An intelligent collaborative optimization method and system for bioresonance energy chamber

Abstract

The present invention discloses an intelligent collaborative optimization method and system for a bioresonance energy cabin, which relates to the field of beauty optimization technology, including collecting user physiological data and extracting user physiological characteristics; constructing a physiological state evaluation model, predicting the user's physiological state score based on the user's physiological characteristics, and classifying the user's state; constructing an intervention plan library based on historical data, matching intervention plans from the intervention plan library according to the user's state classification results; executing the intervention plan, and analyzing the change trend of the user's physiological data during the execution process, and evaluating the intervention effect; dynamically adjusting the intervention intensity according to the evaluation results, and storing the feedback data in a database. The present invention not only solves the problem of insufficient accuracy of physiological state evaluation in the prior art, but also significantly improves the adaptability and dynamic adjustment ability of the intervention plan, and finally realizes the precision, intelligence and continuous optimization of the intervention effect.

Description

Technical Field

[0001] The present invention relates to the field of beauty optimization technology, and in particular to an intelligent collaborative optimization method and system for a bioresonance energy chamber. Background Art

[0002] In recent years, with the rapid growth of personalized health management and beauty optimization demand, non-invasive intervention devices based on biomedical engineering technology have received widespread attention. As an innovative means to improve health status by adjusting the human body's bio-electromagnetic frequency, bioresonance technology has been gradually applied to health management, beauty optimization and rehabilitation treatment. In particular, the bioresonance energy cabin can provide systemic energy regulation and health intervention by integrating a variety of bioresonance technologies. However, traditional bioresonance energy cabins mostly rely on fixed frequencies or preset modes for intervention, lacking the ability to respond to and adapt to the user's individual physiological state in real time. In addition, the existing bioresonance technology has limited data collection and analysis capabilities during the intervention process, and fails to fully utilize the user's multimodal physiological data (such as brain waves, heart rate variability, skin conductance, etc.) for dynamic optimization. This limitation has a certain impact on the accuracy and sustainability of the intervention effect, making it difficult to meet the user's growing demand for high precision in the field of health management and beauty restoration.

[0003] In response to the above shortcomings, intelligent technology and big data analysis have gradually been introduced into the field of bioresonance applications in recent years, attempting to improve the accuracy and adaptability of intervention effects through intelligent collaborative optimization of bioresonance energy cabins. However, existing intelligent attempts still face two major problems: First, the construction of physiological state assessment models fails to fully consider the complex interactive relationship between multiple physiological characteristics, and the accuracy of quantifying the user's health status is insufficient; second, the optimization and adjustment of intervention plans lack an intelligent collaborative mechanism based on real-time data and historical data, resulting in poor dynamic adaptability of the intervention process. The above problems directly affect the actual application effect of bioresonance energy cabins in the fields of health management and beauty optimization. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent collaborative optimization method for a bioresonance energy chamber to solve the problems of insufficient accuracy in physiological state assessment and lack of dynamic adaptability of intervention schemes in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an intelligent collaborative optimization method for a bioresonance energy chamber, which includes collecting user physiological data and extracting user physiological characteristics; constructing a physiological state evaluation model, predicting the user's physiological state score based on the user's physiological characteristics, and classifying the user's state; constructing an intervention plan library based on historical data, and matching intervention plans from the intervention plan library according to the user state classification results; executing the intervention plan, and analyzing the changing trend of the user's physiological data during the execution process, and evaluating the intervention effect; dynamically adjusting the intervention intensity according to the evaluation results, and storing the feedback data in a database.

[0008] As a preferred solution of the intelligent collaborative optimization method for the bioresonance energy chamber described in the present invention, the user physiological data includes physiological signals such as brain waves, heart rate variability, skin conductance, body temperature, blood oxygen saturation and blood flow distribution.

[0009] As a preferred solution of the intelligent collaborative optimization method for the bioresonance energy chamber of the present invention, the specific steps of extracting the user's physiological characteristics are as follows:

[0010] Extract the alpha wave proportion characteristics in EEG through time domain and frequency domain analysis;

[0011] The LF / HF ratio feature in heart rate variability was extracted by frequency domain analysis;

[0012] The change amplitude and frequency characteristics of skin conductance were extracted through time series analysis.

