Optimization method of traditional Chinese medicine plant combination formula for preventing hair loss and promoting hair growth based on neural network algorithm

Abstract

The present invention discloses a method for optimizing a Chinese herbal plant combination anti-hair loss and hair growth formula based on a neural network algorithm, comprising the following steps: S1, collecting user hair health data and establishing a personalized health profile; S2, based on the health profile, analyzing the user's physiological and psychological state in real time, establishing an emotional hair growth feedback mechanism to assess the risk of hair loss; S3, using a deep neural network to analyze the health profile, predicting the trend of hair loss and generating a personalized anti-hair loss and hair growth plan; S4, matching the best plant ingredients and proportions through a neural network optimization model, and dynamically adjusting the efficacy model; S5, adaptively adjusting the plant formula ratio through a quantum neural adaptive learning mechanism; S6, simulating different formula effects through a virtual test platform, optimizing the formula and feeding it back to the health profile; S7, forming a closed-loop feedback mechanism through continuous user feedback and physiological monitoring results, and regularly optimizing the formula. The present invention provides an accurate anti-hair loss and hair growth solution through personalized analysis and dynamic adjustment.

Description

Technical Field

[0001] The present invention relates to the fields of biomedicine and traditional Chinese medicine, and in particular to a method for optimizing a formula of a combination of traditional Chinese medicinal plants for preventing hair loss and promoting hair growth based on a neural network algorithm. Background Art

[0002] As people pay more and more attention to health and beauty, hair loss has become a global public health issue, especially hair loss in men and women, which has gradually had a profound impact on daily life and mental health. Although the existing methods of preventing hair loss and promoting hair growth are constantly developing, there are still obvious defects and limitations, especially in personalized treatment and dynamic optimization. The existing anti-hair loss and hair growth solutions mainly rely on topical medications, oral medications, and physical therapy. Although these methods can relieve hair loss symptoms, most treatment plans fail to make precise adjustments to the individual's physiological state, resulting in unstable and uncertain efficacy.

[0003] Current technologies for preventing hair loss and promoting hair growth usually rely on traditional drug formulas and static treatment plans. These methods mostly use single plant ingredients or standardized drug formulas and lack personalization and dynamic optimization capabilities. Although some Chinese medicinal plants are widely used in preventing hair loss and promoting hair growth, due to the complex pharmacological effects of plant ingredients and strong individual differences, existing treatment plans fail to fully consider factors such as the user's physiological state, genetic differences, living habits and emotions, thus affecting the treatment effect. The shortcomings of existing technologies are mainly reflected in the following aspects:

[0004] 1. Lack of personalized adjustment: Existing anti-hair loss and hair growth solutions are usually based on general formulas or standard treatments, lacking analysis of the personalized needs of each user. Treatment plans fail to fully consider the root causes of hair loss, such as hair follicle health, genetic differences, mood swings, and lifestyle habits, resulting in different efficacy due to individual differences. Traditional methods cannot be effectively adjusted in real time based on the user's physiological changes and mood swings.

[0005] 2. Lack of dynamic optimization in the treatment process: Most anti-hair loss and hair growth methods rely on fixed treatment plans and cannot be dynamically adjusted according to user feedback and physiological changes. The hair loss problem is a long-term development process, and the user's physiological state and environmental factors are constantly changing, requiring the treatment plan to be able to adapt to these changes. However, the existing technology lacks a dynamic optimization mechanism, resulting in the treatment plan failing to be adjusted and optimized in a timely manner according to user needs.

[0006] 3. Lack of comprehensive data analysis capabilities: Existing anti-hair loss and hair growth treatment methods usually rely on a single data point to formulate treatment plans, and fail to effectively integrate multi-dimensional data. The user's genetic information, mood swings, psychological state and other factors have a significant impact on hair growth, but the existing solutions fail to fully analyze these data, resulting in the inability to optimize the treatment plan from multiple dimensions.

[0007] 4. Lack of real-time feedback and rapid response mechanism: Existing treatment methods rely on static rules and fixed plans, lacking real-time feedback and adjustment capabilities. Treatment plans based on single plant ingredients or drug formulas cannot meet the physiological changes of individuals, and health records cannot be adjusted in time according to feedback data, resulting in delayed efficacy.

[0008] 5. Ignoring physiological cycles and long-term changes: Existing solutions do not fully consider the impact of physiological cycle fluctuations on treatment effects. Hair loss is closely related to an individual's physiological state, living habits, and emotions, and requires dynamic adjustments based on physiological changes. Traditional methods fail to provide long-term physiological feedback mechanisms or periodic adjustments, and are difficult to adapt to long-term changes.

[0009] Therefore, how to provide a method for optimizing the anti-hair loss and hair growth formula of a combination of traditional Chinese medicinal plants based on a neural network algorithm is an urgent problem that needs to be solved by those skilled in the art. Summary of the invention

[0010] One purpose of the present invention is to propose a method for optimizing a Chinese herbal plant combination anti-hair loss and hair growth formula based on a neural network algorithm. The present invention makes full use of deep neural networks, quantum feedback mechanisms and physiological data analysis technology, and combines the user's health records, real-time physiological data and virtual test results to describe in detail how to dynamically optimize the anti-hair loss and hair growth formula. Through comprehensive analysis of multi-dimensional data such as user genetic information, emotional fluctuations, and physiological cycles, the proportion of plant ingredients is automatically adjusted to provide a personalized anti-hair loss and hair growth solution.

