mechanism can help quickly identify and locate material damage, ensure the safety and stability of the repair process, and avoid repair failure or further damage due to untimely detection of damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0046] Figure 1 is a flow chart of the method of the present invention; DETAILED DESCRIPTION

[0047] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, the method for monitoring the repair process of absorbable biomaterials comprises the following steps:

[0049] Step 1: Environmental parameter collection and preprocessing;

[0050] The state parameters of the absorbable biomaterial are collected through skin patch sensors and implantable biocompatible sensors, and the state parameters include temperature, pH value, stress, strain and conductivity.

[0051] It should be noted that the biocompatible sensor is biocompatible and will not trigger an immune response. It is usually made of materials with good biocompatibility, such as silicon, carbon nanotubes, metal oxides, etc., and is encapsulated in combination with a biocompatible polymer.

[0052] At preset time intervals, the original data of the state parameters of the skin patch sensor and the implantable biocompatible sensor are collected through wireless transmission, and the collected original data of the state parameters are transmitted to the computer, and the pre-processed state parameters are obtained through noise and signal smoothing processing.

[0053] The noise and signal smoothing process is specifically as follows:

[0054] The original data of temperature, pH value, stress, strain and conductivity collected at each preset time interval t are collected and recorded as the original data set , and input it into the Gaussian filter to balance the signal and remove the high-frequency noise. The core operation formula of the Gaussian filter is ;in The set of pre-processed state parameters obtained after Gaussian filtering operation, including pre-processed temperature, pH value, stress, strain and conductivity; wherein σ is a preset filter standard deviation, used to control the degree of smoothing; wherein N is a preset size of the sliding window, i.e. the number of adjacent data points considered around the current data point t, used to control the reference range of Gaussian filtering; wherein i is the original data index, used to specify the specific number of the reference value of the original data in the current window in Gaussian filtering; wherein t is the time index of the current data point, i.e. the position of the current data point being processed by Gaussian filtering.

[0055] The set of pre-processed state parameters obtained after Gaussian filtering operation is sent to the data processing center for further data fusion and feature extraction analysis.

[0056] Step two, feature extraction analysis and data fusion;

[0057] The set of pre-processed state parameters is subjected to feature extraction and data fusion, and the preset value is matched according to the data fusion result to evaluate the state of the absorbable biological material.

[0058] PCA principal component analysis is used for data feature extraction operation, and the specific process is as follows:

[0059] The set of pre-processed state parameters The state parameter items in the set of pre-processed state parameters, including temperature, pH value, stress, strain and conductivity, are input into the standardization formula to obtain the standardized temperature X1(t), pH value X2(t), stress X3(t), strain X4(t) and conductivity X5(t); wherein is the mean value of the state parameter items, including the mean values of temperature, pH value, stress, strain and conductivity; wherein is the standard deviation of the state parameter items, including the standard deviations of temperature, pH value, stress, strain and conductivity; wherein is the state parameter item obtained after standardization. Wherein t is the data acquisition time.

[0060] For the standardized temperature X1(t), pH value X2(t), stress X3(t), strain X4(t) and conductivity X5(t), the covariance matrix C is calculated by the formula , wherein m is the number of samples; wherein and are the data matrix of the state parameters and the transpose matrix of the data matrix.

[0061] According to the size of the eigenvalue, the first k are selected as the principal component data, where k is the preset number of features. Among them, the selected principal component data is the state parameter sub-item data that contains the most information. All principal component data form the principal component data matrix .

[0062] Furthermore, the objective function is established based on the GP Bayesian optimization model, combined with the principal component data matrix Perform data fusion and matching operations on the extracted feature parameters. The specific process is as follows:

[0063] A set of repair process stages of absorbable biomaterials are preset, including p=1: physical support stage; p=2: fibrosis stage; p=3: local degradation stage; p=4: tissue integration stage; p=5: complete absorption stage; p=6: long-term stability stage, which respectively correspond to the absorbable biomaterials adaptation process of the patient in the repair process from complete wrapping, initial healing, local healing, complete healing, postoperative recovery and complete recovery.

[0064] Record the characteristic data of temperature, pH value, stress, strain and conductivity of absorbable biomaterials at each stage of the repair process to obtain the preset values ​​under their respective ideal conditions .

[0065] Constructing the objective function in the GP Bayesian optimization model: , measure the quality of the current restoration process;

[0066] Where fp(x) is the difference measure between the current repair state and the process stage p, is the reference weight of each preset parameter; where xj(t) is the state parameter item data in the principal component data matrix, including temperature, pH value, stress, strain and conductivity; where is the preset value of xj(t) under ideal conditions. is the original data index; n is the principal component data matrix The total number of raw data in .

[0067] Each time the sensor collects new data, its principal component data matrix is ​​calculated This is then incorporated into the objective function of the GP Bayesian optimization model to obtain the difference metrics for each repair process stage. The minimum value, minfp(x), is selected and its corresponding process stage index, p, is extracted. If minfp(x) is less than a preset threshold, the repair process stage represented by stage index p is considered matched.

