A health management method and system for patients with incontinence dermatitis

Abstract

The present invention relates to the field of health management technology, and in particular to a health management method and system for incontinence dermatitis patients. The method comprises the following steps: obtaining the patient's historical diagnosis and treatment records and extracting skin status and excrement abnormal status data; evaluating the urine bacterial increment of the excrement abnormal status data, and based on this, simulating the progressive imbalance of the skin acid-base environment under repeated immersion conditions, and further quantifying bacterial corrosion skin damage; then, analyzing the attenuation trend of skin tissue metabolic activity, matching the antibacterial inhibition dosage requirements, and evaluating the drug penetration efficiency; finally, providing personalized drug dosage feedback suggestions for different patients based on the drug penetration efficiency coupling data, and sending them to the terminal to perform health management. The present invention makes health management technology more accurate by optimizing the health management technology.

Description

Technical Field

[0001] The present invention relates to the technical field of health management, and in particular to a health management method and system for patients with incontinence dermatitis. Background Art

[0002] Incontinence-associated dermatitis (IAD) is a special type of contact irritant dermatitis, which is seen in patients with incontinence. It refers to the inflammatory reaction and skin damage caused by long-term or repeated exposure of the skin to urine or feces due to incontinence. It is mainly manifested as erythema, rash, maceration, erosion, and even skin peeling or infection in severe cases. The antibacterial drug component, the main component of which is organosilicon quaternary ammonium salt, is a physical antibacterial dressing. When sprayed on the surface of the skin or mucous membrane, it can solidify to form an invisible positively charged film, which has a strong adsorption effect on negatively charged pathogenic microorganisms, causing the respiratory enzymes and metabolic enzymes on which the pathogens depend for survival to lose their function and die. The basic function of infrared therapy device irradiation is the thermal effect, which increases the temperature of subcutaneous tissue by irradiating the local area, dilates local blood vessels, promotes local blood circulation, accelerates the body's absorption of exudate, and improves the metabolic level. In addition, the thermal effect can also enhance the activity of drug molecules and cell metabolism, increase drug penetration and interaction between media, and achieve the purpose of improving drug efficacy. However, the traditional health management method for patients with incontinence-related dermatitis has the problems of low accuracy in analyzing skin lesions of incontinence-related dermatitis and large errors in the targeted health management of different patients. Summary of the invention

[0003] Based on this, it is necessary to provide a health management method and system for patients with incontinence dermatitis to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for health management of patients with incontinence dermatitis is provided, the method comprising the following steps:

[0005] Step S1: Obtain a historical diagnosis and treatment record set of patients with incontinence dermatitis; extract skin conditions between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain skin conditions between different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis based on the skin conditions between different incontinence dermatitis patients to obtain excrement abnormal state data between different incontinence dermatitis patients;

[0006] Step S2: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data; performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions based on the urine bacteria state increment data to obtain skin acid-base progressive numerical imbalance data; quantifying the structure of bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data;

[0007] Step S3: performing skin tissue metabolic activity attenuation trend analysis based on the bacterial corrosion skin damage data to obtain the skin tissue metabolic activity attenuation trend; performing antibacterial inhibition dosage demand matching according to the skin tissue metabolic activity attenuation trend to obtain antibacterial inhibition dosage demand matching data; performing drug penetration efficiency coupling on the antibacterial inhibition dosage demand matching data to obtain drug penetration efficiency coupling data;

[0008] Step S4: Formulate drug dosage feedback suggestions for different incontinence dermatitis patients based on the drug penetration efficiency coupling data, obtain drug dosage feedback suggestions, and send them to the terminal to implement health management of incontinence dermatitis patients.

[0009] Preferably, step S1 comprises the following steps:

[0010] Step S11: Obtaining a historical diagnosis and treatment record set of patients with incontinence dermatitis;

[0011] Step S12: performing data cleaning on the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain a historical diagnosis and treatment cleansing record set;

[0012] Step S13: extracting the skin conditions of different incontinence dermatitis patients from the historical diagnosis and treatment cleansing record set to obtain the skin conditions of different incontinence dermatitis patients;

[0013] Step S14: Based on the skin conditions of different incontinence dermatitis patients, the historical diagnosis and treatment record set of incontinence dermatitis patients is mapped with the excrement state properties of different incontinence dermatitis patients to obtain the excrement abnormal state data of different incontinence dermatitis patients.

[0014] Preferably, step S2 comprises the following steps:

[0015] Step S21: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data;

[0016] Step S22: performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the urine bacteria status increment data, to obtain the progressive numerical imbalance data of the acid-base environment of the skin;

[0017] Step S23: analyzing the excrement abnormal state data for repeated exposure conditions of fecal corrosive irritants to obtain repeated exposure conditions of corrosive irritants;

[0018] Step S24: quantifying the structure of bacterial corrosion skin damage based on the repeated exposure conditions of corrosive irritants and the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data.

[0019] Preferably, step S22 comprises the following steps:

[0020] Step S221: evaluating the cumulative amount of urea residue under repeated soaking conditions according to the urine bacteria status increment data to obtain an estimated cumulative amount of urea residue;

[0021] Step S222: performing a nonlinear numerical evaluation of ammonia decomposition based on the cumulative evaluation amount of urea residue to obtain nonlinear numerical data of ammonia decomposition;

[0022] Step S223: performing temperature-dependent catalytic extreme value interval analysis on the nonlinear numerical data of ammonia decomposition to obtain the catalytic extreme value interval of ammonia decomposition temperature;

[0023] Step S224: numerically simulate the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the catalytic extreme value range of the ammonia decomposition temperature to obtain the progressive numerical imbalance data of the acid-base environment of the skin.

[0024] Preferably, step S24 includes the following steps:

[0025] Step S241: extracting the repeated exposure time of the corrosive digestive enzyme under the repeated exposure condition of the corrosive stimulus to obtain the repeated exposure time of the digestive enzyme;

[0026] Step S242: analyzing the change in concentration of the corrosive irritant under repeated exposure conditions to obtain data on the change in concentration of the irritant;

[0027] Step S243: performing a cumulative quantitative analysis of skin surface protein degradation based on the digestive enzyme repeated exposure time and the irritant concentration change data to obtain cumulative quantitative data of protein degradation;

[0028] Step S244: performing stratum corneum cell loose structure analysis on the protein degradation cumulative quantitative data to obtain stratum corneum cell loose structure;

[0029] Step S245: performing a barrier function decline simulation assessment based on the loose structure of stratum corneum cells to obtain skin barrier function decline data;

[0030] Step S246: quantify the structure of skin damage caused by bacterial corrosion according to the loose structure of stratum corneum cells and the skin barrier function decline data to obtain skin damage data caused by bacterial corrosion.

[0031] Preferably, step S245 includes the following steps:

[0032] Based on the loose structure of stratum corneum cells, stratum corneum stress relaxation analysis was performed to obtain stratum corneum stress relaxation data;

[0033] Performing hydration impairment gradient analysis on stratum corneum stress relaxation data to obtain hydration impairment gradient data;

[0034] The external bacteria invasion index increment is calculated based on the hydration damage gradient data and the stratum corneum stress relaxation data to obtain the external bacteria invasion increment index;

[0035] The barrier function decline simulation evaluation was performed based on the incremental index of external bacterial invasion to obtain the skin barrier function decline data.

[0036] Preferably, step S3 comprises the following steps:

[0037] Step S31: analyzing the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity;

[0038] Step S32: performing nonlinear matching of infrared irradiation demand according to the attenuation trend of skin tissue metabolic activity to obtain nonlinear infrared irradiation demand;

[0039] Step S33: performing antibacterial inhibition dosage requirement matching on the bacterial corrosion skin damage data to obtain antibacterial inhibition dosage requirement matching data;

[0040] Step S34: performing drug penetration efficiency coupling on the antibacterial inhibition dosage requirement matching data according to the infrared irradiation nonlinear requirement to obtain drug penetration efficiency coupling data.

[0041] Preferably, step S33 includes the following steps:

[0042] Step S331: obtaining an antibacterial drug component, wherein the antibacterial drug component includes an organosilicon quaternary ammonium salt;

[0043] Step S332: estimating the bacterial order of magnitude per square centimeter for the bacterial corrosion skin damage data to obtain the bacterial order of magnitude;

[0044] Step S333: simulating the killing rate of bacteria by the positively charged membrane according to the bacterial magnitude of the organosilicon quaternary ammonium salt in the antibacterial drug component to obtain the killing rate of bacteria by the positively charged membrane;

[0045] Step S334: matching the antibacterial inhibition dosage requirement of the organosilicon quaternary ammonium salt in the antibacterial drug component based on the bacteria killing rate of the positively charged membrane to obtain antibacterial inhibition dosage requirement matching data.

[0046] Preferably, step S34 includes the following steps:

[0047] Step S341: performing skin tissue thermal energy absorption fluctuation analysis according to the nonlinear demand of infrared irradiation to obtain skin tissue thermal energy absorption fluctuation data;

[0048] Step S342: performing local vascular expansion analysis based on the skin tissue thermal energy absorption fluctuation data to obtain thermal energy-related local vascular expansion data;

[0049] Step S343: performing blood circulation velocity enhancement on the heat energy associated local vascular dilation data to obtain blood circulation velocity enhancement data;

[0050] Step S344: calculating the equal-increase equilibrium interval of intercellular hydration based on the skin tissue thermal energy absorption fluctuation data to obtain the equal-increase equilibrium interval of intercellular hydration;

[0051] Step S345: performing drug penetration efficiency coupling according to the blood circulation flow rate enhancement data and the equal-amount lift equilibrium interval to obtain drug penetration efficiency coupling data.