[0011] The method for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm according to an embodiment of the present invention comprises the following steps:

[0012] Optionally, S2 includes the following steps:

[0013] S21. Collecting the user's physiological signals, including brain wave signals, skin electrical response, skin temperature, and skin conductance data, through a brain-skin interaction network, to monitor the user's emotional fluctuations in real time, generate an emotional fluctuation curve, and quantify the intensity and frequency of emotional changes;

[0014] S22, combining the bioelectrical signals of the user's scalp hair follicle area, collecting the temperature changes, blood flow conditions and skin conductance changes of the user's scalp area, calculating the correlation between hair follicle activity and skin physiological state, and obtaining the hair follicle state index;

[0015] S23. Based on the generated emotion fluctuation curve and the obtained hair follicle status index, a feedback mechanism model of emotion and hair growth is established through a weighted superposition algorithm, and the relationship between emotion fluctuation and hair follicle status index is quantified through weighted superposition:

[0016] ;

[0017] in, Indicates the comprehensive impact value of hair growth, is the intensity of the i-th emotion fluctuation, is the i-th hair follicle status index, is the impact of the ith temperature change on the hair follicle, , and are the weight coefficients of emotional fluctuations, hair follicle status, and temperature changes, respectively, and n is the number of signal points;

[0018] S24. Use a neural network algorithm to learn the correlation between mood fluctuations and hair follicle status, train a neural network model based on the input physiological data and mood fluctuation data, output the user's current hair loss risk and treatment recommendations, and adjust the treatment plan according to the changing trends of emotions and hair follicle status;

[0019] S25. Based on the feedback mechanism model obtained through training and combined with the user's long-term health data, the correlation information between mood fluctuations and hair growth in the personalized health file is improved, and the active ingredients and proportions of the personalized anti-hair loss and hair growth formula are adjusted accordingly. The formula is optimized according to the periodic characteristics of mood fluctuations and the long-term change trend of hair follicle status;

[0020] S26. Feedback the step optimization results to the user's health record, and adjust the personalized formula and update the treatment plan according to the user's hair loss progress, mood swings and changes in hair follicle health.

[0021] Optionally, S4 includes the following steps:

[0022] S41. Collect the user's genomic data, including gene variations, epigenetic characteristics and other genetic markers related to hair growth, use the gene data to establish a gene-hair drug efficacy synergy model, identify the genotype associated with hair loss, and associate the genotype data with the pharmacological properties of traditional Chinese medicine plants to determine the matching relationship between genotype and plant efficacy;

[0023] S42. Based on the identified genotype data and the pharmacological properties of Chinese medicinal plants, a correlation model between Chinese medicinal plant components and hair growth is established. The model assigns a pharmacological effect score to each plant component according to the pharmacological effects of different plant components and their compatibility with the genotype, and generates a pharmacological effect score matrix;

[0024] S43. Calculate the best plant ingredients and their preliminary proportions through a multi-objective optimization algorithm based on the efficacy scoring matrix to generate the best plant formula:

[0025] ;

[0026] in, Score the efficacy of the best botanical formulas, is the efficacy score of the i-th plant component, is the fitness weight of the ith component under a specific genotype, n is the number of plant components, is the jth emotional fluctuation intensity, is the jth hair follicle status index, is the impact value of the jth temperature change on the hair follicle, m is the correlation coefficient of mood fluctuation, hair follicle state and temperature, m is the number of signal points of mood fluctuation, hair follicle state and temperature change, is the influencing factor of the kth genotype, Feedback on the effect of genotype on drug efficacy, is the weight coefficient of the effect of genotype on drug efficacy, p is the number of genotypes, It is the comprehensive impact value of hair growth, indicating the combined impact of emotional fluctuations, hair follicle status and temperature on hair growth.

[0027] S44. Based on the generated optimal plant formula, the quantum superposition feedback mechanism is used to dynamically adjust the efficacy model. Through real-time feedback data, the parameters of the efficacy model are adjusted to adapt to the individual differences of users, so that the efficacy can be optimized under the physiological conditions of different users.

[0028] Optionally, S5 includes the following steps:

[0029] S51, collecting the user's physiological data, including heart rate, blood pressure, body temperature, skin galvanic response, scalp temperature, and hair quality data, through real-time physiological monitoring equipment, and combining the long-term physiological data in the user's personalized health file to establish the user's physiological state feature vector to reflect the user's current physiological state;

[0030] S52. Use a multi-dimensional force field neural network to conduct deep learning analysis on the user's physiological state feature vector, calculate the adaptability of Chinese herbal plant ingredients under each physiological state, and combine personalized health records and real-time feedback to generate a scoring matrix for the adaptability of plant ingredients under different physiological states:

[0031] ;

[0032] in, For the adaptability of plant ingredients under time, is the interaction weight between neuron i and neuron j, and are the ideal distance and current actual distance between neuron i and neuron j, respectively, indicating the interaction strength between physiological data. Score the efficacy of plant ingredient j, To generate a personalized botanical formula, It is a dynamic adjustment factor driven by changes in the user's physiological state;

[0033] S53. Based on the user's current physiological state characteristics, combined with the efficacy score and adaptability score matrix of the plant ingredients, an innovative multi-objective optimization algorithm is used to calculate the optimal adaptation ratio of each plant ingredient and generate a personalized preliminary ratio of the plant formula to provide the best therapeutic effect under different physiological states;

[0034] S54. Dynamically adjust the ratio of plant ingredients based on real-time user feedback data and changes in physiological status, so that the formula is continuously optimized under different physiological conditions of individuals and provides the best anti-hair loss and hair growth effect;

[0035] S55. Combine the user's long-term physiological monitoring data, feedback data and emotional fluctuation information to continuously adjust the personalized plant formula so that the formula is dynamically optimized as the user's physiological state changes, providing personalized anti-hair loss and hair growth effects during the treatment process.

