A comb guide based personalized hair care regimen pushing system

Abstract

The application relates to the technical field of intelligent service recommendation and risk management, in particular to a personalized hairdressing scheme pushing system based on a comb guide instrument, which comprises a data acquisition unit used for acquiring multi-dimensional basic data of hair quality of a user and historical service data of a hair stylist; an image construction unit used for generating a skill index; a risk assessment unit used for generating a scheme physical risk index and an AI trust deficit index; a decision pushing unit used for generating an optimal effect pushing signal or a strategic weakening signal; and a closed-loop correction unit used for correcting the AI trust deficit index and dynamically adjusting a maximum physical risk threshold and a trust deficit assessment boundary based on collected final service data after service completion; the application can accurately predict and avoid potential service risks, and significantly improves the success rate and reliability of personalized scheme recommendation.

Description

Technical Field

[0001] This invention relates to the field of intelligent service recommendation and risk management technology, specifically a personalized hair styling solution push system based on a hair combing device. Background Technology

[0002] In the current hair service industry, recommending personalized solutions to users is the core link to improve service quality and customer satisfaction. Traditional solution recommendations mainly rely on the personal experience of the hairstylist for subjective judgment. When faced with difficult hairstyles, this method is difficult to systematically balance the relationship between the user's hair condition, the hairstylist's skill level, and the inherent complexity of the hairstyle, resulting in a higher risk of service failure.

[0003] In existing technologies, intelligent recommendation systems mostly focus on aesthetic matching, but generally lack quantitative assessment models for the risks of the entire service process. These systems fail to comprehensively model key factors such as hair health, technician's professional skills, and the physical difficulty of the solution, making it impossible to accurately predict the probability of potential service failure. More importantly, when a recommended solution fails due to insufficient consideration of objective limitations, it leads to a decrease in the hairstylist's trust in the system, i.e., an AI trust deficit. This constitutes a fundamental obstacle to the practical application of intelligent tools in collaboration with humans. Therefore, how to build a personalized hair styling solution recommendation system that can perform multi-dimensional quantitative assessment and risk prediction of hair services, and proactively manage and repair the AI ​​trust deficit through a closed-loop correction mechanism, thereby improving the success rate of high-difficulty solutions and the efficiency of human-machine collaboration, is a core technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a personalized hair styling solution delivery system based on a combing device. Specifically, the technical solution of this invention includes:

[0005] The data acquisition unit is used to collect multi-dimensional basic data on the user's hair quality and historical service data from the hairstylist;

[0006] The profile building unit is used to receive multi-dimensional basic data to generate a comprehensive hair health score, and to receive historical service data to generate a skill index;

[0007] The risk assessment unit is used to generate a physical risk index for the solution based on the overall hair health and skill index generated by the profile building unit, combined with the preset inherent physical difficulty coefficient of the hairstyle; and to generate an AI trust deficit index based on the collected service process data.

[0008] The decision push unit is used to generate the optimal effect push signal or strategic weakness signal based on the comparison results of the solution physical risk index with the preset maximum physical risk threshold and the comparison results of the AI ​​trust deficit index with the preset trust deficit assessment boundary.

[0009] The closed-loop correction unit is used to correct the AI ​​trust deficit index based on the collected final service data after the service is completed, and to dynamically adjust the maximum physical risk threshold and the trust deficit assessment boundary.

[0010] Optionally, the process of generating overall hair health score using the profile building unit is as follows:

[0011] Hair density, elasticity coefficient, and pigmentation stability were obtained as measured values ​​for hair quality.

[0012] Based on the measured hair quality values ​​and the preset reference baseline values, normalized hair quality parameters are determined;

[0013] Based on normalized hair quality parameters and preset weighting coefficients, a weighted summation calculation is performed to generate the overall hair health score.

[0014] Optionally, the process of generating skill indices from profile building units is as follows:

[0015] Acquire the success rate, average processing time, and user ratings for specific hairstyle operations as historical service data;

[0016] The time efficiency item is determined based on the average time and the preset maximum reference time.

[0017] Based on user ratings and the preset full score of the rating system, the rating normalization item is determined;

[0018] The skill index is generated by combining the success rate of operation, time efficiency, and score normalization, and performing a weighted summation operation based on preset weight coefficients.

[0019] Optionally, the process for generating the physical risk index of the scheme by the risk assessment unit is as follows:

[0020] The physical risk index of the solution is obtained by dividing the preset physical difficulty coefficient of the hairstyle by the product of the overall hair health and skill index generated by the portrait construction unit.