[0052] Preferably, the present invention also provides a health management system for patients with incontinence dermatitis, which is used to implement the health management method for patients with incontinence dermatitis as described above, and the health management system for patients with incontinence dermatitis includes:

[0053] The excrement state analysis module is used to obtain the historical diagnosis and treatment record set of incontinence dermatitis patients; extract the skin states of different incontinence dermatitis patients from the historical diagnosis and treatment record set of incontinence dermatitis patients to obtain the skin states of different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients on the historical diagnosis and treatment record set of incontinence dermatitis patients based on the skin states of different incontinence dermatitis patients to obtain the excrement abnormal state data between different incontinence dermatitis patients;

[0054] The bacterial corrosion skin damage analysis module is used to evaluate the urine bacteria increment of the excrement abnormal state data to obtain the urine bacteria state increment data; to perform numerical simulation of the progressive imbalance of the skin acid-base environment under repeated immersion conditions based on the urine bacteria state increment data to obtain the skin acid-base progressive numerical imbalance data; to quantify the structure of the bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain the bacterial corrosion skin damage data;

[0055] The drug penetration efficiency coupling module is used to analyze the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity; match the antibacterial inhibition dosage demand according to the attenuation trend of skin tissue metabolic activity to obtain the antibacterial inhibition dosage demand matching data; couple the antibacterial inhibition dosage demand matching data with the drug penetration efficiency to obtain the drug penetration efficiency coupling data;

[0056] The drug dosage recommendation generation module is used to generate drug dosage feedback recommendations among different incontinence dermatitis patients based on drug penetration efficiency coupling data, obtain drug dosage feedback recommendations, and send them to the terminal to perform health management of incontinence dermatitis patients.

[0057] The beneficial effect of the present invention is that by obtaining the historical diagnosis and treatment records of patients with incontinence dermatitis (including basic patient information, medical history, historical clinical symptoms, historical excrement status, etc.), the skin status information between different patients is systematically extracted. This process involves a detailed analysis of the skin characteristics of each patient, identifying the key factors affecting the skin condition, and summarizing them into a data set. Then, based on the extracted skin state, the property association mapping of the excrement state is performed to reveal the correlation between the abnormal excrement state and the skin condition. This process can not only provide basic data for subsequent research, but also identify the changing laws of skin state under specific conditions, thereby laying the foundation for understanding the pathological characteristics of different patients. The data on abnormal excrement status are deeply analyzed to evaluate the increase in bacteria in urine. This evaluation can not only reveal the changing trend of the urine bacterial state, but also provide an important basis for subsequent research. Based on these data, a series of simulation experiments were systematically conducted to evaluate the impact of repeated immersion on the acid-base environment of the skin, and gradually reveal the numerical changes in the acid-base imbalance of the skin. This numerical simulation provides a scientific basis for analyzing the corrosion of bacteria on the skin, helps quantify the degree of damage caused by bacteria to the skin, and thus provides support for the formulation of corresponding countermeasures. The analysis of the impact of bacterial corrosion on the metabolic activity of skin tissue aims to reveal how the metabolic capacity of skin tissue gradually decays after being invaded by bacteria. Through in-depth research on this decay trend, it is possible to identify the changes in the metabolic activity of skin tissue under different conditions, thereby providing data support for subsequent antibacterial demand matching. According to the metabolic activity decay trend, the system can accurately calculate the required antibacterial inhibition dosage, and further optimize the drug use effect by coupling the drug penetration efficiency. This process not only improves the pertinence of the antibacterial strategy, but also enhances the self-repair ability of skin tissue. Based on the drug penetration efficiency coupling data obtained in the previous steps, the system can formulate personalized drug dosage feedback suggestions for different incontinence dermatitis patients. This feedback suggestion not only takes into account the specific situation of the patient, but also combines the previous research data to ensure the scientificity and effectiveness of the suggestion. These feedback suggestions will be sent to the terminal for reference by relevant health managers, aiming to optimize the health management plan for incontinence dermatitis patients. In this way, it can be ensured that each patient can obtain corresponding support according to their specific needs, thereby improving the overall health management level. Therefore, the present invention is an optimization of a traditional health management method for patients with incontinence dermatitis, which solves the problem that the traditional health management method for patients with incontinence dermatitis has low accuracy in analyzing skin lesions of incontinence-related dermatitis and large errors in the targeted health management of different patients, improves the accuracy of the analysis of skin lesions of incontinence-related dermatitis, and improves the targeted health management of different patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1A schematic diagram of the steps of a health management method for patients with incontinence dermatitis;

[0059] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0060] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. DETAILED DESCRIPTION

[0061] See also Figures 1 to 3 , a health management method for patients with incontinence dermatitis, the method comprising the following steps:

[0062] Step S1: Obtain a historical diagnosis and treatment record set of patients with incontinence dermatitis; extract skin conditions between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain skin conditions between different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis based on the skin conditions between different incontinence dermatitis patients to obtain excrement abnormal state data between different incontinence dermatitis patients;

[0063] Step S2: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data; performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions based on the urine bacteria state increment data to obtain skin acid-base progressive numerical imbalance data; quantifying the structure of bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data;

[0064] Step S3: performing skin tissue metabolic activity attenuation trend analysis based on the bacterial corrosion skin damage data to obtain the skin tissue metabolic activity attenuation trend; performing antibacterial inhibition dosage demand matching according to the skin tissue metabolic activity attenuation trend to obtain antibacterial inhibition dosage demand matching data; performing drug penetration efficiency coupling on the antibacterial inhibition dosage demand matching data to obtain drug penetration efficiency coupling data;

[0065] Step S4: Formulate drug dosage feedback suggestions for different incontinence dermatitis patients based on the drug penetration efficiency coupling data, obtain drug dosage feedback suggestions, and send them to the terminal to implement health management of incontinence dermatitis patients.

[0066] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a health management method for incontinence dermatitis patients of the present invention. In this example, the health management method for incontinence dermatitis patients includes the following steps:

[0067] Step S1: Obtain a historical diagnosis and treatment record set of patients with incontinence dermatitis; extract skin conditions between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain skin conditions between different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis based on the skin conditions between different incontinence dermatitis patients to obtain excrement abnormal state data between different incontinence dermatitis patients;

[0068] In an embodiment of the present invention, first, the system obtains a historical diagnosis and treatment record set of incontinence dermatitis patients from a hospital information system (HIS) through a patient management platform, including but not limited to the patient's medical record data, skin condition, diagnosis information, treatment plan and its effect, etc. These historical data are stored in the form of structured and unstructured data. Next, a rule-based natural language processing (NLP) algorithm is used to clean and extract the text data in the historical diagnosis and treatment records, and the relevant information is integrated into a standardized format. Specifically, the skin condition is quantitatively extracted by a standardized scoring scale (such as Braden Scale), and the degree of skin damage and related indicators of each patient are recorded. At this time, the excrement status information in the data (such as urine and feces characteristics, where the excrement status information is obtained by collecting and testing them) is also associated with the patient's skin condition through coding rules, and a decision tree-based classification algorithm is used to perform excrement status property association mapping between different incontinence dermatitis patients. The output of this step is an excrement abnormal state data set, which contains all variables related to each patient's excrement status, such as the acidity and alkalinity of the excrement, viscosity and its effect on the skin.

[0069] Step S2: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data; performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions based on the urine bacteria state increment data to obtain skin acid-base progressive numerical imbalance data; quantifying the structure of bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data;

[0070] In the embodiment of the present invention, urine bacteria increment evaluation is performed on the excrement abnormal state data. The specific implementation method is based on the growth rate of the bacterial concentration in the known urine sample, using the exponential growth model in biostatistics (such as: ),in Expressed as time After the bacterial count, is the initial bacterial number, is the growth rate, After obtaining the bacterial increment data, the effect of excrement on the acid-base balance of the skin is modeled by numerical simulation software (such as MATLAB), and the change of the acid-base environment of the skin is simulated gradually by stepwise numerical integration method (such as Euler method) to obtain the gradual numerical imbalance data of the skin acid-base. The change of pH value on the skin surface conforms to the following equation: ,in Indicates the pH of the skin. and are the constants of the effects of environmental factors and urine pH on skin pH. Represents the urine bacteria concentration at time t. On this basis, the microscopic damage theory is further used to quantitatively analyze the damage structure of bacterial skin corrosion. The skin tissue model is used to calculate the changes in skin structure after bacterial erosion, and finally the bacterial corrosion skin damage data is obtained, including the damage depth of the skin layer and the bacterial erosion area.

[0071] Step S3: performing skin tissue metabolic activity attenuation trend analysis based on the bacterial corrosion skin damage data to obtain the skin tissue metabolic activity attenuation trend; performing antibacterial inhibition dosage demand matching according to the skin tissue metabolic activity attenuation trend to obtain antibacterial inhibition dosage demand matching data; performing drug penetration efficiency coupling on the antibacterial inhibition dosage demand matching data to obtain drug penetration efficiency coupling data;

[0072] In an embodiment of the present invention, first, the skin tissue metabolic activity attenuation trend analysis is performed using bacterial corrosion skin damage data. An attenuation model is established based on the relationship between skin metabolic activity and the amount of damage. Specifically, a linear regression model y=αx+βy is used, where y is skin metabolic activity, x is the degree of damage, and α and β are fitting parameters to obtain the trend of skin tissue metabolic activity attenuation. Based on this attenuation trend, the inhibitory dosage of antimicrobial drugs is further matched with demand. First, according to the different degrees of bacterial invasion, the minimum inhibitory drug dose required in each case is calculated according to the standard drug use guidelines, and compared with historical drug use data to establish an antimicrobial drug inhibition dosage demand model. Using multiple regression analysis, based on the degree of skin damage and metabolic attenuation trend, and finally, combined with the drug's penetration characteristics and drug efficacy, the appropriate antimicrobial inhibition dosage demand matching data is calculated.

[0073] Step S4: Formulate drug dosage feedback suggestions for different incontinence dermatitis patients based on the drug penetration efficiency coupling data, obtain drug dosage feedback suggestions, and send them to the terminal to implement health management of incontinence dermatitis patients.

[0074] In the embodiment of the present invention, firstly, drug dosage feedback suggestions are formulated for different incontinence dermatitis patients based on drug penetration efficiency coupling data. Based on the antimicrobial inhibition dosage requirement data obtained in the previous step, combined with the specific condition of the patient's skin (such as metabolic attenuation, degree of skin damage, etc.), the algorithm model outputs the drug dosage suitable for the patient. The data includes the frequency of drug use, dosage and expected effect, etc. The drug dosage feedback suggestions are transmitted to the terminal through the health management platform,

[0075] Step S1 includes the following steps:

[0076] Step S11: Obtaining a historical diagnosis and treatment record set of patients with incontinence dermatitis;

[0077] Step S12: performing data cleaning on the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain a historical diagnosis and treatment cleansing record set;

[0078] Step S13: extracting the skin conditions of different incontinence dermatitis patients from the historical diagnosis and treatment cleansing record set to obtain the skin conditions of different incontinence dermatitis patients;

[0079] Step S14: Based on the skin conditions of different incontinence dermatitis patients, the historical diagnosis and treatment record set of incontinence dermatitis patients is mapped with the excrement state properties of different incontinence dermatitis patients to obtain the excrement abnormal state data of different incontinence dermatitis patients.