[0036] Optionally, the S6 includes the following steps:

[0037] S61. In the virtual test platform for efficacy optimization, based on the user's personalized health profile and physiological state feature vector, simulate the effects of different plant formulas on the user's physiological state, generate virtual test results, and obtain the adaptability score of each formula under different physiological states;

[0038] S62. Provide real-time feedback and dynamic analysis of the user's physiological data through a virtual test platform, use a multi-dimensional optimization feedback function to perform a weighted evaluation of the adaptability scores of different plant formulas, and introduce an adaptability dynamic adjustment factor , generate the optimized recipe scoring matrix:

[0039] ;

[0040] in, Score the final optimized recipe at time t, is the dynamic influencing factor of plant component i, k is the exponential adjustment coefficient of the adaptability score, which controls the nonlinear influence of the adaptability score. is a dynamic weighting factor that affects the degree of influence of changes in physiological state on formulation optimization. is the change in the current physiological state feature vector, reflecting the user's physiological changes;

[0041] S63. Use the virtual test platform to simulate different ratios of each plant ingredient, and use adaptive oscillation to further optimize the plant formula to maintain the best therapeutic effect under different physiological conditions:

[0042] ;

[0043] in, is the score of the plant formula adjusted for adaptive oscillation, is the oscillation amplitude factor, which regulates the intensity of the adaptive oscillation of the plant formula. is the time adjustment factor, controlling the dynamic response of the oscillation to the recipe optimization, is the initial phase of the oscillation, affecting the initial physiological feedback. It is the time integral of the physiological state characteristic vector, reflecting the cumulative effect of physiological changes;

[0044] S64. Generate the final optimized plant formula based on the virtual test results and adaptive oscillation adjustment, and feed back the optimized formula data to the user's health record to perform dynamic optimization and personalized adjustment of the formula;

[0045] S65. Through the virtual test platform and real-time physiological data feedback, the plant formula is continuously updated and optimized to adapt it to changes in the user's physiological state, thereby providing continuous and effective anti-hair loss and hair growth effects at different treatment stages.

[0046] Optionally, the S7 includes the following steps:

[0047] S71. Build a physiological-formula matching engine through real-time physiological data, long-term hair loss history, user personalized health records and virtual test data, and use a unified multi-dimensional adaptive evolutionary optimization algorithm to transform the user's physiological state into a multi-dimensional adaptive target space, so that the proportion of plant formula ingredients dynamically adapts to changes in the user's physiological state;

[0048] S72. After each optimization cycle, the physiological-plant interaction adjustment framework is used to fine-tune the plant formula by calculating the "interaction energy" between the physiological state and the plant ingredients, so that the formula adjustment can respond dynamically according to physiological fluctuations and optimize the adaptability of the plant ingredients in each cycle:

[0049] ;

[0050] in, For the adjusted plant formula, For the previous cycle of plant formula, is the fitness function, which represents the relationship between plant components and physiological states. is the optimal adaptation point of the i-th plant component, S(t) is the physiological state at the current moment, is the adjustment factor for plant ingredients, is the oscillation adjustment coefficient, is the physiological period oscillation function, is a small constant used to avoid the situation where the oscillation amplitude is zero;

[0051] S73. Based on the physiological cycle activation model and combined with the long-term physiological feedback mechanism, the effect of the optimized plant formula is tracked. By analyzing the change trend of the user's physiological cycle and the adaptability of the formula ingredients, the proportion of the plant formula ingredients is further dynamically adjusted, so that the plant formula can adapt to long-term physiological changes and demand fluctuations;

[0052] S74. Synchronize the optimized plant formula and the user's physiological status data to the user's health record each time, and conduct periodic analysis in combination with long-term physiological data. Based on long-term data and feedback, re-evaluate and adjust the plant formula regularly.

[0053] The beneficial effects of the present invention are:

[0054] (1) The present invention significantly improves the accuracy and personalized treatment effect of the optimization of the Chinese herbal plant combination anti-hair loss and hair growth formula by combining neural network algorithms, quantum feedback mechanisms and multi-dimensional data analysis technology. In particular, in terms of personalized analysis and dynamic adjustment, the present invention overcomes the shortcomings of traditional treatment methods and can optimize and adjust in real time based on multi-dimensional data such as the user's physiological data, genetic information, and emotional fluctuations, thereby achieving a personalized treatment plan with more significant anti-hair loss effect.

[0055] (2) The present invention optimizes the dynamic adjustment process of plant formulas by introducing a physiological-plant interaction adjustment framework and a long-term feedback mechanism based on the physiological cycle, thereby improving the formula adaptability and sustainability of efficacy. This solution can adapt to the long-term physiological changes of users, ensure that the formula is continuously optimized during the treatment process, and enable users to obtain the best treatment effect at different stages of the physiological cycle.