[0013] As a preferred solution of the intelligent collaborative optimization method for the bioresonance energy chamber of the present invention, the physiological state evaluation model is constructed to predict the user's physiological state score based on the user's physiological characteristics and classify the user's state. The specific steps are as follows:

[0014] By comprehensively considering the deviation of user characteristics and the interaction between user characteristics, using the weighted square of the deviation and the deviation penalty coefficient, quantifying the user state through the fractional scoring function, and constructing a physiological state evaluation model, the user's physiological characteristics are input into the physiological state evaluation model to evaluate the user's physiological state level. The expression is:

[0015] ;

[0016] in, is the user's current physiological status score, Indicates The degree to which physiological characteristics deviate from the healthy mean, Indicates The degree of deviation of each physiological characteristic from the healthy mean It represents the physiological characteristic value The fluctuation range, Indicates Normalization factor of physiological characteristic values, Indicates The deviation penalty coefficient of the physiological characteristic value is Indicates and The interaction weights of the physiological features;

[0017] Based on the statistical analysis of the beauty status in historical data, the excellent status threshold S1 and the problem status threshold S2 are defined;

[0018] when When ≥S1, the user's beauty status is classified as excellent;

[0019] When S2≤ When <S1, the user's beauty status is classified as normal status;

[0020] when In S2, the user's beauty status is classified as a problem status.

[0021] As a preferred solution of the intelligent collaborative optimization method for the bioresonance energy chamber of the present invention, the intervention solution library is constructed based on historical data, and the intervention solution is matched from the intervention solution library according to the user status classification result. The specific steps are as follows:

[0022] Collect historical data on skin care products, instrument care, and lifestyle adjustments for different user groups in various beauty states, then generate intervention plans based on historical users’ beauty states, physiological characteristics, and historical intervention effects, and divide intervention plans into primary, secondary, and tertiary intervention plans based on historical intervention effects;

[0023] Label the divided intervention plans and build an intervention plan library through structured storage;

[0024] When the user's beauty status is in an excellent state, a first-level intervention plan is matched from the intervention plans to maintain the current state;

[0025] When the user's beauty status is in a normal state, a secondary intervention plan is matched from the intervention plan library for mild repair;

[0026] When the user's beauty status is in a problematic state, a three-level intervention plan is matched from the intervention plan library for deep repair.

[0027] As a preferred solution of the intelligent collaborative optimization method for the bioresonance energy chamber of the present invention, the specific steps of executing the intervention plan, analyzing the changing trend of the user's physiological data during the execution process, and evaluating the intervention effect are as follows:

[0028] Execute the intervention plan matched in the intervention plan library, comprehensively consider the deviation degree of each user's physiological characteristics, nonlinear change characteristics and dynamic interaction effects between characteristics, and introduce the time decay mechanism to predict the change trend of the user's real-time physiological data. The expression is:

[0029] ;

[0030] in, Indicates the comprehensive change trend of the user's physiological data during the intervention process. represents the total number of physiological characteristics detected, Is the user's Physiological characteristics at the time Real-time measurement values, The intervention started at the Initial baseline values ​​of physiological characteristics, Is user The weight coefficient of each physiological feature, It is The nonlinear change index of physiological characteristics, It is The time decay coefficient of a physiological characteristic, It's time for intervention to begin. is the present moment, Is the user's Physiological characteristics at the time Real-time measurement values, The intervention started at the Initial baseline values ​​of physiological characteristics, is the base of natural logarithms;

[0031] According to the comprehensive change trend of the user's physiological data during the intervention process , to evaluate the intervention effect of the intervention program, the expression is:

[0032] ;

[0033] in, Is the intervention program at the moment intervention effect rating.

[0034] As a preferred solution of the intelligent collaborative optimization method for the bioresonance energy chamber of the present invention, the intervention intensity is dynamically adjusted according to the evaluation results, and the feedback data is stored in the database. The specific steps are as follows:

[0035] Define high-efficiency thresholds based on statistical analysis of intervention effects in historical data and inefficiency threshold ;

[0036] when ≥ When , the intervention effect is considered to be high and the current intervention intensity is maintained;

[0037] when ≤ < When the intervention effect is moderate, the intervention parameters are optimized and the intervention intensity is increased;

[0038] when < When the intervention effect is poor, a higher-level intervention plan is matched from the intervention plan library;

[0039] During the intervention process, users are scored for the intervention effect , user real-time physiological data, user status classification results and intervention parameters are encapsulated into a structured data packet, and the structured data packet is uploaded to the database through the interface program.