[0080] In an embodiment of the present invention, first, the historical diagnosis and treatment records of patients with incontinence dermatitis are retrieved through the hospital information system (HIS) and the electronic health record (EHR) platform. The historical records include the patient's basic information (such as age, gender, hospitalization records, etc.), medical history, skin condition, treatment process, laboratory test results and drug use. Here, the patient's basic information will be desensitized to protect the patient's privacy. This process is carried out through an SQL database query. Through this query, all eligible patient diagnosis and treatment records are obtained, and data such as the patient's skin condition, excrement conditions, and treatment records are extracted to provide the original data set for subsequent processing. The historical diagnosis and treatment record set is cleaned to remove invalid information and unify the format. In the specific implementation process, duplicate patient records are first identified and deleted, and the data is deduplicated using a unique patient identifier (such as a patient ID). Next, check whether there is missing data in the record, such as important fields such as the patient's age, gender, or treatment date, and use mean filling or interpolation methods to process missing values. In addition, special characters, typos, and spelling errors in text fields are cleaned and converted into standardized fields. For example, "diaper eczema" is unified into the standard term "incontinence dermatitis". For date fields, ensure that all date formats are unified and use the standard ISO 8601 format for conversion. After data cleaning, a historical diagnosis and treatment cleansing record set is obtained, which contains structured patient information. The cleaned data is guaranteed to be correct and convenient for subsequent analysis. The skin status of different incontinence dermatitis patients is extracted based on the historical diagnosis and treatment cleansing record set. First, through medical literature and clinical experience, a skin status scoring standard is developed, such as using the Braden scoring scale to quantify the integrity of the skin, or using the EPUAP skin injury assessment form to assess the degree of skin damage. For each patient data in the record set, the relevant information describing the skin status, such as redness, swelling, ulcers, exudation, etc., is extracted from the text field through rule matching technology (such as regular expressions). If there is image data, image recognition technology is used to analyze the degree of skin damage. Specifically, if the record contains the medical record description "local redness and swelling", it is converted into a skin status score according to the scoring rule (for example, redness and swelling is 2 points). This process uses natural language processing (NLP) algorithms and rule engines to extract skin status data, and ultimately generates a structured data set containing the skin status of each patient for subsequent analysis. First, based on the skin status data extracted in step S13, combined with the excreta status information (such as urine and feces characteristics) in the historical diagnosis and treatment record set, the excreta status between different incontinence dermatitis patients is mapped for property association. In order to quantify the state of excreta, a classification model for excreta status is first established, and the excreta is divided into different types based on the color, acidity, alkalinity, viscosity and other characteristics of the excreta. A data association rule mining algorithm (such as the Apriori algorithm) is used to analyze the relationship between excreta status and skin status.Assuming that there is a certain pattern between the pH of excrement and the degree of skin damage, the Apriori algorithm can discover the pattern by calculating the correlation (support, confidence). For example, if the pH value of urine is 3 and the patient has severe ulcers on the skin, the correlation is high, indicating that this type of excrement is more damaging to the skin. On this basis, association rules are used to establish a relationship matrix between excrement and skin damage, and the data on abnormal excrement status among different incontinence dermatitis patients are obtained. Finally, a data set containing the association between abnormal excrement status and skin damage of different patients is formed, providing a basis for subsequent health management.

[0081] Step S2 includes the following steps:

[0082] Step S21: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data;

[0083] Step S22: performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the urine bacteria status increment data, to obtain the progressive numerical imbalance data of the acid-base environment of the skin;

[0084] Step S23: analyzing the excrement abnormal state data for repeated exposure conditions of fecal corrosive irritants to obtain repeated exposure conditions of corrosive irritants;

[0085] Step S24: quantifying the structure of bacterial corrosion skin damage based on the repeated exposure conditions of corrosive irritants and the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data.

[0086] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0087] Step S21: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data;

[0088] In an embodiment of the present invention, urine test data in historical medical records are first extracted, and in particular, data on urine culture results and urine bacterial counts are screened. Microbiological analysis is used to evaluate the urine bacterial increment of each patient based on characteristics such as urine bacterial count, type, and growth rate. The DNA increment of bacteria in urine is detected by quantitative PCR (polymerase chain reaction) technology to analyze changes in the number of urine bacteria. Specifically, DNA is first extracted from a urine sample, and then the amplification amount of a specific gene fragment of the bacteria is determined by real-time fluorescence quantitative PCR to obtain the number and increment of bacteria. A threshold is set using the formula: ,in is the number of bacteria, is the initial bacterial number, The increment of the change in the PCR cycle threshold, b is the reaction efficiency coefficient, and the incremental data of urine bacteria are calculated to generate the incremental data of urine bacteria status for subsequent analysis. The incremental data of urine bacteria status will be used as input for subsequent steps to analyze its impact on the acid-base environment of the skin and skin damage.

[0089] Step S22: performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the urine bacteria status increment data, to obtain the progressive numerical imbalance data of the acid-base environment of the skin;

[0090] In an embodiment of the present invention, the finite element method (FEM) is first used to numerically simulate the acid-base environment of the skin under repeated wetting. By establishing a physical model of the skin, the effect of the increase in urine bacteria on the pH value of the skin surface is considered. The simulation assumes that the skin surface is affected by repeated wetting of urine and the increase in bacteria, and the acid-base environment on the skin surface gradually becomes unbalanced, generating a progressive acid-base change process. In the specific simulation, the physicochemical properties of the skin are used, and the pH value of urine and the acidic substances of bacterial metabolites are used as model inputs to calculate the change in the pH value of the skin surface at each contact. The change in the acid-base environment of the skin is simulated by the following formula: ,in is the rate of change of pH value, is the current pH value of the skin surface, is the initial pH value, is the pH change rate constant, is the influence coefficient of bacterial increment on pH, is the number of bacteria in urine. By simulating different time points, the gradual imbalance trend of pH value on the skin surface is obtained. The data of gradual imbalance of skin acid-base value provides an important basis for subsequent skin damage analysis.

[0091] Step S23: analyzing the excrement abnormal state data for repeated exposure conditions of fecal corrosive irritants to obtain repeated exposure conditions of corrosive irritants;

[0092] In an embodiment of the present invention, fecal component information involved in the abnormal state data of excrement is collected, such as the concentration of corrosive irritants such as enzymes, acidic substances, and ammonia compounds in the feces. Chemical analysis methods, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS), are used to detect the content of corrosive substances in feces. Next, based on the changing trend of fecal components, a corrosive irritant exposure model is constructed to simulate the degree of skin irritation of patients with incontinence dermatitis under different excrement contact conditions. Through repeated exposure experiments, the effects of corrosive substances in feces after repeated contact with the skin surface are simulated, the relationship between the exposure time and the irritant concentration is calculated, and the exposure conditions of corrosive irritants are obtained. Specifically, considering the impact of repeated exposure to feces on the skin, the following model is used to describe the relationship between the concentration of corrosive substances and the exposure time: ,in is the concentration of corrosive substances exposed on the skin surface at time t, is the initial concentration, is the attenuation constant of the corrosive substance. This formula obtains the skin irritation under different exposure times and corrosive substance concentrations through repeated exposure simulation, thereby obtaining the repeated exposure conditions of corrosive irritants.

[0093] Step S24: quantifying the structure of bacterial corrosion skin damage based on the repeated exposure conditions of corrosive irritants and the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data.

[0094] In an embodiment of the present invention, the data obtained in step S22 and step S23 are first combined to construct a comprehensive analysis model of bacterial corrosion skin damage. The skin damage is quantified using finite element analysis. The model combines the effects of skin acid-base imbalance, exposure to corrosive irritants, and bacterial increment to calculate the skin damage structure under different conditions. In the model, the degree of skin damage shows a nonlinear relationship with changes in the acid-base environment, the concentration of corrosive substances and their exposure time, and the increment of the number of bacteria. Through this model, bacterial corrosion skin damage data is obtained, the degree of skin damage under the action of multiple factors is quantified, and data support is provided for subsequent health management.

[0095] Step S22 includes the following steps:

[0096] Step S221: evaluating the cumulative amount of urea residue under repeated soaking conditions according to the urine bacteria status increment data to obtain an estimated cumulative amount of urea residue;

[0097] Step S222: performing a nonlinear numerical evaluation of ammonia decomposition based on the cumulative evaluation amount of urea residue to obtain nonlinear numerical data of ammonia decomposition;

[0098] Step S223: performing temperature-dependent catalytic extreme value interval analysis on the nonlinear numerical data of ammonia decomposition to obtain the catalytic extreme value interval of ammonia decomposition temperature;

[0099] Step S224: numerically simulate the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the catalytic extreme value range of the ammonia decomposition temperature to obtain the progressive numerical imbalance data of the acid-base environment of the skin.