[0056] (3) The present invention provides an efficient and accurate hair loss prevention and hair growth solution by comprehensively using deep neural networks, quantum superposition feedback mechanisms and virtual test platforms. The solution can automatically adjust the plant formula based on real-time feedback and long-term data analysis to ensure that it can be optimized according to the specific needs of the user during each treatment cycle, thereby significantly improving the effect of hair loss prevention and hair growth and the personalized adaptability of treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 This is a flow chart of the method for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm proposed by the present invention;

[0059] Figure 2 This is a diagram of the physiological cycle activation model and long-term physiological feedback mechanism proposed by the present invention. DETAILED DESCRIPTION

[0060] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0061] refer to Figure 1-2 The method for optimizing the formula of Chinese herbal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm comprises the following steps:

[0062] In this implementation, S2 includes the following steps:

[0063] S21. Collecting the user's physiological signals, including brain wave signals, skin electrical response, skin temperature, and skin conductance data, through a brain-skin interaction network, to monitor the user's emotional fluctuations in real time, generate an emotional fluctuation curve, and quantify the intensity and frequency of emotional changes;

[0064] S22, combining the bioelectrical signals of the user's scalp hair follicle area, collecting the temperature changes, blood flow conditions and skin conductance changes of the user's scalp area, calculating the correlation between hair follicle activity and skin physiological state, and obtaining the hair follicle state index;

[0065] S23. Based on the generated emotion fluctuation curve and the obtained hair follicle status index, a feedback mechanism model of emotion and hair growth is established through a weighted superposition algorithm, and the relationship between emotion fluctuation and hair follicle status index is quantified through weighted superposition:

[0066] ;

[0067] in, Indicates the comprehensive impact value of hair growth, is the intensity of the i-th emotion fluctuation, is the i-th hair follicle status index, is the impact of the ith temperature change on the hair follicle, , and are the weight coefficients of emotional fluctuations, hair follicle status, and temperature changes, respectively, and n is the number of signal points;

[0068] S24. Use a neural network algorithm to learn the correlation between mood fluctuations and hair follicle status, train a neural network model based on the input physiological data and mood fluctuation data, output the user's current hair loss risk and treatment recommendations, and adjust the treatment plan according to the changing trends of emotions and hair follicle status;

[0069] S25. Based on the feedback mechanism model obtained through training and combined with the user's long-term health data, the correlation information between mood fluctuations and hair growth in the personalized health file is improved, and the active ingredients and proportions of the personalized anti-hair loss and hair growth formula are adjusted accordingly. The formula is optimized according to the periodic characteristics of mood fluctuations and the long-term change trend of hair follicle status;

[0070] S26. Feedback the step optimization results to the user's health record, and adjust the personalized formula and update the treatment plan according to the user's hair loss progress, mood swings and changes in hair follicle health.

[0071] In this implementation manner, S4 includes the following steps:

[0072] S41. Collect the user's genomic data, including gene variations, epigenetic characteristics and other genetic markers related to hair growth, use the gene data to establish a gene-hair drug efficacy synergy model, identify the genotype associated with hair loss, and associate the genotype data with the pharmacological properties of traditional Chinese medicine plants to determine the matching relationship between genotype and plant efficacy;

[0073] S42. Based on the identified genotype data and the pharmacological properties of Chinese medicinal plants, a correlation model between Chinese medicinal plant components and hair growth is established. The model assigns a pharmacological effect score to each plant component according to the pharmacological effects of different plant components and their compatibility with the genotype, and generates a pharmacological effect score matrix;

[0074] S43. Calculate the best plant ingredients and their preliminary proportions through a multi-objective optimization algorithm based on the efficacy scoring matrix to generate the best plant formula:

[0075] ;

[0076] in, Score the efficacy of the best botanical formulas, is the efficacy score of the i-th plant component, is the fitness weight of the ith component under a specific genotype, n is the number of plant components, is the jth emotional fluctuation intensity, is the jth hair follicle status index, is the impact value of the jth temperature change on the hair follicle, m is the correlation coefficient of mood fluctuation, hair follicle state and temperature, m is the number of signal points of mood fluctuation, hair follicle state and temperature change, is the influencing factor of the kth genotype, Feedback on the effect of genotype on drug efficacy, is the weight coefficient of the effect of genotype on drug efficacy, p is the number of genotypes, It is the comprehensive impact value of hair growth, indicating the combined impact of emotional fluctuations, hair follicle status and temperature on hair growth.

[0077] S44. Based on the generated optimal plant formula, the quantum superposition feedback mechanism is used to dynamically adjust the efficacy model. Through real-time feedback data, the parameters of the efficacy model are adjusted to adapt to the individual differences of users, so that the efficacy can be optimized under the physiological conditions of different users.