[0068] During the repair process, each time the sensor collects new data, the Bayesian optimization model adjusts the repair strategy based on the current data and outputs the repair process stage p obtained through matching.

[0069] Step 3: Monitoring the repair process based on identification of repair process stages;

[0070] Targeted process monitoring is performed based on the matched repair process stages.

[0071] For p=1: physical support stage; p=2: fibrosis stage; p=3: local degradation stage; during the repair process of absorbable biomaterials, the stress-strain relationship is calculated, the stress-strain distribution diagram of the absorbable biomaterial is drawn, the material repair process is simulated through a finite element analysis model, and the stress-strain distribution diagram is visualized.

[0072] Draw the stress-strain diagram of the microunit.

[0073] When the stress or strain relationship in the stress-strain relationship diagram is identified as being in the elastic strain region and the strain is greater than a preset threshold, it is determined that the absorbable biomaterial is about to crack or break, and a rupture warning signal is output.

[0074] When it is recognized that the stress or strain appearing in the stress-strain relationship diagram has exited the elastic strain region and entered the plastic strain region, it is determined that the absorbable biomaterial has cracked or been damaged, the volume of the plastic region is calculated, the degree of damage is determined, and the location and size of the damaged region are displayed on a computer.

[0075] It should be noted that cracks or breakage reflect whether the material can provide sufficient mechanical properties in the initial support, especially in the physical support stage (p=1). The main function of the repair material is to provide physical support for the damaged area and ensure the stability of the tissue or bone. If cracks or breakage occur at this stage, it may mean that the repair material fails to provide sufficient support strength, thereby affecting the healing process. In this case, it is crucial to detect and treat cracks or breakage as early as possible to prevent the damage from expanding and affecting the repair process. In the fibrosis stage (p=2) and the local degradation stage (p=3), although the degradation and absorption process of the material begins, the material still needs to maintain certain mechanical properties to support the repair of surrounding tissues. At this stage, the appearance of cracks or breakage may indicate that the repair material degrades too quickly or is subjected to uneven force, affecting tissue integration and repair effects.

[0076] For p=4: organizational integration stage; p=5: complete absorption stage; p=6: long-term stability stage.

[0077] Targeted analysis is performed on temperature and pH values. If the temperature or pH value exceeds the preset range, an absorption warning signal is output to indicate possible abnormal conditions and determine whether rapid degradation or local instability has occurred.

[0078] It's important to note that temperature and pH are key parameters that reflect the material's degradation and absorption processes. As the material degrades, local temperatures may rise, and pH may fluctuate, especially during periods of inflammation or active cellular repair. These changes must be monitored in real time to ensure they remain within the ideal range.

[0079] Step 4: Data storage and feedback;

[0080] All sensor data, results of the repair process, and the time, type, and corresponding location of the generated warning signals are stored in the database in real time to facilitate subsequent analysis and research.

[0081] Based on the comparison of current repair results with historical data, the system generates a report and sends it to the user to provide data support and repair effect evaluation.

[0082] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0083] It should also be understood that the terms used in this disclosure are for the purpose of describing particular embodiments only and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations;

[0084] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for monitoring the repair process of absorbable biomaterials, characterized in that: The following steps are involved: Step 1: Environmental parameter collection and preprocessing; The state parameters of the absorbable biomaterial are collected by using a skin patch sensor and an implantable biocompatible sensor, wherein the state parameters include temperature, pH value, stress, strain and conductivity; and the collected state parameters are subjected to noise and signal smoothing by preprocessing; Step 2: Feature extraction, analysis and data fusion; Principal component analysis (PCA) is used to extract features from the preprocessed state parameter set. The extracted features are then fused using Bayesian optimization. The data fusion results are then matched to preset values ​​to evaluate the state of the absorbable biomaterial and match it to a preset set of repair process stages for the absorbable biomaterial, including the physical support stage, fibrosis stage, local degradation stage, tissue integration stage, complete absorption stage, and long-term stability stage. The specific process of data fusion of the extracted features is as follows: Matching operation, the specific process is: A set of repair process stages for absorbable biomaterials is preset, including p=1: physical support stage; p=2: fibrosis stage; p=3: local degradation stage; p=4: tissue integration stage; p=5: complete absorption stage; and p=6: long-term stability stage, which respectively correspond to the patient's adaptation process to the absorbable biomaterial during the repair process, from complete encapsulation, initial healing, partial healing, complete healing, postoperative recovery, and complete recovery. Record the characteristic data of temperature, pH value, stress, strain and conductivity of absorbable biomaterials at each stage of the repair process to obtain the preset values ​​under their respective ideal conditions ; Constructing the objective function in the GP Bayesian optimization model: , Measuring the quality of current restoration processes; Where fp(x) is the difference measure between the current repair state and the process stage p, is the reference weight of each preset parameter; Where xj(t) is the state parameter item data in the principal component data matrix, including temperature, pH value, stress, strain and conductivity; is the preset value of xj(t) under ideal conditions; Where j principal component data matrix is the original data index; n is the principal component data matrix The total number of raw data in ; Each time the sensor collects new data, its principal component data matrix is ​​calculated And bring it into the objective function of the GP Bayesian optimization model to obtain the difference measurement of each repair process stage; select the minimum value minfp(x) and extract its corresponding process stage index p; If minfp(x) is less than the preset threshold, it is determined that the repair process stage represented by the stage index p is matched; During the repair process, each time the sensor collects new data, the Bayesian optimization model adjusts the repair strategy based on the current data and outputs the repair process stage p obtained through matching; Step 3: Monitoring the repair process based on identification of repair process stages; Targeted process monitoring is performed based on the matched repair process stages; stress and strain monitoring is performed on the physical support stage, fibrosis stage, and local degradation stage; Temperature and pH monitoring during tissue integration, complete absorption, and long-term stability; Step 4: Data storage and feedback; All sensor data, the results of the repair process, and the time, type, and corresponding location of the generated warning signals are stored in the database in real time to facilitate subsequent analysis and research; Based on the comparison of current repair results with historical data, the system generates a report and sends it to the user to provide data support and repair effect evaluation.