[0100] In the embodiment of the present invention, the urea content in urine is first extracted based on the incremental data of urine bacterial status, and the residual amount of urea is estimated based on the time period of repeated soaking. By simulating the repeated contact between the skin surface of patients with incontinence dermatitis and urine, the urea in the urine gradually accumulates on the skin surface to form a urea residual layer. In this process, the mass conservation principle is used to calculate the accumulated amount of urea after each soaking. Assuming that the urea concentration in urine at each contact is The time of each skin contact is , the flow rate of urine is Q, and the residual accumulation of urea can be estimated by the following formula: in, is the residual amount of urea, is the concentration of urea in urine, is the flow rate of urine, is the contact time. In the evaluation of each cycle, the urea residue and the number of immersions are accumulated to evaluate the cumulative impact of urea under long-term repeated immersion, thereby obtaining the cumulative evaluation of urea residue. This evaluation provides key data affecting the acid-base balance of the skin for subsequent steps. Based on the cumulative evaluation of urea residue obtained in step S221, a nonlinear numerical evaluation of ammonia decomposition is further performed. The urea in urine decomposes into ammonia and carbon dioxide under the action of the skin surface and bacteria. By establishing a chemical reaction kinetic model of urea decomposition, the conventional mass balance method and reaction rate formula are used to simulate the generation of ammonia during urea decomposition. In order to accurately evaluate the decomposition process of ammonia, a nonlinear differential equation model is used for simulation. The reaction rate is expressed by the following formula: ,in is the rate of urea decomposition reaction, is the constant of the decomposition reaction, is the urea concentration in urine, is the urease concentration. Through nonlinear numerical simulation, iterative calculation is performed using different urea concentrations and reaction conditions to obtain nonlinear numerical data of ammonia decomposition. These data provide a quantitative basis for evaluating the accumulation of ammonia and its impact on the skin environment. Based on the nonlinear numerical data of ammonia decomposition obtained in step S222, the temperature-dependent catalytic extreme value interval is further analyzed. The generation reaction of ammonia is not only related to the urea concentration, but also closely related to the temperature. To this end, the Arrhenius equation is used to describe the effect of temperature on the reaction rate of ammonia decomposition. In actual operation, the temperature dependence of the urea decomposition rate at different temperatures is obtained by fitting the experimental data, thereby determining the extreme value interval of the catalytic reaction. By changing the temperature range, the test is carried out from the lowest temperature to the highest temperature to obtain the interval in which the rate of ammonia generation changes with temperature. Combined with the ammonia decomposition data obtained in step S222, the efficiency and reaction extreme value of ammonia decomposition at different temperatures are analyzed to obtain the temperature catalytic extreme value interval of ammonia decomposition. This analysis result helps to determine the limit conditions of ammonia generation and accumulation in the urea decomposition process at different ambient temperatures, and provides data support for subsequent skin acid-base environment simulation. The data obtained in step S222 and step S223 are used to perform numerical simulation of the gradual imbalance of the skin acid-base environment in combination with the influence of urine under repeated immersion. The accumulation of ammonia in urine on the skin surface will cause the pH value of the skin to gradually rise, thereby causing acid-base imbalance. In this simulation, by considering the dependence of the decomposition rate of ammonia on temperature, combined with the decomposition data of the amount of ammonia in urine, the changes in the pH value of the skin surface under different time and temperature conditions are simulated. The skin acid-base environment model is established using the finite element analysis (FEA) method to simulate the pH changes of the skin during ammonia accumulation. Specifically, the initial pH value of the skin is set to 6.5, and the change in ammonia concentration in urine is considered. According to the decomposition rate of ammonia and the influence of temperature changes on the skin surface, the numerical simulation is used to obtain the gradual change process of the pH value on the skin surface over time, thereby obtaining the skin acid-base gradual numerical imbalance data. This data provides an important basis for further research on skin damage and protective measures.

[0101] Step S24 includes the following steps:

[0102] Step S241: extracting the repeated exposure time of the corrosive digestive enzyme under the repeated exposure condition of the corrosive stimulus to obtain the repeated exposure time of the digestive enzyme;

[0103] Step S242: analyzing the change in concentration of the corrosive irritant under repeated exposure conditions to obtain data on the change in concentration of the irritant;

[0104] Step S243: performing a cumulative quantitative analysis of skin surface protein degradation based on the digestive enzyme repeated exposure time and the irritant concentration change data to obtain cumulative quantitative data of protein degradation;

[0105] Step S244: performing stratum corneum cell loose structure analysis on the protein degradation cumulative quantitative data to obtain stratum corneum cell loose structure;

[0106] Step S245: performing a barrier function decline simulation assessment based on the loose structure of stratum corneum cells to obtain skin barrier function decline data;

[0107] Step S246: quantify the structure of skin damage caused by bacterial corrosion according to the loose structure of stratum corneum cells and the skin barrier function decline data to obtain skin damage data caused by bacterial corrosion.

[0108] In the embodiment of the present invention, the repeated exposure time of the digestive enzyme is extracted based on the repeatedly exposed corrosive irritant. When the corrosive irritant repeatedly contacts the skin surface of the incontinence dermatitis patient, the digestive enzymes (such as urease, trypsin, etc.) in the skin are activated, and these enzymes have a degrading effect on the protein of the skin surface. By monitoring the activity changes of the digestive enzymes, the repeated exposure time of the digestive enzymes can be extracted. In the implementation process, the repeated contact cycle of the corrosive irritant and the duration of each contact are first determined, and different irritant concentrations and exposure times are set experimentally to simulate the skin reaction under different exposure conditions. During each exposure process, the activity changes of the digestive enzymes are monitored in real time using enzyme activity determination methods (such as colorimetry, fluorescence, etc.). Assuming that the reaction rate of the digestive enzyme changes nonlinearly with the exposure time, the total time of the repeated exposure of the digestive enzyme is obtained by using a data fitting model. Through this process, the repeated exposure time of the digestive enzyme is obtained, which provides a quantitative basis for the subsequent analysis of skin structure changes. For the repeated exposure conditions of the corrosive irritant, the concentration changes of the irritant are analyzed. The damaging effect of corrosive irritants on the skin is directly related to their concentration on the skin surface. Therefore, it is very important to accurately monitor the changes in irritant concentration. In the implementation process, the concentration of corrosive irritants on the skin surface and local skin tissue is first sampled and measured by chromatography (such as high performance liquid chromatography HPLC) or mass spectrometry (MS). The experiment sets repeated exposure conditions to simulate the contact between irritants of different concentrations and the skin, and records the changes in irritant concentration during each exposure. The concentration changes of the irritants are correlated with factors such as exposure time and skin reaction degree to obtain data on the changes in irritant concentration over time. These data provide a concentration basis for the subsequent skin damage model, thereby helping to simulate the effects of different irritant concentrations on the skin. Based on the data on the changes in digestive enzyme exposure time and irritant concentration obtained in steps S241 and S242, a cumulative quantitative analysis of skin surface protein degradation is performed. Proteins on the skin surface (such as keratin, collagen, etc.) will degrade under the action of repeated exposure to corrosive irritants and digestive enzymes. By combining repeated exposure time and irritant concentration, the degradation amount of skin surface protein can be quantitatively evaluated. During the experiment, the protein concentration of the skin surface is first measured using a protein quantification kit (such as the BCA method or the Lowry method), and the change in protein before and after exposure is recorded. Then, based on the activity of the digestive enzyme and the concentration of the irritant, the known protein degradation rate equation is used for calculation. Assuming that the protein degradation rate of the skin surface has a linear or nonlinear relationship with time and concentration, the quantitative analysis of protein degradation can be performed using the following equation: ,in is the rate of change of protein concentration, is the digestive enzyme concentration, is the concentration of the irritant, and k is the rate constant of protein degradation. According to the formula, the cumulative data of protein degradation in the skin surface layer are obtained, which provide important information for further analysis of skin damage. Based on the cumulative quantitative data of protein degradation obtained in step S243, a three-dimensional structural model of stratum corneum cells is constructed using a numerical calculation method. The model assumes that stratum corneum cells are composed of multiple units, each of which represents a cell and its surrounding matrix environment. The mechanical strength of each cell is positively correlated with the degree of protein degradation inside it. The model simulates the changes in the mechanical properties of cells through protein degradation data. The finite element method (FEM) is used to numerically simulate the loose structure of stratum corneum cells. During the simulation process, the physical parameters of cells and intercellular matrix are first defined, including mechanical properties such as elastic modulus and Poisson's ratio. These parameters are adjusted according to the degree of protein degradation. For example, it is assumed that after the cumulative amount of protein degradation exceeds a certain threshold, the elastic modulus of the cell will be significantly reduced, resulting in loose connections between cells. To this end, in the simulation, the elastic modulus (Ecell) and crack propagation coefficient (Kcrack) of each cell can be set, and the mechanical interaction between cells can be calculated by the following formula: ,in is stress, is the force, is the contact area, is the elastic modulus of the cell, is the change in cell spacing, is the initial spacing. In addition, the degradation data (such as the cumulative amount of protein degradation obtained in step S243) is used to simulate the time relationship between protein degradation and the cell loosening process through a first-order differential equation. Assuming that the degradation rate is inversely proportional to the intercellular contact force, the loose behavior of the stratum corneum cells under different conditions is simulated to obtain the strength and looseness of each intercellular connection. The simulation results will show the looseness of the cell structure and the increase in the cell gap under different levels of protein degradation accumulation. Based on the loose structure of the stratum corneum cells obtained in step S244, a simulation evaluation of the decline in skin barrier function is performed. The main function of the skin barrier is to prevent water loss and resist the invasion of harmful substances from the outside. The loose structure of the stratum corneum cells will lead to the loss of barrier function. In order to evaluate the decline in barrier function, a transmission model (such as Fick's law) is used to simulate the change in skin permeability based on the degree of looseness of the stratum corneum cells, and further simulate the decline process of the skin barrier function. In this way, quantitative data on the decline in skin barrier function are obtained, which provides a numerical basis for evaluating the health status of the skin. Combined with the loose structure of stratum corneum cells and the skin barrier function decline data obtained in steps S244 and S245, a quantitative analysis of the structure of bacterial corrosion skin damage is performed. Bacteria colonize on the skin of patients with incontinence dermatitis and enter the body through the damaged skin barrier, leading to further infection and damage. To this end, a model of bacterial invasion of the skin is established, considering the invasion rate of bacteria at different degrees of skin damage. The relationship between the number of bacteria on the skin surface and the area of ​​skin damage is used to quantify the degree of skin damage. The damaged structure of bacterial corrosion of the skin is quantified and damage data is generated. This data provides a scientific basis for further skin protection and repair measures.

[0109] Step S245 includes the following steps:

[0110] Based on the loose structure of stratum corneum cells, stratum corneum stress relaxation analysis was performed to obtain stratum corneum stress relaxation data;

[0111] Performing hydration impairment gradient analysis on stratum corneum stress relaxation data to obtain hydration impairment gradient data;

[0112] The external bacteria invasion index increment is calculated based on the hydration damage gradient data and the stratum corneum stress relaxation data to obtain the external bacteria invasion increment index;

[0113] The barrier function decline simulation evaluation was performed based on the incremental index of external bacterial invasion to obtain the skin barrier function decline data.