[0078] In this implementation manner, S5 includes the following steps:

[0079] S51, collecting the user's physiological data, including heart rate, blood pressure, body temperature, skin galvanic response, scalp temperature, and hair quality data, through real-time physiological monitoring equipment, and combining the long-term physiological data in the user's personalized health file to establish the user's physiological state feature vector to reflect the user's current physiological state;

[0080] S52. Use a multi-dimensional force field neural network to conduct deep learning analysis on the user's physiological state feature vector, calculate the adaptability of Chinese herbal plant ingredients under each physiological state, and combine personalized health records and real-time feedback to generate a scoring matrix for the adaptability of plant ingredients under different physiological states:

[0081] ;

[0082] in, For the adaptability of plant ingredients under time, is the interaction weight between neuron i and neuron j, and are the ideal distance and current actual distance between neuron i and neuron j, respectively, indicating the interaction strength between physiological data. Score the efficacy of plant ingredient j, To generate a personalized botanical formula, It is a dynamic adjustment factor driven by changes in the user's physiological state;

[0083] S53. Based on the user's current physiological state characteristics, combined with the efficacy score and adaptability score matrix of the plant ingredients, an innovative multi-objective optimization algorithm is used to calculate the optimal adaptation ratio of each plant ingredient and generate a personalized preliminary ratio of the plant formula to provide the best therapeutic effect under different physiological states;

[0084] S54. Dynamically adjust the ratio of plant ingredients based on real-time user feedback data and changes in physiological status, so that the formula is continuously optimized under different physiological conditions of individuals and provides the best anti-hair loss and hair growth effect;

[0085] S55. Combine the user's long-term physiological monitoring data, feedback data and emotional fluctuation information to continuously adjust the personalized plant formula so that the formula is dynamically optimized as the user's physiological state changes, providing personalized anti-hair loss and hair growth effects during the treatment process.

[0086] In this implementation manner, S6 includes the following steps:

[0087] S61. In the virtual test platform for efficacy optimization, based on the user's personalized health profile and physiological state feature vector, simulate the effects of different plant formulas on the user's physiological state, generate virtual test results, and obtain the adaptability score of each formula under different physiological states;

[0088] S62. Provide real-time feedback and dynamic analysis of the user's physiological data through a virtual test platform, use a multi-dimensional optimization feedback function to perform a weighted evaluation of the adaptability scores of different plant formulas, and introduce an adaptability dynamic adjustment factor , generate the optimized recipe scoring matrix:

[0089] ;

[0090] in, Score the final optimized recipe at time t, is the dynamic influencing factor of plant component i, k is the exponential adjustment coefficient of the adaptability score, which controls the nonlinear influence of the adaptability score. is a dynamic weighting factor that affects the degree of influence of changes in physiological state on formulation optimization. is the change in the current physiological state feature vector, reflecting the user's physiological changes;

[0091] S63. Use the virtual test platform to simulate different ratios of each plant ingredient, and use adaptive oscillation to further optimize the plant formula to maintain the best therapeutic effect under different physiological conditions:

[0092] ;

[0093] in, is the score of the plant formula adjusted for adaptive oscillation, is the oscillation amplitude factor, which regulates the intensity of the adaptive oscillation of the plant formula. is the time adjustment factor, controlling the dynamic response of the oscillation to the recipe optimization, is the initial phase of the oscillation, affecting the initial physiological feedback. It is the time integral of the physiological state characteristic vector, reflecting the cumulative effect of physiological changes;

[0094] S64. Generate the final optimized plant formula based on the virtual test results and adaptive oscillation adjustment, and feed back the optimized formula data to the user's health record to perform dynamic optimization and personalized adjustment of the formula;

[0095] S65. Through the virtual test platform and real-time physiological data feedback, the plant formula is continuously updated and optimized to adapt it to changes in the user's physiological state, thereby providing continuous and effective anti-hair loss and hair growth effects at different treatment stages.

[0096] In this implementation manner, the S7 includes the following steps:

[0097] S71. Build a physiological-formula matching engine through real-time physiological data, long-term hair loss history, user personalized health records and virtual test data, and use a unified multi-dimensional adaptive evolutionary optimization algorithm to transform the user's physiological state into a multi-dimensional adaptive target space, so that the proportion of plant formula ingredients dynamically adapts to changes in the user's physiological state;

[0098] S72. After each optimization cycle, the physiological-plant interaction adjustment framework is used to fine-tune the plant formula by calculating the "interaction energy" between the physiological state and the plant ingredients, so that the formula adjustment can respond dynamically according to physiological fluctuations and optimize the adaptability of the plant ingredients in each cycle:

[0099] ;

[0100] in, For the adjusted plant formula, For the previous cycle of plant formula, is the fitness function, which represents the relationship between plant components and physiological states. is the optimal adaptation point of the i-th plant component, S(t) is the physiological state at the current moment, is the adjustment factor for plant ingredients, is the oscillation adjustment coefficient, is the physiological period oscillation function, is a small constant used to avoid the situation where the oscillation amplitude is zero;

[0101] S73. Based on the physiological cycle activation model and combined with the long-term physiological feedback mechanism, the effect of the optimized plant formula is tracked. By analyzing the change trend of the user's physiological cycle and the adaptability of the formula ingredients, the proportion of the plant formula ingredients is further dynamically adjusted, so that the plant formula can adapt to long-term physiological changes and demand fluctuations;

[0102] S74. Synchronize the optimized plant formula and the user's physiological status data to the user's health record each time, and conduct periodic analysis in combination with long-term physiological data. Based on long-term data and feedback, re-evaluate and adjust the plant formula regularly.