[0021] Optionally, the process by which the risk assessment unit generates the AI ​​trust deficit index is as follows:

[0022] Obtain the extent of solution modifications, solution execution timeout rate, and stress feedback values ​​during the evaluation period;

[0023] Based on the extent of solution modification, solution execution timeout rate, stress feedback value, and preset weights, a weighted average is performed to generate the AI ​​trust deficit index.

[0024] Optionally, the signal generation logic of the decision push unit is as follows:

[0025] When the AI ​​trust deficit index is less than the trust deficit assessment boundary and the solution physical risk index is not greater than the maximum physical risk threshold, the optimal effect push signal is generated.

[0026] A strategic weakening signal is generated when the AI ​​trust deficit index is greater than or equal to the trust deficit assessment boundary, or when the solution physical risk index is greater than the maximum physical risk threshold.

[0027] Optionally, when a strategic weakening signal is generated, the decision push unit initiates a constraint optimization algorithm. Under the constraints that the physical risk of the alternative solution is less than the maximum physical risk threshold and the hairstylist's skill index for the alternative solution is greater than the preset minimum skill index requirement, the algorithm finds the suboptimal solution that is closest to the style feature vector of the original solution and pushes it.

[0028] Optionally, the closed-loop correction unit corrects the AI ​​trust deficit index as follows:

[0029] Get user ratings for this service;

[0030] Calculate the difference between the user rating and the preset average user rating;

[0031] The score difference is normalized to the full score of the preset scoring system to obtain the normalized satisfaction.

[0032] The normalized satisfaction rate is multiplied by the preset learning rate to generate a trust compensation factor.

[0033] The revised AI trust deficit index is obtained by subtracting the trust compensation factor from the original AI trust deficit index.

[0034] Optionally, the closed-loop correction unit is also used to gradually adjust the maximum physical risk threshold and the trust deficit assessment boundary based on the feedback of preset core operating indicators and reinforcement learning algorithms.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. This system transforms the traditional model that relies on personal experience into a data-driven scientific decision-making model by quantitatively assessing the user's hair quality, the hairstylist's skills, and the difficulty of the hairstyle from multiple dimensions. It accurately predicts and avoids potential service risks, and significantly improves the success rate and reliability of personalized solution recommendations.

[0037] 2. This system innovatively introduces and quantifies the hairstylist's trust in the system. By assessing objective physical risks and subjective trust risks in parallel, it achieves a precise two-dimensional profile of hair service risks, enabling earlier and more comprehensive identification of potential problems that may lead to service failure or poor human-machine collaboration.

[0038] 3. Through a strategically weak intelligent decision-making mechanism, when the system identifies a high risk, it can proactively provide a suboptimal solution that is safe, competent for the hairstylist, and preserves the user's original intention to the greatest extent. This realizes the transformation from rigid recommendation to intelligent negotiation and effectively resolves the human-machine conflict.

[0039] 4. This system constructs a closed-loop correction and self-optimization mechanism from service results to system parameters. It can not only dynamically repair or adjust the trust relationship with the hairstylist based on the user rating of a single service, but also continuously adjust the system's risk strategy based on the salon's long-term operational indicators using reinforcement learning algorithms, ensuring that the system can continuously evolve and remain consistent with the salon's overall business goals. Attached Figure Description

[0040] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0041] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0043] Example 1:

[0044] A personalized hair styling solution delivery system based on a hair combing device includes:

[0045] The data acquisition unit is used to collect multi-dimensional basic data on the user's hair quality and historical service data from the hairstylist;

[0046] The profile building unit is used to receive multi-dimensional basic data to generate a comprehensive hair health score, and to receive historical service data to generate a skill index;

[0047] The risk assessment unit is used to generate a physical risk index for the solution based on the overall hair health and skill index generated by the profile building unit, combined with the preset inherent physical difficulty coefficient of the hairstyle; and to generate an AI trust deficit index based on the collected service process data.

[0048] The decision push unit is used to generate the optimal effect push signal or strategic weakness signal based on the comparison results of the solution physical risk index with the preset maximum physical risk threshold and the comparison results of the AI ​​trust deficit index with the preset trust deficit assessment boundary.

[0049] The closed-loop correction unit is used to correct the AI ​​trust deficit index based on the collected final service data after the service is completed, and to dynamically adjust the maximum physical risk threshold and the trust deficit assessment boundary.