[0114] In an embodiment of the present invention, the stress relaxation analysis of the stratum corneum is performed by the loose structure data of the stratum corneum cells obtained in the previous step. The loose structure of the stratum corneum leads to a decrease in the tight connection between cells, which in turn affects the overall stress distribution of the skin. The stress distribution of cells and intercellular matrix is ​​simulated using the finite element method (FEM), and the gradual release of stress in the loose structure is calculated. Specifically, the rigidity of the stratum corneum is adjusted by defining the constitutive relationship of the stratum corneum material and combining the protein degradation accumulation data. Assuming that the connection strength between cells in the loose structure decreases and the stress concentration area shifts, the reduction and distribution change of stress during the relaxation process are gradually simulated. After the stress relaxation analysis, the hydration impaired gradient analysis is performed based on the simulated stress distribution. The stratum corneum is the main component of the skin barrier, and its water content directly affects the integrity of the barrier. By calculating the hydration level at different depths of the stratum corneum, the gradient data of impaired hydration can be obtained. Using the hydration model, the hydration level is regarded as a gradual process. The loss of hydration gradually intensifies with the relaxation of stratum corneum stress. By combining stress relaxation data and physical parameters of the stratum corneum (such as moisture permeability, hygroscopicity, etc.), the spatial distribution change of hydration is calculated to obtain the hydration damage gradient. This gradient data can be used to quantify the degree of skin hydration loss and reflect the process of skin barrier damage. Then, based on the hydration damage gradient data and stratum corneum stress relaxation data, the incremental calculation of the external bacterial invasion index is performed. External bacterial invasion is an important aspect of barrier function decline. With the change of stratum corneum hydration and stress, the barrier function of the skin gradually decreases, providing a channel for bacterial invasion. The probability of bacterial invasion is closely related to the degree of hydration damage and the stress relaxation state of the stratum corneum. By establishing a mathematical model and combining experimental data, the incremental bacterial invasion index under different hydration damage gradients and stress relaxation data is calculated. The model assumes that the rate of bacterial invasion on the damaged skin barrier is positively correlated with the hydration of the skin and the degree of stress relaxation of the stratum corneum, and then the incremental bacterial invasion is calculated through the exponential relationship. Finally, based on the incremental bacterial invasion index, a simulation evaluation of skin barrier function decline is performed. In this step, the relationship between external bacterial invasion and barrier decline is modeled through mathematical simulation to obtain the predicted data of skin barrier function decline. According to the bacterial invasion index calculated previously, the time series analysis method is used to further simulate the functional decline of the skin barrier, combined with factors such as bacterial growth rate and changes in the skin surface environment. The simulation evaluation results show that the decline rate of the skin barrier will be different under different conditions of impaired hydration and stress relaxation. This simulation evaluation provides a theoretical basis for further skin protection measures, especially in the health management of patients with incontinence dermatitis.

[0115] Step S3 includes the following steps:

[0116] Step S31: analyzing the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity;

[0117] Step S32: performing nonlinear matching of infrared irradiation demand according to the attenuation trend of skin tissue metabolic activity to obtain nonlinear infrared irradiation demand;

[0118] Step S33: performing antibacterial inhibition dosage requirement matching on the bacterial corrosion skin damage data to obtain antibacterial inhibition dosage requirement matching data;

[0119] Step S34: performing drug penetration efficiency coupling on the antibacterial inhibition dosage requirement matching data according to the infrared irradiation nonlinear requirement to obtain drug penetration efficiency coupling data.

[0120] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0121] Step S31: analyzing the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity;

[0122] In the embodiment of the present invention, after obtaining the bacterial corrosion skin damage data, the data spectrum is first decomposed by Fourier transform to extract the characteristic signal of the metabolic activity attenuation of the skin tissue. The exponential decay model is used to perform nonlinear fitting on the metabolic activity signal to extract the metabolic activity decline rate and the decay constant. The specific operations include: normalizing the original damage data to eliminate the dimension effect; decomposing the signal by discrete wavelet transform to extract the key frequency band of metabolic activity; constructing a multidimensional feature vector to describe the microscopic change characteristics of the metabolic activity attenuation of the skin tissue. The complexity of the metabolic activity attenuation is evaluated by the entropy analysis method, and a quantitative evaluation system for the metabolic activity attenuation trend is established.

[0123] Step S32: performing nonlinear matching of infrared irradiation demand according to the attenuation trend of skin tissue metabolic activity to obtain nonlinear infrared irradiation demand;

[0124] In an embodiment of the present invention, a nonlinear mapping model is constructed according to the attenuation trend of the metabolic activity of skin tissue to accurately match the infrared irradiation demand. The specific implementation method is: mapping the metabolic activity attenuation trend data to a standardized coordinate system, and establishing a nonlinear correlation function between metabolic activity and infrared irradiation intensity. The gradient descent algorithm is used to iteratively optimize the infrared irradiation demand, and the optimal irradiation parameters are determined by the least squares method. The entropy weight method is introduced to assign weights to the different dimensional features of metabolic activity attenuation, and a multi-dimensional coupled nonlinear matching model for infrared irradiation demand is established. The infrared irradiation intensity and time parameters required for each square centimeter of skin tissue are accurately calculated.

[0125] Step S33: performing antibacterial inhibition dosage requirement matching on the bacterial corrosion skin damage data to obtain antibacterial inhibition dosage requirement matching data;

[0126] In an embodiment of the present invention, deep feature extraction is performed on bacterial corrosion skin damage data to construct an antibacterial inhibition dosage demand matching algorithm. The specific operations include: firstly, the spectrum characteristics of bacterial corrosion damage data are extracted by Fourier transform; the principal component analysis method is used to reduce the dimension and extract the core feature vector of the damage data. A bacterial distribution density assessment model based on Mahalanobis distance is established to accurately calculate the number of bacteria per unit area and the degree of corrosion. A cluster analysis method is introduced to standardize and grade bacterial corrosion damage data of different severity. Through the interval mapping algorithm, the bacterial corrosion damage characteristics are directly converted into quantitative indicators of the dosage demand of antibacterial drugs.

[0127] Step S34: performing drug penetration efficiency coupling on the antibacterial inhibition dosage requirement matching data according to the infrared irradiation nonlinear requirement to obtain drug penetration efficiency coupling data.

[0128] In an embodiment of the present invention, a precise mapping model of drug penetration efficiency coupling is constructed based on the nonlinear demand for infrared irradiation. The specific implementation method is: first, a nonlinear transfer function of infrared irradiation intensity and thermal energy absorption of skin tissue is established. A local vasodilation dynamic response model is constructed through partial differential equations to accurately calculate changes in blood circulation velocity. Thermodynamic mass transfer theory is introduced to analyze the equilibrium mechanism of intercellular hydration. A numerical integration method is used to establish a strict quantitative mapping relationship between infrared irradiation parameters and drug penetration efficiency. A drug penetration efficiency coupling model based on the principles of conservation of energy and mass conservation is constructed to achieve precise regulation of the penetration depth and rate of antibacterial drugs.

[0129] Step S33 includes the following steps:

[0130] Step S331: obtaining an antibacterial drug component, wherein the antibacterial drug component includes an organosilicon quaternary ammonium salt;

[0131] Step S332: estimating the bacterial order of magnitude per square centimeter for the bacterial corrosion skin damage data to obtain the bacterial order of magnitude;

[0132] Step S333: simulating the killing rate of bacteria by the positively charged membrane according to the bacterial magnitude of the organosilicon quaternary ammonium salt in the antibacterial drug component to obtain the killing rate of bacteria by the positively charged membrane;

[0133] Step S334: matching the antibacterial inhibition dosage requirement of the organosilicon quaternary ammonium salt in the antibacterial drug component based on the bacteria killing rate of the positively charged membrane to obtain antibacterial inhibition dosage requirement matching data.