[0103] Embodiment 1:

[0104] In order to verify the feasibility of the present invention in implementation, the present invention was applied to a dermatology clinic in Beijing to optimize its anti-hair loss and hair growth treatment plan. The clinic focuses on treating various types of hair loss problems, especially androgenic alopecia, stress alopecia, etc. Although the clinic has adopted traditional drug treatments and topical treatment methods, the existing anti-hair loss treatment plans still have certain limitations, especially in personalized treatment and dynamic adjustment. These traditional methods fail to fully consider individual differences such as patients' physiological state, genetic information, living habits and mood swings, so there is great uncertainty in the treatment effect. For this reason, the clinic decided to introduce the Chinese herbal plant combination anti-hair loss and hair growth formula optimization method based on the neural network algorithm of the present invention, aiming to provide each patient with a personalized and precise anti-hair loss and hair growth treatment plan, and dynamically optimize the treatment effect according to real-time feedback and physiological changes.

[0105] In this example, we first collected the patient's hair health parameters based on their specific hair loss type and health status, and established a personalized health profile based on their genomic data, lifestyle, environmental factors, mood swings, etc. In order to ensure that each patient's treatment plan is as accurate as possible, the system generates a preliminary anti-hair loss formula based on these data as the starting point for personalized treatment.

[0106] Next, we applied a deep neural network algorithm to model and learn each patient's health profile, combined with their historical hair loss data, emotion-hair growth feedback mechanism, and real-time user feedback to predict future hair loss trends, potential time nodes, and their causes. Based on these predictions, the system automatically generates a hair loss prevention and hair growth plan suitable for the patient, and precisely adjusts the ingredients and proportions of Chinese medicinal plants to ensure that the treatment plan can be dynamically optimized according to the user's physiological response.

[0107] In actual application, the clinic uses the quantum superposition feedback mechanism to dynamically adjust the drug efficacy model according to the patient's real-time physiological response to ensure the adaptability and matching of the drug efficacy under different physiological conditions. In addition, the system also simulates the application effects of different plant formulas through a virtual test platform, further optimizes the proportional adjustment of plant ingredients based on the results of the virtual test, and feeds back the optimized plan to the patient's health record.

[0108] In order to verify the effectiveness of this treatment method, a dermatology clinic in Beijing conducted a three-month anti-hair loss and hair growth treatment on 10 male patients and 10 female patients aged 18 to 45 from May 1, 2023 to July 31, 2023. Before entering the experiment, all patients underwent detailed physiological examinations and genetic data collection to ensure that the type of hair loss was androgenic alopecia, stress alopecia or hereditary alopecia.

[0109] Table 1: Data table of the effect of anti-hair loss and hair growth treatment

[0110] ;

[0111] The experimental results show that after three months of treatment, the hair follicle density of patients increased by an average of 15%-20%, of which the hair follicle density in the hairline area increased by about 25%; the hair diameter generally increased by 10%-15%, especially in the forehead and hairline area, the hair diameter increased by about 18%; the hair growth rate increased from 0.5cm per month to 1.0-1.3cm, an increase of 100%. The amount of hair loss was significantly reduced, and about 75% of patients reduced their monthly hair loss from 150 hairs to 60-80 hairs. Especially for patients with stress-induced hair loss, the amount of hair loss was reduced by more than 50%.

[0112] In addition, during the treatment process, the system dynamically adjusts the plant formula according to each patient's real-time feedback and physiological state to optimize the treatment effect. For patients with hereditary hair loss, the system increases the proportion of plant ingredients with stronger efficacy, and for patients with greater mood swings, the system increases plant ingredients with stress resistance, thereby effectively mitigating the negative impact of mood swings on hair growth.

[0113] After the treatment, the clinic conducted a 6-month long-term efficacy follow-up on all patients. The data showed that 80% of the patients maintained significant therapeutic effects within 6 months after treatment, and their hair density and growth rate remained at a high level.

[0114] This embodiment shows that the Chinese medicinal plant combination anti-hair loss and hair growth formula optimization method based on the neural network algorithm of the present invention significantly improves the treatment effect through personalized analysis, dynamic adjustment and virtual test platform optimization. Experimental data proves that this method can effectively alleviate the symptoms of hair loss, promote hair growth, and continuously optimize according to the user's real-time feedback and physiological state. Through this innovative treatment plan, patients can obtain a more personalized and efficient anti-hair loss and hair growth effect, effectively improving the long-term stability and personalized adaptability of the treatment.

[0115] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A device for optimizing the formula of Chinese herbal medicine combination for preventing hair loss and promoting hair growth based on a neural network algorithm, characterized in that: The device includes: The first module is used to collect the user's hair health parameters through non-invasive detection technology, obtain hair follicle density, hair diameter, and hair loss cycle data, and combine the user's genomic information, environmental changes, climate conditions, and living habits to generate and dynamically update a personalized health profile; The second module is used to analyze the changes in the user's physiological and psychological state in real time through the brain-skin interaction network based on the user's health record, establish an emotion-hair growth feedback mechanism, identify the impact of emotional changes on hair growth, improve the data association in the personalized health record, and further predict the potential risk of hair loss development; The third module is used to use deep neural networks to model and learn personalized health records, combine historical hair loss data, emotion-hair growth feedback mechanism and real-time user feedback to predict future hair loss trends, potential time nodes and causes, and output a hair loss prevention and hair growth plan suitable for users; The fourth module is used for gene-hair pharmacological synergistic modeling, combining user gene information and the pharmacological properties of Chinese medicinal plants, accurately matching the best plant ingredients with the initial ratio by optimizing the neural network model, and dynamically adjusting the pharmacological model using the quantum superposition feedback mechanism, and adjusting the adaptability and matching degree of the pharmacological effect in real time according to the user's physiological response; The fifth module is used to adaptively adjust the proportion of ingredients in personalized botanical formulas through quantum neuroadaptive learning mechanisms combined with the user's real-time physiological monitoring data to optimize the efficacy of the formula under specific physiological conditions; The sixth module is used to simulate the application effects of different formulas in the efficacy optimization virtual test platform, further optimize the ingredients and proportions of the formulas based on the virtual test results, and feed back the optimized solutions to the user's health records; The seventh module is used to form a closed-loop feedback mechanism through continuous user feedback, virtual test data and physiological monitoring results. It regularly re-evaluates and optimizes the formula based on the user's long-term hair loss data and physiological changes, so that the personalized plan can be continuously adjusted and updated as the user's status changes.