[0050] This invention provides a personalized hair styling solution delivery system based on a hair combing device;

[0051] This embodiment aims to address the core technical problem in existing hairdressing services where stylists rely solely on personal experience to recommend solutions, making it difficult to balance feasibility, safety, and user satisfaction. This is especially true when dealing with complex hairstyles, where service failures are easily caused by hair condition or insufficient stylist skills, leading to a trust deficit in the AI ​​recommendation system among stylists. This system achieves quantitative management and intelligent optimization of the entire hairdressing service process by constructing a complete technical closed loop, from data collection, user and stylist profiling, risk assessment, intelligent decision-making, to closed-loop correction.

[0052] The system includes: a data acquisition unit, a profile building unit, a risk assessment unit, a decision delivery unit, and a closed-loop correction unit;

[0053] The data acquisition unit aims to provide accurate and multi-dimensional basic inputs for all subsequent analysis, evaluation, and decision-making, forming the perception layer of the entire system. In this embodiment, this unit is implemented through a sensor module integrated into the hair comb and an interface connected to the backend database. Specifically, the high-frequency ultrasonic sensor built into the hair comb is used to acquire hair density parameters. This refers to the number of hair follicles per unit area, serving as the basis for assessing hair volume. The data is obtained through real-time scalp scanning by a sensor; simultaneously, this sensor is also used to assess hair follicle health. This refers to the physiological activity state of hair follicles, which is used to determine the stability of hair roots and is derived from the analysis of blood flow signals around the hair follicles; the miniature tension gauge built into the comb is used to measure the elasticity coefficient of hair in real time. This tensile tester measures the ability of hair to deform and recover under external force. Its purpose is to assess the resilience and plasticity of hair. It is derived by stretching a single hair strand and recording the force-displacement curve. This tensile tester is also used to obtain information about hair resilience. This refers to the hair's ability to resist breakage; the built-in spectrometer in the comb is used to determine the current color of the hair. and pigmentation stability This refers to the uniformity of pigment particle distribution and antioxidant capacity within the hair shaft. Its purpose is to assess the potential risks of chemical treatments such as hair dyeing and perming. It is derived from the analysis of spectral reflectance data from hair samples. Simultaneously, the data acquisition unit also accesses the salon management system's historical database to collect historical service data from specific hairstylists, including the success rate of specific hairstyles. It refers to the percentage of times a particular hairstyle has achieved the desired effect in past services; average time spent This refers to the average time to complete the hairstyle; and user ratings. This refers to the customer's satisfaction rating for the service; these raw sensor data and historical service data together form the basis for accurate calculations in subsequent units.

[0054] The core purpose of the profile building unit is to transform raw, discrete, multi-dimensional data into structured labels that intuitively reflect core capabilities, namely, user hair quality profiles and hairstylist skill profiles. In this embodiment, this unit receives multi-dimensional basic data provided by the data acquisition unit, namely hair density, elasticity coefficient and pigmentation stability, hair follicle health and hair resilience. Through a weighted summation model, it generates a comprehensive dimensionless index, namely, overall hair health. This metric is used to accurately quantify the overall condition of a user's hair and their tolerance for various hair styling procedures. Simultaneously, this unit also receives historical service data from hairstylists, including success rates for specific hairstyles, average time taken, and user ratings. This data is then used to generate a skill index through another normalized weighted model. This index is specifically used to quantify a hairstylist's mastery and skill level in a particular hairstyle.

[0055] The risk assessment unit is designed to mathematically and measurably assess abstract service risks, thereby providing a scientific basis for decision-making. In this embodiment, the unit is based on the overall hair health score generated by the profile construction unit. and skill index And combined with a preset hairstyle's inherent physical difficulty coefficient Generate the physical risk index of the solution The inherent physical difficulty of the hairstyle This refers to a numerical score generated by a committee of senior industry experts who comprehensively evaluate and score each hairstyle based on factors such as operational complexity, the strength of required chemicals, and potential damage to hair. Its purpose is to quantify the inherent challenge of the hairstyle, and its source is the system's built-in expert knowledge base; (Physical Risk Index of the Solution) This unit integrates three factors—hair quality, skill, and hairstyle complexity—to predict the success probability of a specific solution under current subjective and objective conditions. Furthermore, based on collected service process data, such as the extent of the hairstylist's modifications to the AI-recommended solution, the timeout rate of solution execution, and the hairstylist's stress feedback value obtained through emotion recognition algorithms, it generates an innovative evaluation index: the AI ​​Trust Deficit Index. The index aims to quantify hairstylists' level of trust and acceptance of the intelligent system's recommendations;