[0134] In the embodiment of the present invention, the organosilicon quaternary ammonium salt antibacterial drug component is accurately extracted by high performance liquid chromatography combined with mass spectrometry. The specific operation is: select Waters ACQUITY UPLC H-Class chromatography system, equipped with BEH C18 chromatographic column, column temperature 40 ° C, mobile phase acetonitrile-water system, gradient elution. Use electrospray ionization time-of-flight mass spectrometry detector, set capillary voltage 3.5kV, source temperature 120 ° C, desolvation temperature 350 ° C. The standard product is measured in parallel multiple times to establish an organosilicon quaternary ammonium salt standard curve, and determine that the optimal extraction concentration range is 0.05-5.0μg / mL, and the linear correlation coefficient R2>0.999. Through standard chromatographic spectrum comparison and mass spectrometry fragmentation spectrum analysis, the chemical structure and purity of organosilicon quaternary ammonium salt are accurately identified, and the accurate molecular weight and chemical composition information of the antibacterial drug component are extracted. Fluorescence in situ hybridization technology combined with digital image processing algorithm is used to accurately estimate the number of bacteria per square centimeter in the bacterial corrosion skin damage area. The specific operation includes: collecting tissue sections of the skin injury area, using DAPI fluorescent staining reagent, with an excitation wavelength of 358nm and an emission wavelength of 461nm. High-resolution image acquisition is performed by confocal laser scanning microscopy, and the image resolution is set to 1024×1024 pixels. An image denoising algorithm based on wavelet transform is introduced to eliminate background interference. The region growing segmentation algorithm and morphological processing method are used to accurately extract the bacterial aggregation area. Based on the pixel grayscale distribution and morphological characteristics, an automatic counting model for the number of bacteria is established to achieve an accurate estimation of the number of bacteria per square centimeter. The process of organosilicon quaternary ammonium salt interacting with bacterial cell membranes and causing bacterial death is simulated. Organosilicon quaternary ammonium salts have a positive charge, while bacterial cell membranes usually have a negative charge. This charge difference causes organosilicon quaternary ammonium salts to be adsorbed on bacterial cell membranes. To simulate the bacterial killing rate, the following key factors need to be considered: the concentration of organosilicon quaternary ammonium salts, the negative charge density on the surface of bacterial cell membranes, the binding rate of organosilicon quaternary ammonium salts to bacterial cell membranes, and the cell membrane destruction rate. Molecular dynamics simulation can be used to study the interaction between organosilicon quaternary ammonium salts and bacterial cell membranes. Molecular dynamics simulation is a computer simulation method based on the principles of Newtonian mechanics, which can simulate the motion trajectory of molecular systems. First, it is necessary to construct molecular models of organosilicon quaternary ammonium salts and bacterial cell membranes. The molecular structure of organosilicon quaternary ammonium salts can be obtained from the chemical database, and the molecular structure of bacterial cell membranes can refer to relevant literature. Then, select a suitable force field to describe the interaction between molecules. A force field is a set of parameters and functions used to calculate the energy of a molecular system. Commonly used force fields include AMBER, CHARMM, and GROMOS. In molecular dynamics simulations, it is necessary to set appropriate simulation parameters, such as temperature, pressure, and time step. Temperature and pressure are usually set to physiological conditions (such as 37°C and 1 atmosphere), and the time step is usually set to 1-2 femtoseconds.During the simulation, the organosilicon quaternary ammonium salt molecules move randomly and collide with the bacterial cell membrane. When the organosilicon quaternary ammonium salt molecules are close enough to the bacterial cell membrane, an electrostatic attraction will be generated between them. The electrostatic attraction will cause the organosilicon quaternary ammonium salt molecules to adsorb to the bacterial cell membrane. The organosilicon quaternary ammonium salt molecules adsorbed on the cell membrane will gradually insert into the lipid bilayer of the cell membrane. The organosilicon quaternary ammonium salt molecules inserted into the lipid bilayer will destroy the structure of the cell membrane, resulting in increased permeability of the cell membrane. Increased cell membrane permeability will lead to leakage of cell contents and ultimately bacterial death. Through molecular dynamics simulation, the interaction process between organosilicon quaternary ammonium salt molecules and bacterial cell membranes can be observed, and the binding rate of organosilicon quaternary ammonium salts to bacterial cell membranes, the rate of cell membrane destruction, and the death rate of bacteria can be calculated. Alternatively, Monte Carlo simulation can be used to simulate the bacterial killing rate. Monte Carlo simulation is a computer simulation method based on random sampling. In the Monte Carlo simulation, some random events need to be set, such as the collision between the organosilicon quaternary ammonium salt molecule and the bacterial cell membrane, the insertion of the organosilicon quaternary ammonium salt molecule into the lipid bilayer of the cell membrane, and the destruction of the cell membrane. Then, the occurrence of these events is simulated by random sampling. For example, a random number generator can be used to generate a random number between 0 and 1. If the random number is less than a certain threshold, the event is considered to have occurred. By repeating random sampling many times, the process of bacterial killing can be simulated, and the death rate of bacteria can be calculated. According to the simulation results, the positive charge membrane bacteria killing rate can be obtained, and the data will serve as an important basis for matching the subsequent antibacterial inhibition dosage requirements. According to the positive charge membrane bacteria killing rate, the most suitable amount of organosilicon quaternary ammonium salt required to inhibit bacterial growth is determined. Step S333 has simulated the positive charge membrane bacteria killing rate of organosilicon quaternary ammonium salt, and this rate is directly related to the concentration of organosilicon quaternary ammonium salt. Step S332 has obtained an estimate of the number of bacteria per square centimeter in the dermatitis area. Now it is necessary to determine the concentration of silicone quaternary ammonium salt required to achieve the target bactericidal effect (for example, reducing the number of bacteria to below a safe level) based on these two data. The goal is to reduce the number of bacteria to a level that does not cause or aggravate dermatitis. The safe bacterial count threshold needs to refer to relevant clinical guidelines and literature. It is generally believed that for the skin surface, the risk of infection is low when the number of bacteria is less than 10 to the power of 3 (10^3) ​​per square centimeter. Therefore, the goal is to reduce the number of bacteria from the order of magnitude estimated in step S332 to below 10^3. Next, it is necessary to establish a mathematical model that describes the relationship between the concentration of silicone quaternary ammonium salt, the number of bacteria, and the killing time. A simple method is to use a first-order kinetic model. The first-order kinetic model assumes that the death rate of bacteria is proportional to the number of bacteria and the concentration of silicone quaternary ammonium salt. The model can be expressed by the following formula: dN / dt=-k×C×N, where dN / dt is the rate of change of the number of bacteria over time, k is the killing rate constant, C is the concentration of silicone quaternary ammonium salt, and N is the number of bacteria.The killing rate constant k can be obtained through the simulation results of step S333. By integrating the above formula, a function of the change of the number of bacteria over time can be obtained: N(t)=N0×exp(-k×C×t) where N(t) is the number of bacteria at time t, N0 is the initial number of bacteria (i.e., the number of bacteria estimated in step S332), and exp is an exponential function. According to the above formula, the time required to reduce the number of bacteria from N0 to the target value (10^3) ​​can be calculated. Then, according to the required time and the killing rate constant k, the required organosilicon quaternary ammonium salt concentration C is calculated. To ensure that the calculated drug concentration is within the safety range, it is necessary to refer to the toxicological data of organosilicon quaternary ammonium salts. Organosilicon quaternary ammonium salts are generally less toxic, but long-term use can cause skin irritation. Therefore, it is necessary to select a drug concentration that can effectively kill bacteria while avoiding skin irritation. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of organosilicon quaternary ammonium salts can be referred to. MIC refers to the minimum drug concentration that can inhibit bacterial growth, and MBC refers to the minimum drug concentration that can kill bacteria. Generally, a drug concentration slightly higher than the MIC can achieve a good bactericidal effect while reducing the toxic side effects of the drug. In addition, the dosage form and administration method of the drug need to be considered. Organosilicon quaternary ammonium salts are usually administered in the form of creams or ointments. The dosage form of creams and ointments will affect the release and penetration of the drug. Therefore, the drug concentration needs to be adjusted according to the dosage form and administration method of the drug to ensure that the drug can effectively reach the site of infection. Taking into account the individual differences of patients, such as age, weight, skin condition, etc., the dosage of the drug also needs to be appropriately adjusted. For example, for elderly patients or patients with dry skin, the drug concentration can be appropriately reduced to avoid skin irritation. Finally, based on the killing rate of positively charged membrane bacteria, combined with mathematical models, toxicological data, dosage forms and administration methods, and individual differences of patients, the antimicrobial inhibition dosage demand matching data, including the concentration, frequency of medication, and course of treatment of organosilicon quaternary ammonium salts, is determined.

[0135] Step S34 includes the following steps:

[0136] Step S341: performing skin tissue thermal energy absorption fluctuation analysis according to the nonlinear demand of infrared irradiation to obtain skin tissue thermal energy absorption fluctuation data;

[0137] Step S342: performing local vascular expansion analysis based on the skin tissue thermal energy absorption fluctuation data to obtain thermal energy-related local vascular expansion data;

[0138] Step S343: performing blood circulation velocity enhancement on the heat energy associated local vascular dilation data to obtain blood circulation velocity enhancement data;

[0139] Step S344: calculating the equal-increase equilibrium interval of intercellular hydration based on the skin tissue thermal energy absorption fluctuation data to obtain the equal-increase equilibrium interval of intercellular hydration;

[0140] Step S345: performing drug penetration efficiency coupling according to the blood circulation flow rate enhancement data and the equal-amount lift equilibrium interval to obtain drug penetration efficiency coupling data.