2. The method for monitoring the repair process of absorbable biomaterials according to claim 1, characterized in that: The specific process of state parameter collection is as follows: At preset time intervals, the original data of the state parameters of the skin patch sensor and the implantable biocompatible sensor are collected through wireless transmission, and the collected original data of the state parameters are transmitted to the computer, and the pre-processed state parameters are obtained through noise and signal smoothing processing.

3. The method for monitoring the repair process of absorbable biomaterials according to claim 1, characterized in that: The specific process of noise and signal smoothing is: The original data of temperature, pH value, stress, strain and conductivity collected at each preset time interval t are collected and recorded as the original data set , and input it into the Gaussian filter to balance the signal and remove the high-frequency noise. The core operation formula of the Gaussian filter is ;in is the set of pre-processed state parameters obtained after Gaussian filtering, including pre-processed temperature, pH value, stress, strain and conductivity; σ is the preset filter standard deviation, which is used to control the degree of smoothing; N is the size of the preset sliding window, that is, the number of neighboring data points considered around the current data point t, which is used to control the reference range of the Gaussian filter; i is the original data index, which is used to specify the specific number of the reference value of the original data in the Gaussian filter within the current window; t is the time index of the current data point, that is, the position of the current data point being processed by the Gaussian filter; The set of preprocessed state parameters obtained after Gaussian filtering Send it to the data processing center for further data fusion and feature extraction analysis.

4. The method for monitoring the repair process of absorbable biomaterials according to claim 1, characterized in that: The specific process of feature extraction for the set of preprocessed state parameters is as follows: Extract the set of preprocessed state parameters The state parameters in the equation include temperature, pH value, stress, strain and conductivity, and are input into the standardized formula The standardized temperature X1(t), pH value X2(t), stress X3(t), strain X4(t) and conductivity X5(t) are obtained; is the mean of the state parameter items, including the mean of temperature, pH value, stress, strain and conductivity; is the standard deviation of the state parameter items, including the standard deviation of temperature, pH value, stress, strain and conductivity; is the state parameter item obtained after standardization; t is the data collection time; For the standardized temperature X1(t), pH value X2(t), stress X3(t), strain X4(t) and conductivity X5(t), the formula Calculate the covariance matrix C, where m is the number of samples; and is the data matrix of state parameters and the transposed matrix of the data matrix; According to the size of the eigenvalue, the first k are selected as the principal component data, where k is the preset number of features; the selected principal component data is the state parameter item data containing the most information; all principal component data form the principal component data matrix .

5. The method for monitoring the repair process of absorbable biomaterials according to claim 1, characterized in that: The specific process of stress and strain monitoring is as follows: For p=1: physical support stage; p=2: fibrosis stage; p=3: local degradation stage; Calculating the stress-strain relationship, drawing a stress-strain distribution diagram of the absorbable biomaterial, simulating the material repair process through a finite element analysis model, and visually outputting the stress-strain distribution diagram; Draw the stress-strain diagram of the micro unit; When it is identified that the stress or strain relationship in the stress-strain relationship diagram is in the elastic strain region and the strain is greater than a preset threshold, it is determined that the absorbable biomaterial is about to crack or break, and a rupture warning signal is output; When it is recognized that the stress or strain appearing in the stress-strain relationship diagram has exited the elastic strain region and entered the plastic strain region, it is determined that the absorbable biomaterial has cracked or been damaged, the volume of the plastic region is calculated, the degree of damage is determined, and the location and size of the damaged region are displayed on a computer.

6. The method for monitoring the repair process of absorbable biomaterials according to claim 1, characterized in that: The specific process of temperature and pH monitoring is as follows: For p=4: organizational integration stage; p=5: complete absorption stage; p=6: long-term stability stage; Targeted analysis is performed on temperature and pH values. If the temperature or pH value exceeds the preset range, an absorption warning signal is output to indicate possible abnormal conditions and determine whether rapid degradation or local instability has occurred.

Images