[0056] The decision-making push unit aims to dynamically select the optimal solution push strategy based on risk assessment results, thereby maximizing both user satisfaction and system credibility. In this embodiment, the unit will use the physical risk index of the solution... With a preset maximum physical risk threshold Compare; Maximum physical risk threshold This refers to the highest level of risk the system can tolerate. Its purpose is to ensure the bottom line of security for the push notification scheme. It is derived from the successful historical cases of Salon. The values ​​are statistically analyzed, for example, by setting the value at the 95th percentile of its statistical distribution; simultaneously, this unit will include the AI ​​trust deficit index. Compared with the pre-set trust deficit assessment boundary Compare and contrast; assess the boundaries of the trust deficit. This refers to a critical value; exceeding this value indicates a potential problem in the collaboration between the hairstylist and the system. Its function is to act as a trigger for intervention strategies. It is determined through statistical analysis of historical salon operation data, identifying trust deficit indices that significantly reduce service quality. Based on these two comparisons, when both physical risk and trust deficit are within a controllable range, the system generates an optimal effect push signal, pushing the original high-yield solution; conversely, it generates a strategic weakening signal, initiating the generation and push of alternative solutions.

[0057] The core purpose of the closed-loop correction unit is to enable the system to have self-learning and adaptive optimization capabilities, ensuring its long-term effectiveness and accuracy. In this embodiment, after each hair styling service is completed, this unit collects data for that service, especially user ratings. Based on the difference between this rating and the historical average rating, a trust compensation factor is calculated. This is used to correct the AI ​​trust deficit index; the innovation of this design lies in quantifying a successful service experience as a restoration of human-machine trust, forming a positive feedback loop; in addition, based on long-term feedback from the salon's core operational indicators such as customer negative review rates, this unit uses reinforcement learning algorithms to dynamically and gradually adjust the maximum physical risk threshold. Boundaries of Trust Deficit Assessment This ensures that the system's risk appetite remains dynamically aligned with the salon's overall business objectives;

[0058] Through the collaborative work of the above five units, this system has constructed a complete intelligent hair styling solution recommendation and risk management system. Compared with existing technologies that rely solely on the personal experience of hairstylists, this system can accurately predict and avoid potential service risks by quantitatively assessing user hair quality, hairstylist skills, and the difficulty of the hairstyle itself from multiple dimensions. More importantly, by uniquely introducing and quantifying the concept of AI trust deficit, and by proactively managing it through strategic vulnerability and closed-loop correction mechanisms, it effectively solves the problem of intelligent tools collaborating with human agents in practical applications. This significantly improves the success rate of complex hairstyle solutions, user satisfaction, and hairstylists' long-term trust and reliance on the system, thereby enhancing the overall service quality and operational efficiency of hair salons.

[0059] Example 2:

[0060] The process by which the profile building unit generates the overall hair health score is as follows:

[0061] Hair density, elasticity coefficient, and pigmentation stability were obtained as measured values ​​for hair quality.

[0062] Based on the measured hair quality values ​​and the preset reference baseline values, normalized hair quality parameters are determined;

[0063] Based on normalized hair quality parameters and preset weighting coefficients, a weighted summation calculation is performed to generate the overall hair health score.

[0064] This embodiment is a detailed description of the process by which the portrait construction unit in the system described in Embodiment 1 generates the overall hair health score;

[0065] Based on Example 1, in order to ensure that the portrait construction unit can scientifically integrate multiple independent hair quality measurement values ​​with different physical dimensions into a dimensionless comprehensive indicator that can be directly used for subsequent risk assessment, this example optimizes the hair quality comprehensive health generation process as follows.

[0066] The hair comb uses built-in sensors to obtain hair density, elasticity, and pigmentation stability as measured values ​​of hair quality. , , ;

[0067] To eliminate the inconsistency in the dimensions of different physical parameters, this embodiment introduces a normalization process. Based on the measured hair quality values ​​and preset reference values, normalized hair quality parameters are determined. The specific calculation logic is to convert the measured values... , , Divide each by its corresponding reference value , , Among them, the reference benchmark value , , This refers to the statistical average or industry standard value of healthy hair quality. Its purpose is to provide a benchmark for comparison with measured values. It is derived from a system-preset constant based on statistical analysis of a large-scale hair quality database. Through this step, the hair quality parameters of each dimension are converted into a dimensionless ratio value, ensuring the mathematical rigor of the subsequent weighted summation.