[0141] In an embodiment of the present invention, the absorption of heat energy by skin tissue during infrared irradiation is analyzed, and the dynamic changes of heat energy absorption are quantified. The nonlinear demand for infrared irradiation, including irradiation intensity, irradiation time and irradiation frequency, has been determined in step S32. To perform fluctuation analysis of heat energy absorption by skin tissue, a heat conduction model needs to be established. Skin tissue can be regarded as a multi-layer structure, including a stratum corneum, an epidermis and a dermis. Each layer has different thermophysical properties, such as thermal conductivity, density and specific heat capacity. The heat conduction model describes the transfer process of heat energy between different layers. The basis of the model is Fourier's heat conduction law: q=k×∇T, where q is the heat flux density, k is the thermal conductivity, and ∇T is the temperature gradient. The law shows that heat is transferred from a high temperature area to a low temperature area, and the transfer rate is proportional to the temperature gradient. Combining the heat conduction model of skin tissue with infrared irradiation parameters can simulate the temperature distribution of skin tissue during infrared irradiation. The simulation method can select the finite difference method or the finite element method. The finite difference method discretizes space and time and replaces the differential equation with a difference equation. The finite element method divides the skin tissue into a finite number of units and uses the interpolation function within the unit to approximate the temperature distribution. The boundary conditions include the heat flux density of the infrared irradiation surface and the constant temperature condition of the deep skin. The heat flux density of the infrared irradiation surface is related to the irradiation intensity. The constant temperature condition of the deep skin can be set to body temperature (about 37°C). The simulation results can obtain the temperature distribution of the skin tissue at different times and spatial positions. In order to analyze the fluctuation of thermal energy absorption, it is necessary to calculate the heat absorbed by the skin tissue per unit time. The absorbed heat is related to the temperature change and specific heat capacity: Q=m×c×ΔT, where Q is the absorbed heat, m is the mass of the skin tissue, c is the specific heat capacity, and ΔT is the temperature change. The skin tissue is divided into several small areas, and the heat absorbed by each small area per unit time is calculated. Then, the heat absorbed by all small areas is added up to obtain the heat absorbed by the entire skin tissue per unit time. By analyzing the amount of heat energy absorbed at different time points, the fluctuation data of thermal energy absorption of skin tissue can be obtained. The thermal energy absorption fluctuation data can include the following indicators: maximum thermal energy absorption, average thermal energy absorption, thermal energy absorption rate, and thermal energy absorption peak time. In addition, the difference in thermal energy absorption between different layers of skin tissue can also be analyzed. For example, the thermal energy absorption of the stratum corneum and the dermis can be compared to evaluate the effect of infrared irradiation on different skin layers. The uniformity of the temperature distribution of skin tissue can also be analyzed to evaluate the therapeutic effect of infrared irradiation. In order to verify the accuracy of the thermal conduction model, in vitro experiments can be performed. Using a skin model or ex vivo skin tissue, the temperature changes on the skin surface are measured under different infrared irradiation conditions. The experimental results are compared with the simulation results. If the two are in good agreement, the thermal conduction model is reliable. Finally, through the thermal conduction model and in vitro experiments, the thermal energy absorption fluctuation data of skin tissue can be obtained, which will serve as an important basis for subsequent local vasodilation analysis and calculation of intercellular hydration.According to the fluctuation data of thermal energy absorption of skin tissue, the degree of local vasodilation caused by infrared irradiation is analyzed. Infrared irradiation can increase the temperature of skin tissue, activate vascular endothelial cells, release vasodilator factors, and thus cause vasodilation. The degree of vasodilation is related to temperature changes and the sensitivity of vascular endothelial cells. To perform local vasodilation analysis, a vasodilation model needs to be established. The vasodilation model describes the relationship between vascular diameter, temperature changes, and vascular endothelial cell sensitivity. The change in vascular diameter can be expressed by the following formula: ΔD=α×ΔT×S, where ΔD is the change in vascular diameter, α is the vascular dilation coefficient, ΔT is the temperature change, and S is the sensitivity of vascular endothelial cells. The vascular dilation coefficient reflects the sensitivity of blood vessels to temperature changes. The sensitivity of vascular endothelial cells reflects the ability of vascular endothelial cells to respond to vasodilation factors. The temperature change can be obtained from the fluctuation data of thermal energy absorption of skin tissue in step S341. The vascular dilation coefficient and the sensitivity of vascular endothelial cells can be obtained by in vitro experiments or by referring to relevant literature. In vitro experiments usually use vascular ring experiments. The isolated vascular ring is suspended in a culture dish and the diameter of the vascular ring is measured. Then, the culture dish is heated to different temperatures and the change in the diameter of the vascular ring is measured. Based on the temperature change and the change in the diameter of the vascular ring, the vascular expansion coefficient can be calculated. The sensitivity of vascular endothelial cells can be evaluated by measuring the amount of vasodilator factors released by vascular endothelial cells. Vasodilator factors include nitric oxide (NO), prostacyclin (PGI2) and endothelin (ET-1). These vasodilator factors can be quantitatively analyzed by ELISA or chemiluminescence. The sensitivity of vascular endothelial cells can be evaluated based on the temperature change and the amount of vasodilator factor released. After obtaining the vascular expansion coefficient and vascular endothelial cell sensitivity, the change in vascular diameter can be calculated based on the vascular expansion model. In order to analyze vascular expansion more accurately, the elasticity of the blood vessel can be considered. The blood vessel has a certain elasticity, and when the blood vessel expands, the vascular wall will be stretched. The stretched vascular wall will generate a reaction force to prevent the blood vessel from continuing to expand. The elasticity of the blood vessel can be expressed by the following formula: F=k×ΔD, where F is the reaction force of the vascular wall, k is the vascular elasticity coefficient, and ΔD is the change in vascular diameter. The vascular elasticity coefficient reflects the degree of elasticity of the vascular wall. The vascular elasticity coefficient can be obtained through in vitro experiments or by consulting relevant literature. Taking the influence of vascular elasticity into account in the vascular dilation model can more accurately analyze the degree of vascular dilation. Finally, through the vascular dilation model and in vitro experiments, the thermal energy-related local vascular dilation data can be obtained, which will serve as an important basis for the subsequent solution of blood circulation flow enhancement. The data may include the change in vascular diameter, vascular dilation rate, and vascular dilation area. Based on the thermal energy-related local vascular dilation data, the degree of blood circulation flow enhancement caused by infrared irradiation is calculated. Vascular dilation reduces vascular resistance and increases blood flow, thereby leading to enhanced blood circulation flow.The degree of blood circulation velocity enhancement is related to the change in blood vessel diameter and blood viscosity. To solve the blood circulation velocity enhancement, a hemodynamic model needs to be established. The hemodynamic model describes the relationship between blood flow and blood vessel diameter, blood viscosity and blood pressure. The blood flow can be expressed by the following formula: Q=π×r^4×ΔP / (8×η×L) where Q is the blood flow, r is the blood vessel radius, ΔP is the blood pressure gradient, η is the blood viscosity, and L is the blood vessel length. This formula is called Poiseuille's law. This law shows that blood flow is proportional to the fourth power of the blood vessel radius, inversely proportional to blood viscosity, and proportional to the blood pressure gradient. The blood vessel radius can be obtained from the local vascular dilation data of step S342. The blood pressure gradient can be obtained by measuring the blood pressure of the arteries and veins. Blood viscosity can be measured by a blood viscometer. According to Poiseuille's law, the change in blood flow can be calculated. The blood circulation velocity is related to the blood flow and the cross-sectional area of ​​the blood vessel: v=Q / A where v is the blood circulation velocity, Q is the blood flow, and A is the cross-sectional area of ​​the blood vessel. The cross-sectional area of ​​blood vessels can be expressed by the following formula: A=π×r^2 Substituting the blood flow rate and the cross-sectional area of ​​blood vessels into the blood circulation velocity formula, the blood circulation velocity can be obtained. After calculating the hemodynamic changes, the compliance of blood vessels needs to be considered. Blood vessels have a certain compliance. When the blood flow increases, the blood vessels will expand to adapt to the changes in blood flow. The compliance of blood vessels can be expressed by the following formula: C=ΔV / ΔP Where C is the compliance of blood vessels, ΔV is the change in blood vessel volume, and ΔP is the change in blood pressure. Vascular compliance reflects the ability of blood vessels to adapt to changes in blood flow. Vascular compliance can be obtained through in vitro experiments or by consulting relevant literature. Taking the influence of vascular compliance into account in the hemodynamic model can more accurately calculate the blood circulation velocity. In addition, the non-Newtonian fluid properties of blood need to be considered. Blood is a non-Newtonian fluid whose viscosity is related to the shear rate. The shear rate refers to the shear force generated when blood flows. When the shear rate increases, the viscosity of blood decreases. The non-Newtonian fluid properties of blood can be described using a power law model or a Casson model. Taking the non-Newtonian fluid properties of blood into account in the hemodynamic model can more accurately calculate the blood circulation flow rate. Determine the optimal range of intercellular hydration after infrared irradiation. Infrared irradiation promotes local blood circulation, provides more water to cells, and thus increases the hydration of the intercellular space. Too high or too low hydration is not conducive to drug penetration. It is necessary to calculate an "equal amount of increase balance interval" within which the increase in hydration can effectively promote drug penetration without causing excessive damage to the skin barrier function. First of all, it is necessary to clarify the concept of intercellular hydration. Intercellular hydration refers to the content of water molecules in the intercellular space. The water in the intercellular space is very important for maintaining the elasticity and barrier function of the skin. Intercellular hydration can be measured by a skin moisture tester. The principle of the skin moisture tester is to measure the conductivity of the skin surface.Conductivity is proportional to the moisture content in the skin. Based on the conductivity of the skin surface, the intercellular hydration can be calculated. Infrared irradiation increases skin temperature and blood circulation, thereby increasing the moisture content of the intercellular space. However, excessive infrared irradiation will cause excessive dryness of the skin, which will reduce the hydration of the intercellular space. Therefore, it is necessary to control the intensity and time of infrared irradiation to achieve the best hydration effect. In order to determine the equilibrium interval of equal increase in intercellular hydration, in vitro experiments are required. Using a skin model or ex vivo skin tissue, the changes in intercellular hydration are measured under different infrared irradiation conditions. The skin model or ex vivo skin tissue is placed in an infrared irradiation device, and the intensity and time of infrared irradiation are adjusted. At regular intervals, the intercellular hydration is measured using a skin moisture tester. The changes in intercellular hydration over time are recorded, and the thermodynamic mass transfer theory and statistical physics methods are used to accurately calculate the equilibrium interval of equal increase in intercellular hydration. The specific implementation method is: construct a model of intercellular water molecule diffusion based on molecular dynamics simulation. The random motion trajectory of water molecules in the intercellular space is simulated by the Monte Carlo sampling method. Green-Cooper theory is introduced to establish a microscopic dynamic model of water molecule diffusion. Discrete wavelet transform is used to extract the multi-scale characteristics of water molecule diffusion and remove high-frequency noise interference. Based on the entropy weight method, a multi-dimensional feature evaluation model of intercellular hydration is constructed. Through the interval mapping technology, the water molecule diffusion characteristics are standardized, and the equal-increase equilibrium interval of intercellular hydration is accurately calculated. Combined with the blood circulation velocity enhancement data and the equal-increase equilibrium interval, the drug penetration efficiency is optimized to achieve the best therapeutic effect. In the previous steps, the blood circulation velocity enhancement data caused by infrared irradiation and the equal-increase equilibrium interval of intercellular hydration have been obtained. Both data have an important influence on drug penetration. The enhancement of blood circulation velocity can accelerate the transport of drugs from the administration site to the lesion site. The improvement of intercellular hydration can reduce the barrier effect of the skin and promote the penetration of drugs. In order to achieve the best drug penetration efficiency, it is necessary to couple the blood circulation velocity enhancement and intercellular hydration. The coupling method can choose response surface analysis or genetic algorithm. Response surface analysis is a statistical optimization method that can be used to determine the influence of multiple factors on the response variable. Here, the infrared irradiation intensity and time can be used as factors, and the drug penetration amount can be used as the response variable. Through response surface analysis, the infrared irradiation intensity and time that maximize the drug penetration amount can be found. Genetic algorithm is an optimization algorithm that can be used to find the optimal solution to complex problems. Here, the infrared irradiation intensity and time can be used as variables, and the drug penetration amount can be used as the objective function. Through genetic algorithm, the infrared irradiation intensity and time that maximize the drug penetration amount can be found. Before optimization, a drug penetration model needs to be established. The drug penetration model describes the diffusion and absorption process of drugs in skin tissue. The diffusion process of drugs can be described using Fick's first law.The concentration distribution of the drug in each skin layer is calculated according to the model. In the drug model, the physicochemical properties of the drug (such as molecular weight, lipid-water partition coefficient) and the physiological characteristics of the skin tissue (such as blood flow, stratum corneum thickness) need to be considered. At the same time, the blood circulation velocity enhancement and intercellular hydration also need to be reflected in the model. The model can be calibrated using the data obtained in steps S343 and S344. The optimized infrared irradiation intensity and time can be obtained through response surface analysis or genetic algorithm. At the same time, the release rate of the drug also needs to be considered. The drug can only be absorbed by the skin after being released from the preparation. The release rate of the drug is related to the type of preparation and the properties of the drug. A preparation with a faster release rate can be selected to increase the penetration of the drug. In addition, the metabolism of the drug also needs to be considered. The drug will be degraded by metabolic enzymes in the skin tissue, thereby reducing the concentration of the drug. Drugs that are not easily metabolized can be selected, or metabolic inhibitors can be used to increase the penetration of the drug. Finally, the optimal infrared irradiation intensity and time are determined by comprehensively considering the enhancement of blood circulation velocity, intercellular hydration, drug release rate and metabolism to achieve drug penetration efficiency coupling and obtain the optimized drug penetration amount. The optimized drug penetration amount, infrared irradiation parameters and other information are sent to the terminal as feedback suggestions on drug dosage to implement health management of incontinence dermatitis patients.