[0040] In a second aspect, the present invention provides an intelligent collaborative optimization system for a bioresonance energy cabin, comprising a data acquisition module, a state classification module, an intervention plan matching module, an intervention plan execution module and an intervention intensity adjustment module; the data acquisition module is used to collect user physiological data and extract user physiological characteristics; the state classification module is used to build a physiological state evaluation model, predict the user's physiological state score based on the user's physiological characteristics, and classify the user's state; the intervention plan matching module is used to build an intervention plan library based on historical data, and match the intervention plan from the intervention plan library according to the user state classification result; the intervention plan execution module is used to execute the intervention plan, and analyze the changing trend of the user's physiological data during the execution process, and evaluate the intervention effect; the intervention intensity adjustment module is used to dynamically adjust the intervention intensity according to the evaluation results, and store the feedback data in a database.

[0041] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the intelligent collaborative optimization method for a bioresonance energy chamber as described in the first aspect of the present invention is implemented.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the intelligent collaborative optimization method for a bioresonance energy chamber as described in the first aspect of the present invention is implemented.

[0043] The beneficial effects of the present invention are as follows: by collecting user physiological data, extracting multimodal physiological characteristics, constructing a physiological state assessment model, accurately predicting the user's physiological state score and classifying it, a scientific basis is provided for matching personalized intervention plans; in the process of implementing the intervention plan, by real-time analysis of the changing trend of the user's physiological data and the intervention effect score, the intervention intensity is dynamically adjusted, and the user's feedback data is stored in the database, realizing the adaptive optimization of the intervention process and data closed-loop management. This method not only solves the problem of insufficient accuracy of physiological state assessment in the prior art, but also significantly improves the adaptability and dynamic adjustment ability of the intervention plan, and finally realizes the precision, intelligence and continuous optimization of the intervention effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 This is a flow chart of the intelligent collaborative optimization method for the bioresonance energy chamber in Example 1.

[0046] Figure 2 Schematic diagram of the intelligent collaborative optimization system for the bioresonance energy chamber in Example 1. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0050] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides an intelligent collaborative optimization method for a bioresonance energy chamber, comprising the following steps:

[0051] S1: Collect user physiological data and extract user physiological characteristics.

[0052] S1.1: User physiological data includes physiological signals such as brain waves, heart rate variability, skin conductance, body temperature, blood oxygen saturation and blood flow distribution.

[0053] Furthermore, user physiological data comes from the multimodal collection of physiological signals such as brain waves, heart rate variability, skin conductance, body temperature, blood oxygen saturation and blood flow distribution. These data are used to comprehensively reflect the user's physiological state characteristics and provide a scientific basis for accurate assessment and dynamic intervention.

[0054] S1.2: Extract the alpha wave proportion characteristics in EEG through time domain and frequency domain analysis;

[0055] For example, through frequency domain analysis of EEG signals, the proportion of signal power in the 8-13 Hz frequency band to the overall power is extracted, and the alpha wave proportion characteristics of the user in a relaxed state are calculated.

[0056] S1.3: Extract LF / HF ratio features in heart rate variability through frequency domain analysis;

[0057] For example, through frequency domain analysis of ECG signals, the low-frequency (LF, 0.04-0.15 Hz) and high-frequency (HF, 0.15-0.4 Hz) components are separated and their power ratio is calculated to evaluate the balance state of the autonomic nervous system.

[0058] S1.4: Extract the change amplitude and frequency characteristics of skin conductance through time series analysis.

[0059] For example, by analyzing the time series of skin conductance signals, the maximum change amplitude and fluctuation frequency of skin conductance after stimulation are extracted to reflect the user's emotional stress response level.

[0060] S2: Build a physiological state assessment model, predict the user's physiological state score based on the user's physiological characteristics, and classify the user's state.