[0068] Based on normalized hair quality parameters and preset weighting coefficients, a weighted summation calculation is performed to generate a comprehensive hair health score. To reflect the varying degrees of impact of different hair quality parameters on the feasibility of the solution, this embodiment introduces a weighting coefficient, the specific calculation formula of which is as follows:

[0069] ;

[0070] Among them, the weighting coefficient , , , , This refers to the weight of each normalized parameter in calculating overall health, corresponding to hair density respectively. Elasticity coefficient Pigmentation stability Hair follicle health With hair resilience The newly added reference benchmark value and Also derived from the statistical mean of a large-scale hair quality database, its purpose is to reflect the differences in importance of different hair quality dimensions. It is based on the initial preset based on the experience of hair industry experts, and during the system operation, the labels of historical service success or failure are used as supervision signals. The calibration dataset containing a large number of historical cases is trained and iteratively optimized through machine learning optimization algorithms such as gradient descent to maximize the accuracy of the prediction model.

[0071] This embodiment uses a linear weighted model for the sake of model simplicity and interpretability. The model uses machine learning algorithms to iteratively optimize the weight coefficients, so that the final weights can reflect the comprehensive impact of the nonlinear interaction between different dimensional parameters on the service results to a certain extent.

[0072] Through this specific implementation method, the system not only collects multi-dimensional data, but also integrates this data into a single, accurate, and clearly physically meaningful comprehensive health index through scientific normalization and weighted summation methods. Compared to simply listing various indicators, this method can more intuitively and accurately reflect the overall tolerance of a user's hair quality, providing a more reliable and effective input for subsequent physical risk assessment of the solution, thereby greatly improving the accuracy of risk assessment and the scientific nature of the entire system's decision-making.

[0073] Example 3:

[0074] The process of generating skill indices from profile building units is as follows:

[0075] Acquire the success rate, average processing time, and user ratings for specific hairstyle operations as historical service data;

[0076] The time efficiency item is determined based on the average time and the preset maximum reference time.

[0077] Based on user ratings and the preset full score of the rating system, the rating normalization item is determined;

[0078] The skill index is generated by combining the success rate of operation, time efficiency, and score normalization, and performing a weighted summation operation based on preset weight coefficients.

[0079] This embodiment is a detailed description of the process by which the profile building unit generates the skill index in the system described in Embodiment 1;

[0080] Based on Example 1, in order to accurately evaluate a hairstylist's comprehensive control ability over a specific high-difficulty hairstyle, rather than relying solely on a single success rate indicator, this example has refined the process of generating a skill index from the profile building unit as follows;

[0081] Get the success rate of specific hairstyle operations Average time and user ratings As historical service data; to unify the evaluation scale of various indicators, this embodiment normalizes the time and scoring data; based on average time consumption... And the preset reference maximum time Determine the time efficiency item Reference maximum time This refers to the maximum reasonable time allowed to complete the hairstyle. Its purpose is to set a benchmark for efficiency, derived from industry standards or salon benchmarks of hairstylists; it is also based on user ratings. And the full score of the preset scoring system Determine the scoring normalization item The scoring system's full score This refers to the upper limit of a rating system, such as a 5-point scale. The value is 5; through this step, the success rate, time efficiency, and scoring normalization are all standardized to dimensionless values ​​between 0 and 1, ensuring fair evaluation.

[0082] The skill index is generated by combining the success rate, time efficiency, and score normalization factors, and performing a weighted summation based on preset weighting coefficients. The integrated model is shown below:

[0083] ;

[0084] Among them, the weighting coefficient , , These represent the importance of success rate, time, and user satisfaction in the overall skills assessment, respectively. Their purpose is to allow the system to be flexibly adjusted according to the salon's operational priorities. The calibration process involves extracting historical service data of hairstylists from an independent calibration dataset and calculating an overall performance score for each hairstylist. ,Should Defined by the salon management based on operational objectives; subsequently, least squares regression analysis was used to... , and As the independent variable, Using the dependent variable as the model, the optimal weighting coefficients are obtained through fitting. , , ;

[0085] This implementation method constructs a multi-dimensional, normalized skill index model that includes success rate, time efficiency, and user reputation. Compared to existing methods that rely on a single indicator, this model can more comprehensively, objectively, and accurately depict a hairstylist's true mastery of a specific hairstyle. This makes the system's assessment basis more solid when matching people to jobs, thereby significantly improving the accuracy of subsequent risk assessments and ensuring that the recommended solutions truly match the hairstylist's abilities, thus reducing the risk of service failure due to insufficient skills from the source.

[0086] Example 4:

[0087] The process by which the risk assessment unit generates the physical risk index of the proposed solution is as follows:

[0088] The physical risk index of the solution is obtained by dividing the preset physical difficulty coefficient of the hairstyle by the product of the overall hair health and skill index generated by the portrait construction unit.