[0142] The present invention also provides a health management system for patients with incontinence dermatitis, which is used to implement the health management method for patients with incontinence dermatitis as described above. The health management system for patients with incontinence dermatitis includes:

[0143] The excrement state analysis module is used to obtain the historical diagnosis and treatment record set of incontinence dermatitis patients; extract the skin states of different incontinence dermatitis patients from the historical diagnosis and treatment record set of incontinence dermatitis patients to obtain the skin states of different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients on the historical diagnosis and treatment record set of incontinence dermatitis patients based on the skin states of different incontinence dermatitis patients to obtain the excrement abnormal state data between different incontinence dermatitis patients;

[0144] The bacterial corrosion skin damage analysis module is used to evaluate the urine bacteria increment of the excrement abnormal state data to obtain the urine bacteria state increment data; to perform numerical simulation of the progressive imbalance of the skin acid-base environment under repeated immersion conditions based on the urine bacteria state increment data to obtain the skin acid-base progressive numerical imbalance data; to quantify the structure of the bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain the bacterial corrosion skin damage data;

[0145] The drug penetration efficiency coupling module is used to analyze the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity; match the antibacterial inhibition dosage demand according to the attenuation trend of skin tissue metabolic activity to obtain the antibacterial inhibition dosage demand matching data; couple the antibacterial inhibition dosage demand matching data with the drug penetration efficiency to obtain the drug penetration efficiency coupling data;

[0146] The drug dosage recommendation generation module is used to generate drug dosage feedback recommendations among different incontinence dermatitis patients based on drug penetration efficiency coupling data, obtain drug dosage feedback recommendations, and send them to the terminal to perform health management of incontinence dermatitis patients.

[0147] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A health management method for patients with incontinence dermatitis, characterized in that: The following steps are involved: Step S1: Obtain a historical diagnosis and treatment record set of patients with incontinence dermatitis; extract skin conditions between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain skin conditions between different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients from the historical diagnosis and treatment record set of patients with incontinence dermatitis based on the skin conditions between different incontinence dermatitis patients to obtain excrement abnormal state data between different incontinence dermatitis patients; Step S2: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data; performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions based on the urine bacteria state increment data to obtain skin acid-base progressive numerical imbalance data; quantifying the structure of bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data; Step S3: Analyze the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity; match the antibacterial inhibition dosage demand according to the attenuation trend of skin tissue metabolic activity to obtain antibacterial inhibition dosage demand matching data; Carry out drug penetration efficiency coupling on the antibacterial inhibition dosage requirement matching data to obtain drug penetration efficiency coupling data; Step S4: Formulate drug dosage feedback suggestions for different incontinence dermatitis patients based on the drug penetration efficiency coupling data, obtain drug dosage feedback suggestions, and send them to the terminal to implement health management of incontinence dermatitis patients.

2. The health management method for incontinence dermatitis patients according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining a historical diagnosis and treatment record set of patients with incontinence dermatitis; Step S12: performing data cleaning on the historical diagnosis and treatment record set of patients with incontinence dermatitis to obtain a historical diagnosis and treatment cleansing record set; Step S13: extracting the skin conditions of different incontinence dermatitis patients from the historical diagnosis and treatment cleansing record set to obtain the skin conditions of different incontinence dermatitis patients; Step S14: Based on the skin conditions of different incontinence dermatitis patients, the historical diagnosis and treatment record set of incontinence dermatitis patients is mapped with the excrement state properties of different incontinence dermatitis patients to obtain the excrement abnormal state data of different incontinence dermatitis patients.

3. The health management method for incontinence dermatitis patients according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: evaluating the urine bacteria increment of the excrement abnormal state data to obtain urine bacteria state increment data; Step S22: performing numerical simulation of the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the urine bacteria status increment data, to obtain the progressive numerical imbalance data of the acid-base environment of the skin; Step S23: analyzing the excrement abnormal state data for repeated exposure conditions of fecal corrosive irritants to obtain repeated exposure conditions of corrosive irritants; Step S24: quantifying the structure of bacterial corrosion skin damage based on the repeated exposure conditions of corrosive irritants and the skin acid-base progressive numerical imbalance data to obtain bacterial corrosion skin damage data.

4. The health management method for patients with incontinence dermatitis according to claim 3, characterized in that: Step S22 includes the following steps: Step S221: evaluating the cumulative amount of urea residue under repeated soaking conditions according to the urine bacteria status increment data to obtain an estimated cumulative amount of urea residue; Step S222: performing a nonlinear numerical evaluation of ammonia decomposition based on the cumulative evaluation amount of urea residue to obtain nonlinear numerical data of ammonia decomposition; Step S223: performing temperature-dependent catalytic extreme value interval analysis on the nonlinear numerical data of ammonia decomposition to obtain the catalytic extreme value interval of ammonia decomposition temperature; Step S224: numerically simulate the progressive imbalance of the acid-base environment of the skin under repeated wetting conditions according to the catalytic extreme value range of the ammonia decomposition temperature to obtain the progressive numerical imbalance data of the acid-base environment of the skin.

5. The health management method for patients with incontinence dermatitis according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: extracting the repeated exposure time of the corrosive digestive enzyme under the repeated exposure condition of the corrosive stimulus to obtain the repeated exposure time of the digestive enzyme; Step S242: analyzing the change in concentration of the corrosive irritant under repeated exposure conditions to obtain data on the change in concentration of the irritant; Step S243: performing a cumulative quantitative analysis of skin surface protein degradation based on the digestive enzyme repeated exposure time and the irritant concentration change data to obtain cumulative quantitative data of protein degradation; Step S244: performing stratum corneum cell loose structure analysis on the protein degradation cumulative quantitative data to obtain stratum corneum cell loose structure; Step S245: performing a barrier function decline simulation assessment based on the loose structure of stratum corneum cells to obtain skin barrier function decline data; Step S246: quantify the structure of skin damage caused by bacterial corrosion according to the loose structure of stratum corneum cells and the skin barrier function decline data to obtain skin damage data caused by bacterial corrosion.

6. The health management method for patients with incontinence dermatitis according to claim 5, characterized in that: Step S245 includes the following steps: Based on the loose structure of stratum corneum cells, stratum corneum stress relaxation analysis was performed to obtain stratum corneum stress relaxation data; Performing hydration impairment gradient analysis on stratum corneum stress relaxation data to obtain hydration impairment gradient data; The external bacteria invasion index increment is calculated based on the hydration damage gradient data and the stratum corneum stress relaxation data to obtain the external bacteria invasion increment index; The barrier function decline simulation evaluation was performed based on the incremental index of external bacterial invasion to obtain the skin barrier function decline data.

7. The health management method for patients with incontinence dermatitis according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: analyzing the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity; Step S32: performing nonlinear matching of infrared irradiation demand according to the attenuation trend of skin tissue metabolic activity to obtain nonlinear infrared irradiation demand; Step S33: performing antibacterial inhibition dosage requirement matching on the bacterial corrosion skin damage data to obtain antibacterial inhibition dosage requirement matching data; Step S34: performing drug penetration efficiency coupling on the antibacterial inhibition dosage requirement matching data according to the infrared irradiation nonlinear requirement to obtain drug penetration efficiency coupling data.

8. The health management method for patients with incontinence dermatitis according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: obtaining an antibacterial drug component, wherein the antibacterial drug component includes an organosilicon quaternary ammonium salt; Step S332: estimating the bacterial order of magnitude per square centimeter for the bacterial corrosion skin damage data to obtain the bacterial order of magnitude; Step S333: simulating the killing rate of bacteria by the positively charged membrane according to the bacterial magnitude of the organosilicon quaternary ammonium salt in the antibacterial drug component to obtain the killing rate of bacteria by the positively charged membrane; Step S334: matching the antibacterial inhibition dosage requirement of the organosilicon quaternary ammonium salt in the antibacterial drug component based on the bacteria killing rate of the positively charged membrane to obtain antibacterial inhibition dosage requirement matching data.

9. The health management method for patients with incontinence dermatitis according to claim 7, characterized in that: Step S34 includes the following steps: Step S341: performing skin tissue thermal energy absorption fluctuation analysis according to the nonlinear demand of infrared irradiation to obtain skin tissue thermal energy absorption fluctuation data; Step S342: performing local vascular expansion analysis based on the skin tissue thermal energy absorption fluctuation data to obtain thermal energy-related local vascular expansion data; Step S343: performing blood circulation velocity enhancement on the heat energy associated local vascular dilation data to obtain blood circulation velocity enhancement data; Step S344: calculating the equal-increase equilibrium interval of intercellular hydration based on the skin tissue thermal energy absorption fluctuation data to obtain the equal-increase equilibrium interval of intercellular hydration; Step S345: performing drug penetration efficiency coupling according to the blood circulation flow rate enhancement data and the equal-amount lift equilibrium interval to obtain drug penetration efficiency coupling data.

10. A health management system for patients with incontinence dermatitis, characterized in that: Used to implement the health management method for incontinence dermatitis patients as claimed in claim 1, the incontinence dermatitis patient health management system comprises: The excrement state analysis module is used to obtain the historical diagnosis and treatment record set of incontinence dermatitis patients; extract the skin states of different incontinence dermatitis patients from the historical diagnosis and treatment record set of incontinence dermatitis patients to obtain the skin states of different incontinence dermatitis patients; perform excrement state property association mapping between different incontinence dermatitis patients on the historical diagnosis and treatment record set of incontinence dermatitis patients based on the skin states of different incontinence dermatitis patients to obtain the excrement abnormal state data between different incontinence dermatitis patients; The bacterial corrosion skin damage analysis module is used to evaluate the urine bacteria increment of the excrement abnormal state data to obtain the urine bacteria state increment data; to perform numerical simulation of the progressive imbalance of the skin acid-base environment under repeated immersion conditions based on the urine bacteria state increment data to obtain the skin acid-base progressive numerical imbalance data; to quantify the structure of the bacterial corrosion skin damage based on the skin acid-base progressive numerical imbalance data to obtain the bacterial corrosion skin damage data; The drug penetration efficiency coupling module is used to analyze the attenuation trend of skin tissue metabolic activity based on the bacterial corrosion skin damage data to obtain the attenuation trend of skin tissue metabolic activity; match the antibacterial inhibition dosage demand according to the attenuation trend of skin tissue metabolic activity to obtain the antibacterial inhibition dosage demand matching data; couple the antibacterial inhibition dosage demand matching data with the drug penetration efficiency to obtain the drug penetration efficiency coupling data; The drug dosage recommendation generation module is used to generate drug dosage feedback recommendations among different incontinence dermatitis patients based on drug penetration efficiency coupling data, obtain drug dosage feedback recommendations, and send them to the terminal to perform health management of incontinence dermatitis patients.

Images