[0061] S2.1: By comprehensively considering the deviation of user characteristics and the interaction between user characteristics, using the weighted square of the deviation and the deviation penalty coefficient, quantifying the user state through the fractional scoring function, constructing a physiological state evaluation model, inputting the user's physiological characteristics into the physiological state evaluation model, and evaluating the user's physiological state level, the expression is:

[0062] ;

[0063] in, is the user's current physiological status score, Indicates The degree to which physiological characteristics deviate from the healthy mean, Indicates The degree of deviation of each physiological characteristic from the healthy mean It represents the physiological characteristic value The fluctuation range, Indicates Normalization factor of physiological characteristic values, Indicates The deviation penalty coefficient of the physiological characteristic value is Indicates and The interaction weights of the physiological features;

[0064] It should be noted that the fractional scoring function measures the abnormality of a single feature by the weighted square of the deviation and the deviation penalty coefficient, and introduces the interaction weights between features to evaluate the synergy or conflict between different physiological features. Finally, the above results are mapped into an easy-to-understand physiological state score in the form of exponential decay. This method can comprehensively reflect the overall health level and dynamic change trend of the user's physiological state, and provide a scientific basis for subsequent classification and intervention.

[0065] The healthy mean is a reference value of a specific physiological characteristic in a healthy state based on statistics of large sample data. It is usually calculated by the mean or median of the corresponding physiological characteristics of healthy people and adjusted in combination with individual characteristics (such as age, gender, activity level, etc.). For example, the healthy mean of heart rate variability may be derived from the average range of the LF / HF ratio of the normal population, while the healthy mean of the alpha wave ratio of the EEG is based on the statistical characteristics of the state of psychological relaxation. These healthy means are used as baseline values ​​to calculate the degree of deviation of the user's current physiological characteristics, thereby assessing their health level.

[0066] S2.2: Based on the statistical analysis of the beauty status in historical data, define the excellent status threshold S1 and the problem status threshold S2;

[0067] when When ≥S1, the user's beauty status is classified as excellent;

[0068] For example, when the user's beauty status score is 85 points, which is higher than the excellent status threshold S1 (80 points), it indicates that the user's current skin condition is good and only needs to maintain the existing care plan.

[0069] When S2≤ When <S1, the user's beauty status is classified as normal status;

[0070] For example, when the user's beauty status score is 65 points, which is between the problem status threshold S2 (50 points) and the excellent status threshold S1 (80 points), it means that the user's skin condition is average and requires mild repair intervention.

[0071] when In S2, the user's beauty status is classified as a problem status.

[0072] Care and lifestyle adjustments, then generate intervention plans based on historical user beauty status, physiological characteristics, and historical intervention effects, and divide the intervention plans into primary intervention plans, secondary intervention plans, and tertiary intervention plans based on historical intervention effects;

[0073] It should be noted that by collecting the skin care products, instrument care plans and lifestyle adjustment methods used by different user groups in various beauty states (such as excellent state, normal state, and problem state) in historical data, combined with the user's physiological characteristics and the beauty effects after intervention, data analysis and classification are performed to generate personalized intervention plans. Subsequently, these plans are graded according to the quality of the intervention effects in the historical data: plans with significant effects are classified as first-level intervention plans for maintaining excellent states; plans with better effects are classified as second-level intervention plans for repairing normal states; plans with the best intervention effects for problem states are classified as third-level intervention plans for deep repair.

[0074] For example, historical data shows that users whose skin is insufficiently hydrated but whose condition scores are between normal levels have good results using a hydrating mask (skin care product) and a medium-frequency radio frequency device for care (instrument solution). This combination is classified as a secondary intervention solution, which is used to mildly repair and improve skin condition.

[0075] S3.2: Label the divided intervention plans and build an intervention plan library through structured storage;

[0076] When the user's beauty status is in an excellent state, a first-level intervention plan is matched from the intervention plans to maintain the current state;

[0077] When the user's beauty status is in a normal state, a secondary intervention plan is matched from the intervention plan library for mild repair;

[0078] When the user's beauty status is in a problematic state, a three-level intervention plan is matched from the intervention plan library for deep repair.

[0079] It should be noted that by labeling the divided intervention plans and using structured storage to build an intervention plan library, standardization and efficient retrieval of intervention plans are achieved. After the user's beauty status is evaluated, different levels of intervention plans are matched according to the status: the excellent status is matched with the first-level plan to maintain the status quo, the normal status is matched with the second-level plan for light repair, and the problem status is matched with the third-level plan for deep repair. This classification matching mechanism ensures the accuracy and adaptability of the intervention plan, effectively avoids the "one-size-fits-all" intervention method, and at the same time improves the efficiency and effect of the intervention, providing users with a personalized and dynamic beauty status management solution.