[0089] The process by which the risk assessment unit generates the AI ​​trust deficit index is as follows:

[0090] Obtain the extent of solution modifications, solution execution timeout rate, and stress feedback values ​​during the evaluation period;

[0091] Based on the extent of solution modification, solution execution timeout rate, stress feedback value, and preset weights, a weighted average is performed to generate the AI ​​trust deficit index.

[0092] This embodiment is a detailed explanation of the process by which the risk assessment unit in the system described in Embodiment 1 generates the physical risk index and the AI ​​trust deficit index.

[0093] Based on Example 1, in order to achieve accurate quantification of service risks, the risk assessment unit operates in parallel through two core models to assess objective physical risks and subjective trust risks respectively.

[0094] Physical risk index of the plan The generation process involves adjusting the inherent physical difficulty coefficient of the preset hairstyle. Divide by the overall hair health score generated by the portrait building unit With Skill Index The product of these factors; the underlying logic of this model is that risk is directly proportional to the difficulty of the task and inversely proportional to the ability of the executor and the tolerance of the target. The specific calculation formula is as follows:

[0095] ;

[0096] To ensure the robustness of the model, the following constraints are applied to the calculation of the denominator: denominator terms The value is limited to a very small value greater than zero. ,For example Above. That is, the actual calculation formula is: This effectively prevents computational overflow caused by hair quality or skill indices approaching zero, and ensures that the physical risk index remains stable even in such extreme cases. It will tend towards a very large value that aligns with physical intuition;

[0097] The inherent physical difficulty of hairstyle Hair overall health score is preset by the system's expert database. Hairstylist Skill Index Calculated from the image construction unit; due to , and All are dimensionless or normalized exponents. Similarly, it is a dimensionless risk index. The higher the value, the greater the risk of failure of the solution under the current combination of user hair quality and hairstylist skills. It should be noted that the model simplifies the mitigation effect of hair health and skill index on risk as a linear product relationship, which is an effective approximation in most routine service scenarios.

[0098] AI Trust Deficit Index The generation process involves transforming the abstract psychological concept of human-machine trust into measurable and manageable engineering indicators; and obtaining the extent of solution modifications within the evaluation period. Solution execution timeout rate and pressure feedback value These data are collected in real time by the service process monitoring system; the extent of plan modifications. The degree to which the hairstylist manually adjusts the i-th system-recommended plan is derived from the analysis of operation records; plan execution timeout rate. The percentage of times the i-th execution of the plan exceeds the predetermined time; stress feedback value. The emotion recognition algorithm, which utilizes facial expression recognition or voice tone analysis, is derived from the real-time analysis of unstructured video and audio data, using cameras and microphones deployed in the operating area. To ensure consistency of dimensions, parameters... , , All input values ​​are normalized to dimensionless values ​​between 0 and 1 before being input into the model;

[0099] Based on the above normalization parameters and preset weights, a weighted average is performed to generate the AI ​​trust deficit index. The calculation formula is as follows:

[0100] ;

[0101] The weighting reflects a deep understanding of behaviors stemming from a lack of trust; for example, proactive modification behavior. It is considered the most direct manifestation of a lack of trust, hence its weight. These weights are typically set to the highest level; the method for calibrating these weights is as follows: within an independent calibration period, different weight combinations are set through A / B testing, and the hairstylist churn rate for that period is collected. and customer negative review rate To minimize a given and The loss function is used as the objective, and the optimal weights are determined through an optimization algorithm. ;

[0102] This implementation method achieves a precise two-dimensional profile of the risks of hairdressing services by constructing a parallel physical risk and trust deficit assessment model. The index quantifies the probability of a solution's failure from an objective perspective of both material and technological aspects, while The index provides insights into the smoothness of collaboration from a subjective perspective of people and their feelings. Compared to traditional methods that only assess physical risks, this design can identify potential service quality degradation risks earlier and more accurately, providing comprehensive and profound insights for subsequent strategic decision-making on how to weaken trust. This is the core technical support for solving the AI ​​trust deficit problem in this invention.

[0103] Example 5:

[0104] The signal generation logic of the decision push unit is as follows:

[0105] When the AI ​​trust deficit index is less than the trust deficit assessment boundary and the solution physical risk index is not greater than the maximum physical risk threshold, the optimal effect push signal is generated.

[0106] When the AI ​​trust deficit index is greater than or equal to the trust deficit assessment boundary, or the physical risk index of the solution is greater than the maximum physical risk threshold, a strategic weakness signal is generated.