2. The device for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm according to claim 1, characterized in that: The second module specifically includes: The first submodule is used to collect the user's physiological signals through the brain-skin interaction network, including brain wave signals, skin electrical response, skin temperature, and skin conductance data, monitor the user's emotional fluctuations in real time, generate emotional fluctuation curves, and quantify the intensity and frequency of emotional changes; The second submodule is used to combine the bioelectrical signals of the user's scalp hair follicle area, collect the temperature changes, blood flow conditions and skin conductance changes in the user's scalp area, calculate the correlation between hair follicle activity and skin physiological state, and obtain the hair follicle state index; The third submodule is used to establish a feedback mechanism model of emotion and hair growth based on the generated emotion fluctuation curve and the obtained hair follicle status index through a weighted superposition algorithm, and quantify the relationship between emotion fluctuation and hair follicle status index through weighted superposition: ； in, Indicates the comprehensive impact value of hair growth, is the intensity of the i-th emotion fluctuation, is the i-th hair follicle status index, is the impact of the ith temperature change on the hair follicle, , and are the weight coefficients of emotional fluctuations, hair follicle status, and temperature changes, respectively, and n is the number of signal points; The fourth submodule is used to use a neural network algorithm to learn the correlation between emotional fluctuations and hair follicle status, train the neural network model based on the input physiological data and emotional fluctuation data, output the user's current hair loss risk and treatment suggestions, and adjust the treatment plan according to the changing trends of emotions and hair follicle status; The fifth submodule is used to improve the correlation information between mood swings and hair growth in the personalized health file based on the feedback mechanism model obtained through training and combined with the user's long-term health data, and adjust the active ingredients and proportions of the personalized anti-hair loss and hair growth formula accordingly, and optimize the formula according to the periodic characteristics of mood swings and the long-term change trend of hair follicle status; The sixth sub-module is used to feed back the step optimization results to the user's health record, and adjust the personalized formula and update the treatment plan according to the user's hair loss progress, mood swings and changes in the health status of hair follicles.

3. The device for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm according to claim 1, characterized in that: The fourth module specifically includes: The seventh submodule is used to collect the user's genomic data, including gene variations, epigenetic characteristics and other genetic markers related to hair growth, and use the gene data to establish a gene-hair drug efficacy synergy model to identify genotypes related to hair loss, and associate the genotype data with the pharmacological properties of traditional Chinese medicine plants to determine the matching relationship between genotypes and plant efficacy; The eighth submodule is used to establish a correlation model between Chinese medicinal plant components and hair growth based on the identified genotype data and the pharmacological properties of Chinese medicinal plants. The model assigns a pharmacological effect score to each plant component according to the pharmacological effects of different plant components and their compatibility with the genotype, and generates a pharmacological effect score matrix; The ninth submodule is used to calculate the best plant ingredients and their preliminary proportions based on the efficacy score matrix through a multi-objective optimization algorithm to generate the best plant formula: ; in, Score the efficacy of the best botanical formulas, is the efficacy score of the i-th plant component, is the fitness weight of the i-th component under a specific genotype, n is the number of plant components, is the jth emotional fluctuation intensity, is the jth hair follicle status index, is the impact value of the jth temperature change on the hair follicle, m is the number of signal points of emotional fluctuations, hair follicle status and temperature changes, is the correlation coefficient between mood swings, hair follicle status and temperature, is the influencing factor of the kth genotype, Feedback on the effect of genotype on drug efficacy, is the weight coefficient of the effect of genotype on drug efficacy, p is the number of genotypes, is the comprehensive impact value of hair growth, indicating the comprehensive impact of emotional fluctuations, hair follicle status and temperature on hair growth; The tenth submodule is used to dynamically adjust the efficacy model based on the generated optimal plant formula using the quantum superposition feedback mechanism. Through real-time feedback data, the parameters of the efficacy model are adjusted to adapt to the individual differences of users, so that the efficacy can be optimized under the physiological conditions of different users.