[0080] S4: Execute the intervention plan, analyze the changing trend of the user's physiological data during the execution process, and evaluate the intervention effect.

[0081] S4.1: Execute the intervention plan matched in the intervention plan library, comprehensively consider the deviation degree, nonlinear change characteristics and dynamic interaction effects of each user's physiological characteristics, and introduce the time decay mechanism to predict the change trend of the user's real-time physiological data. The expression is:

[0082] ;

[0083] in, Indicates the comprehensive change trend of the user's physiological data during the intervention process. represents the total number of physiological characteristics detected, Is the user's Physiological characteristics at the time Real-time measurement values, The intervention started at the Initial baseline values ​​of physiological characteristics, Is user The weight coefficient of each physiological feature, It is The nonlinear change index of physiological characteristics, It is The time decay coefficient of a physiological characteristic, It's time for intervention to begin. is the present moment, Is the user's Physiological characteristics at the time Real-time measurement values, The intervention started at the Initial baseline values ​​of physiological characteristics, is the base of natural logarithms;

[0084] It should be noted that this expression calculates the overall change in the user's physiological state by comprehensively analyzing the change trend of the user's real-time physiological data during the intervention process, combining the initial baseline value, real-time measurement value, and nonlinear characteristics of each physiological characteristic. The expression takes into account the importance weights of different physiological characteristics and the dynamic interaction effects between them, and introduces a time decay mechanism to give more recent data a greater influence, thereby accurately predicting the dynamic change trend of the user's physiological state during the intervention process. This method can fully reflect the user's adaptability and effectiveness during the intervention process, and provide a scientific basis for adjusting the intensity of the intervention.

[0085] S4.2: Based on the comprehensive change trend of the user's physiological data during the intervention process , to evaluate the intervention effect of the intervention program, the expression is:

[0086] ;

[0087] in, Is the intervention program at the moment intervention effect rating.

[0088] It should be noted that the real-time effect score of the current intervention plan is evaluated through the comprehensive change trend of the user's physiological data. The intervention effect score increases as the degree of deviation of the user's physiological state decreases, reflecting the actual effectiveness of the intervention on the user's state adjustment. By combining the weight and fluctuation range of physiological characteristics, the score can dynamically quantify the effectiveness of the intervention plan, providing a scientific real-time reference for subsequent intervention intensity adjustment and plan optimization.

[0089] S5: Dynamically adjust the intervention intensity based on the evaluation results and store the feedback data in the database.

[0090] S5.1: Define high-efficacy thresholds based on statistical analysis of intervention effects in historical data and inefficiency threshold ;

[0091] Furthermore, we first collect a large amount of real-time scoring data during the user intervention process, and analyze its distribution and its correlation with the improvement of physiological status. By performing segmented statistics on the distribution of scoring data, we define intervention plans with high scores (e.g., 90 points and above) as efficient interventions, and set the efficient threshold accordingly. ; Define interventions with low scores (e.g. 50 points and below) as ineffective interventions, and set ineffectiveness thresholds accordingly .

[0092] when ≥ When , the intervention effect is considered to be high and the current intervention intensity is maintained;

[0093] For example, when the user's intervention effect score is 92 points (higher than G1=90), it indicates that the current combination of moisturizing mask and medium-frequency radio frequency device care is effective, and the skin moisture and elasticity indicators have reached excellent levels. Therefore, there is no need to adjust the plan, and care can continue at the current frequency and intensity.

[0094] when ≤ < When the intervention effect is moderate, the intervention parameters are optimized and the intervention intensity is increased;

[0095] For example, when the user's intervention effect score is 75 points (between G2=50 and G1=90), although the skin condition has improved, the effect is not ideal. Therefore, the frequency of care can be increased (such as increasing from once a week to twice a week) or the product dosage can be increased (such as increasing the amount of essence used from 1 pump to 2 pumps) to further enhance the intervention effect.

[0096] when < When the intervention effect is poor, a higher-level intervention plan is matched from the intervention plan library;

[0097] For example, when the user's intervention effect score is 40 points (lower than G2=50), it indicates that the basic hydrating care currently used is poor and cannot meet the needs of deep repair. Therefore, it is necessary to match a higher-level intervention plan from the intervention plan library, such as introducing a high-intensity radio frequency beauty device combined with a medical-grade repair essence for deep repair care to significantly improve the skin condition.