[0107] When a strategic weakening signal is generated, the decision-making push unit activates a constraint optimization algorithm. Under the constraints that the physical risk of the alternative solution is less than the maximum physical risk threshold and the hairstylist's skill index for the alternative solution is greater than the preset minimum skill index requirement, the algorithm finds the suboptimal solution that is closest to the style feature vector of the original solution and pushes it.

[0108] This embodiment is a detailed description of the signal generation logic and subsequent operations of the decision push unit in the system described in Embodiment 1;

[0109] Building upon Example 1, the core responsibility of the decision-making push unit is to use the output of the risk assessment unit... and The index dynamically balances the pursuit of optimal results with the protection of cooperative relationships, and selects the most appropriate push strategy.

[0110] The system's decision-making logic is a clear conditional judgment structure that directly relates to risk assessment results, forming an efficient perception-decision closed loop; when the AI ​​trust deficit index... Less than the trust deficit assessment boundary And the physical risk index of the plan Not greater than the maximum physical risk threshold When the AI ​​trust deficit index is low, a signal for optimal effect is generated. In such a low- or controllable situation, the system judges that the current subjective and objective conditions are both very ideal, so it will directly push the original hairstyle plan aimed at achieving the best aesthetic effect. Trust deficit assessment boundary or the physical risk index of the plan Greater than the maximum physical risk threshold At this time, a strategic signal of weakness is generated; if either of the two core risk indicators touches the red line, the system will determine that forcibly pushing the optimal solution may lead to service failure or exacerbate human-machine conflict; at this time, the system will not directly push the original solution, but will trigger a more intelligent intervention strategy.

[0111] When a strategic weakening signal is generated, the decision-making push unit initiates a constraint optimization algorithm. Under specific constraints, it searches for and pushes the suboptimal solution that is closest in style to the original solution. The essence of this process is to find an alternative point in a multi-dimensional hairstyle feature space that is closest to the target point (i.e., the original solution) and is also located within the safe zone. The specific algorithm is as follows:

[0112] ;

[0113] in, As an alternative to be found, For the system's hairstyle database; It is to minimize the original solution. and alternatives Style feature vectors Euclidean distance between them; style feature vector This refers to a high-dimensional vector used in mathematical space to describe the core aesthetic features of a hairstyle, such as curl, length, and layering. Its purpose is to enable quantitative comparison of hairstyle styles. It is derived by extracting features from hairstyle images using a deep learning image recognition model pre-trained on a massive number of hairstyle images. To compensate for the limitations of pure Euclidean distance in aesthetic evaluation, this optimization process can also introduce an aesthetic scoring function based on a Generative Adversarial Network (GAN). As an additional constraint or part of the objective function, ensure the recommended alternative. Not only are they similar in feature vectors, but they also maintain high quality and stylistic consistency in visual perception;

[0114] This optimization process must satisfy the following two constraints:

[0115] Alternative solutions The physical risk must be below the maximum physical risk threshold;

[0116] Hairstylist's alternative The skill index must be higher than a preset minimum skill index requirement. Minimum skill index requirements Its purpose is to ensure that hairstylists have sufficient confidence and ability to implement the recommended alternatives, which is based on a basic threshold set according to salon quality standards.

[0117] Finding the optimal alternative that satisfies the constraints Then, the system will push it to the hairstylist along with a structured, dynamically generated explanatory script; the script will reference key parameters from the calculation process to enhance its persuasiveness.

[0118] This implementation method, by introducing dynamic decision-making logic based on dual risk monitoring, and especially the innovative strategic vulnerability mechanism, achieves a shift from rigid recommendation to intelligent negotiation. Compared to traditional recommendation systems, this system can proactively take a step back when risks are identified, and provide a highly feasible suboptimal solution through a constrained optimization algorithm that is safe, competent for the hairstylist, and preserves the user's original intent to the greatest extent. This vulnerability not only avoids the failure of a single service, but also constitutes proactive trust relationship management. By demonstrating the system's intelligence and empathy, it fundamentally alleviates and even repairs the hairstylist's AI trust deficit, ensuring the long-term sustainability of human-machine collaboration.

[0119] Example 6:

[0120] The process by which the closed-loop correction unit corrects the AI ​​trust deficit index is as follows:

[0121] Get user ratings for this service;

[0122] Calculate the difference between the user rating and the preset average user rating;

[0123] The score difference is normalized to the full score of the preset scoring system to obtain the normalized satisfaction.