4. The device for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm according to claim 1, characterized in that: The fifth module specifically includes: The eleventh submodule is used to collect the user's physiological data through real-time physiological monitoring equipment, including heart rate, blood pressure, body temperature, skin galvanic response, scalp temperature, and hair quality data, and to establish the user's physiological state feature vector in combination with the long-term physiological data in the user's personalized health file to reflect the user's current physiological state; The twelfth submodule is used to use the multi-dimensional force field neural network to perform deep learning analysis on the user's physiological state feature vector, calculate the adaptability of Chinese herbal plant ingredients under each physiological state, and generate a plant ingredient adaptability score matrix for different physiological states by combining personalized health records and real-time feedback: ; in, is the adaptability of plant components at time t, is the interaction weight between neuron i and neuron j, and are the ideal distance and current actual distance between neuron i and neuron j, respectively, indicating the interaction strength between physiological data. Score the efficacy of plant ingredient j, To generate a personalized botanical formula, It is a dynamic adjustment factor driven by changes in the user's physiological state; The thirteenth submodule is used to calculate the optimal adaptation ratio of each plant ingredient based on the user's current physiological state characteristics, combined with the efficacy score and adaptability score matrix of the plant ingredients, using an innovative multi-objective optimization algorithm, and generate a personalized preliminary ratio of the plant formula to provide the best therapeutic effect under different physiological states; The fourteenth submodule is used to dynamically adjust the ratio of plant ingredients according to real-time user feedback data and changes in physiological status, so that the formula is continuously optimized under different physiological conditions of individuals and provides the best anti-hair loss and hair growth effect; The fifteenth sub-module is used to combine the user's long-term physiological monitoring data, feedback data and emotional fluctuation information to continuously adjust the personalized plant formula, so that the formula is dynamically optimized as the user's physiological state changes, and provide personalized anti-hair loss and hair growth effects during the treatment process.

5. The device for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm according to claim 4, characterized in that: The sixth module specifically includes: The sixteenth submodule is used to simulate the effects of different plant formulas on the user's physiological state based on the user's personalized health profile and physiological state feature vector in the efficacy optimization virtual test platform, generate virtual test results, and obtain the adaptability score of each formula under different physiological states; The seventeenth submodule is used to provide real-time feedback and dynamic analysis of the user's physiological data through a virtual test platform, use a multi-dimensional optimization feedback function to perform a weighted evaluation of the adaptability scores of different plant formulas, and introduce an adaptability dynamic adjustment factor , generate the optimized recipe scoring matrix: ; in, Score the final optimized recipe at time t, is the dynamic influencing factor of plant component i, k is the exponential adjustment coefficient of the adaptability score, which controls the nonlinear influence of the adaptability score. is a dynamic weighting factor that affects the degree of influence of changes in physiological state on formulation optimization. is the change in the current physiological state feature vector, reflecting the user's physiological changes; The eighteenth submodule is used to simulate different ratios of each plant ingredient through a virtual test platform, and further optimize the plant formula using adaptive oscillation to maintain the best therapeutic effect under different physiological states: ; in, is the score of the plant formula adjusted for adaptive oscillation, is the oscillation amplitude factor, which regulates the intensity of the adaptive oscillation of the plant formula. is the time adjustment factor, controlling the dynamic response of the oscillation to the recipe optimization, is the initial phase of the oscillation, affecting the initial physiological feedback. It is the time integral of the physiological state characteristic vector, reflecting the cumulative effect of physiological changes; The nineteenth submodule is used to generate the final optimized plant formula according to the virtual test results and adaptive oscillation adjustment, and feed back the optimized formula data to the user's health record to perform dynamic optimization and personalized adjustment of the formula; The twentieth sub-module is used to continuously update and optimize the plant formula through a virtual test platform and real-time physiological data feedback, so that it can adapt to changes in the user's physiological state, thereby providing continuous and effective anti-hair loss and hair growth effects at different treatment stages.

6. The device for optimizing the formula of Chinese medicinal plant combination for preventing hair loss and promoting hair growth based on a neural network algorithm according to claim 1, characterized in that: The seventh module specifically includes: The twenty-first submodule is used to build a physiological-formula matching engine through real-time physiological data, long-term hair loss history, user personalized health records and virtual test data, and use a unified multi-dimensional adaptive evolutionary optimization algorithm to transform the user's physiological state into a multi-dimensional adaptive target space, so that the proportion of plant formula ingredients dynamically adapts to changes in the user's physiological state; The twenty-second submodule is used to fine-tune the plant formula after each optimization cycle by using the physiological-plant interaction adjustment framework to calculate the "interaction energy" between the physiological state and the plant ingredients, so that the formula adjustment can respond dynamically according to physiological fluctuations and optimize the adaptability of the plant ingredients in each cycle: ; in, For the adjusted plant formula, For the previous cycle of plant formula, is the fitness function, which represents the relationship between plant components and physiological states. is the optimal adaptation point of the i-th plant component, S(t) is the physiological state at the current moment, is the adjustment factor for plant ingredients, is the oscillation adjustment coefficient, is the physiological period oscillation function, is a small constant used to avoid the situation where the oscillation amplitude is zero; The twenty-third submodule is used to track the effect of the optimized plant formula based on the physiological cycle activation model and the long-term physiological feedback mechanism. By analyzing the change trend of the user's physiological cycle and the adaptability of the formula ingredients, the proportion of the plant formula ingredients is further dynamically adjusted, so that the plant formula can adapt to long-term physiological changes and demand fluctuations; The twenty-fourth sub-module is used to synchronize the optimized plant formula and the user's physiological status data to the user's health record, and conduct periodic analysis in combination with long-term physiological data. Based on long-term data and feedback, the plant formula is regularly re-evaluated and adjusted.

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