[0098] S5.2: During the intervention process, rate the user's intervention effect , user real-time physiological data, user status classification results and intervention parameters are encapsulated into a structured data packet, and the structured data packet is uploaded to the database through the interface program.

[0099] It should be noted that by encapsulating the user's intervention effect score, real-time physiological data, state classification results and intervention parameters into a structured data package and uploading it to the database through an interface program, standardized recording and efficient storage of intervention process data are achieved. This method not only ensures the integrity and consistency of the data, but also provides reliable data support for subsequent data mining, intervention optimization and personalized program recommendation, which helps to build sustainable intelligent collaborative optimization, thereby improving the overall intervention effect and user experience.

[0100] The present embodiment also provides an intelligent collaborative optimization system for a bioresonance energy chamber, comprising: a data acquisition module, a state classification module, an intervention plan matching module, an intervention plan execution module and an intervention intensity adjustment module; the data acquisition module is used to collect user physiological data and extract user physiological characteristics; the state classification module is used to build a physiological state evaluation model, predict the user's physiological state score based on the user's physiological characteristics, and classify the user's state; the intervention plan matching module is used to build an intervention plan library based on historical data, and match the intervention plan from the intervention plan library according to the user state classification result; the intervention plan execution module is used to execute the intervention plan, and during the execution process, analyze the change trend of the user's physiological data and evaluate the intervention effect; the intervention intensity adjustment module is used to dynamically adjust the intervention intensity according to the evaluation results, and store the feedback data in a database.

[0101] This embodiment also provides a computer device, which is suitable for the case of an intelligent collaborative optimization method for a bioresonance energy chamber, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent collaborative optimization method for a bioresonance energy chamber as proposed in the above embodiment.

[0102] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0103] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent collaborative optimization method for a bioresonance energy chamber proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0104] In summary, the present invention provides a scientific basis for matching personalized intervention plans by: collecting user physiological data, extracting multimodal physiological characteristics, constructing a physiological state assessment model, accurately predicting the user's physiological state score and classifying it; in the process of implementing the intervention plan, by real-time analysis of the changing trend of the user's physiological data and the intervention effect score, dynamically adjusting the intervention intensity, and storing the user's feedback data in the database, the adaptive optimization of the intervention process and data closed-loop management are realized. This method not only solves the problem of insufficient accuracy of physiological state assessment in the prior art, but also significantly improves the adaptability and dynamic adjustment ability of the intervention plan, and finally realizes the precision, intelligence and continuous optimization of the intervention effect.

[0105] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. An intelligent collaborative optimization method for a bioresonance energy chamber, characterized in that: include, Collect user physiological data and extract user physiological characteristics; Build a physiological state assessment model to predict the user's physiological state score based on the user's physiological characteristics and classify the user's state; Build an intervention plan library based on historical data, and match intervention plans from the intervention plan library according to the user status classification results; Execute the intervention plan, analyze the changing trend of the user's physiological data during the execution process, and evaluate the intervention effect; Dynamically adjust intervention intensity based on evaluation results and store feedback data in a database; The physiological state evaluation model is constructed to predict the user's physiological state score based on the user's physiological characteristics and classify the user's state. The specific steps are as follows: By comprehensively considering the deviation of user characteristics and the interaction between user characteristics, using the weighted square of the deviation and the deviation penalty coefficient, quantifying the user state through the fractional scoring function, and constructing a physiological state evaluation model, the user's physiological characteristics are input into the physiological state evaluation model to evaluate the user's physiological state level. The expression is: ； in, is the user's current physiological status score, Indicates The degree of deviation of each physiological characteristic from the healthy mean Indicates The degree of deviation of each physiological characteristic from the healthy mean It represents the physiological characteristic value The fluctuation range of Indicates Normalization factor of physiological characteristic values, Indicates The deviation penalty coefficient of the physiological characteristic value is Indicates and The interaction weights of the physiological features; Based on the statistical analysis of the beauty status in historical data, the excellent status threshold S1 and the problem status threshold S2 are defined; when When ≥S1, the user's beauty status is classified as excellent; When S2≤ When <S1, the user's beauty status is classified as normal status; when In S2, the user's beauty status is classified as a problem status; The intervention plan library is constructed based on historical data, and the intervention plan is matched from the intervention plan library according to the user status classification result. The specific steps are as follows: Collect historical data on skin care products, instrument care, and lifestyle adjustments for different user groups in various beauty states, then generate intervention plans based on historical users’ beauty states, physiological characteristics, and historical intervention effects, and divide intervention plans into primary, secondary, and tertiary intervention plans based on historical intervention effects; Label the divided intervention plans and build an intervention plan library through structured storage; When the user's beauty status is in an excellent state, a first-level intervention plan is matched from the intervention plans to maintain the current state; When the user's beauty status is in a normal state, a secondary intervention plan is matched from the intervention plan library for mild repair; When the user's beauty status is in a problematic state, a three-level intervention plan is matched from the intervention plan library for deep repair; The specific steps of executing the intervention plan, analyzing the changing trend of the user's physiological data during the execution process, and evaluating the intervention effect are as follows: Execute the intervention plan matched in the intervention plan library, comprehensively consider the deviation degree of each user's physiological characteristics, nonlinear change characteristics and dynamic interaction effects between characteristics, and introduce the time decay mechanism to predict the change trend of the user's real-time physiological data. The expression is: ； in, Indicates the comprehensive change trend of the user's physiological data during the intervention process. represents the total number of physiological characteristics detected, Is the user's Physiological characteristics at the time Real-time measurement values, The intervention started at the Initial baseline values ​​of physiological characteristics, Is user The weight coefficient of each physiological feature, It is The nonlinear change index of physiological characteristics, It is The time decay coefficient of a physiological characteristic, It's time for intervention to begin. is the present moment, Is the user's Physiological characteristics at the time Real-time measurement values, The intervention started at the Initial baseline values ​​of physiological characteristics, is the base of natural logarithms; According to the comprehensive change trend of the user's physiological data during the intervention process , to evaluate the intervention effect of the intervention program, the expression is: ； in, Is the intervention program at the moment intervention effect rating.

2. The intelligent collaborative optimization method for a bioresonance energy chamber according to claim 1, characterized in that: The user's physiological data includes physiological signals such as brain waves, heart rate variability, skin conductance, body temperature, blood oxygen saturation and blood flow distribution.

3. The intelligent collaborative optimization method for a bioresonance energy chamber according to claim 2, characterized in that: The specific steps of extracting the user's physiological characteristics are as follows: Extract the alpha wave proportion characteristics in EEG through time domain and frequency domain analysis; The LF / HF ratio feature in heart rate variability was extracted by frequency domain analysis; The change amplitude and frequency characteristics of skin conductance were extracted through time series analysis.

4. The intelligent collaborative optimization method for a bioresonance energy chamber according to claim 3, characterized in that: The specific steps of dynamically adjusting the intervention intensity according to the evaluation results and storing the feedback data in the database are as follows: Define high-efficiency thresholds based on statistical analysis of intervention effects in historical data and inefficiency threshold ; when ≥ When , the intervention effect is considered to be high and the current intervention intensity is maintained; when ≤ < When the intervention effect is moderate, the intervention parameters are optimized and the intervention intensity is increased; when < When the intervention effect is poor, a higher-level intervention plan is matched from the intervention plan library; During the intervention process, users are scored for the intervention effect , user real-time physiological data, user status classification results and intervention parameters are encapsulated into a structured data packet, and the structured data packet is uploaded to the database through the interface program.

5. An intelligent collaborative optimization system for a bioresonance energy chamber, based on the intelligent collaborative optimization method for a bioresonance energy chamber according to any one of claims 1 to 4, characterized in that: It includes a data collection module, a state classification module, an intervention plan matching module, an intervention plan execution module and an intervention intensity adjustment module; A data collection module is used to collect user physiological data and extract user physiological characteristics; The state classification module is used to build a physiological state evaluation model, predict the user's physiological state score based on the user's physiological characteristics, and classify the user's state; An intervention plan matching module is used to build an intervention plan library based on historical data and match intervention plans from the intervention plan library according to the user status classification results; The intervention plan execution module is used to execute the intervention plan, analyze the changing trend of the user's physiological data during the execution process, and evaluate the intervention effect; The intervention intensity adjustment module is used to dynamically adjust the intervention intensity according to the evaluation results and store the feedback data in the database.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent collaborative optimization method for a bioresonance energy chamber according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent collaborative optimization method for a bioresonance energy chamber according to any one of claims 1 to 4 are implemented.

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