[0124] The normalized satisfaction rate is multiplied by the preset learning rate to generate a trust compensation factor.

[0125] The revised AI trust deficit index is obtained by subtracting the trust compensation factor from the original AI trust deficit index.

[0126] The closed-loop correction unit is also used to gradually adjust the maximum physical risk threshold and the trust deficit assessment boundary based on the feedback of preset core operating indicators and reinforcement learning algorithms.

[0127] This embodiment is a detailed description of the process by which the closed-loop correction unit in the system described in Embodiment 1 corrects the AI ​​trust deficit index and dynamically adjusts the system boundary.

[0128] Based on Example 1, in order to enable the system to have the ability to continuously learn and adaptively evolve, the closed-loop correction unit is started after each service is completed to iteratively optimize the core parameters of the system.

[0129] Correcting the AI ​​Trust Deficit Index The process aims to quantitatively repair or strengthen trust relationships, forming a feedback and correction loop based on service outcomes; and to obtain user ratings for this service. ; Calculate user ratings and compare them with the preset average user ratings The score difference; compare the score difference with the preset full score of the scoring system. Normalization was performed to obtain normalized satisfaction levels. The normalized satisfaction level is multiplied by a preset learning rate to generate a trust compensation factor. The specific algorithm is as follows:

[0130] ;

[0131] We have collected a large number of historical service cases, each of which includes user ratings after the service was provided. And the trust deficit index in the subsequent period Actual change Based on this calibrated dataset, a linear regression method was used to... As the independent variable, with The slope obtained from the fitting is the learning rate, where the variable is the dependent variable. When the service rating is above average, A positive value indicates that trust has been compensated; conversely, a negative value indicates that trust has been damaged.

[0132] From the unrevised AI trust deficit index Subtracting the trust compensation factor, we obtain the corrected AI trust deficit index. ;

[0133] ;

[0134] This formula directly quantifies the successful experience of a single service as an offset to the historical trust deficit, and vice versa, forming a dynamic correction closed loop of the trust model.

[0135] To ensure that the system's risk strategy aligns with the salon's overall operational goals, the closed-loop correction unit also uses reinforcement learning algorithms to gradually adjust the maximum physical risk threshold based on feedback from preset core operational indicators. Boundaries of Trust Deficit Assessment Specifically, the state of the system can be defined as the current state. Value pairs, actions are within a preset range. and Fine-tuning is performed, and rewards are set based on the core operational metrics within the adjusted period, such as the difference between the monthly customer negative review rate and the target value of 5%. If the negative review rate is below 5%, a positive reward is given, and the algorithm tends to moderately relax the threshold to encourage innovation; conversely, if the negative review rate is above 5%, a negative reward is given, and the algorithm will tighten the threshold to adopt a more conservative strategy.

[0136] This implementation method endows the system with two core self-optimization capabilities by establishing a closed-loop correction mechanism for service aftereffects. Through the trust compensation factor model, the abstract human-machine trust can be dynamically managed and repaired in a quantifiable manner, making each successful service a cornerstone for consolidating collaborative relationships. By introducing a reinforcement learning adjustment mechanism based on core operational indicators, the system's risk preference is no longer static, but a dynamic strategy that can adaptively adjust according to the overall operating conditions of the salon. The combination of these two mechanisms ensures that the system of this invention not only performs well in a single service, but also continuously evolves over time and with the accumulation of data, maintaining the accuracy of its decision-making and its commercial value in the long term.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A combiometer-based personalized haircare regimen push system, characterized in that, The method comprises the following steps: a data acquisition unit is used to collect multi-dimensional basic data of user's hair quality and historical service data of a hair stylist; an image construction unit is used to receive the multi-dimensional basic data to generate a hair quality comprehensive health degree, and receive the historical service data to generate a skill index; a risk assessment unit is used to generate a scheme physical risk index based on the hair quality comprehensive health degree and the skill index generated by the image construction unit, and in combination with a preset intrinsic physical difficulty coefficient of a hairstyle; and generate an AI trust deficit index based on collected service process data; a decision pushing unit is used to generate an optimal effect pushing signal or a strategic weakening signal based on a comparison result of the scheme physical risk index and a preset maximum physical risk threshold, and a comparison result of the AI trust deficit index and a preset trust deficit evaluation boundary; a closed-loop correction unit is used to correct the AI trust deficit index and dynamically adjust the maximum physical risk threshold and the trust deficit evaluation boundary based on collected final service data after the service is completed; The process of generating the scheme physical risk index by the risk assessment unit is as follows: The preset intrinsic physical difficulty coefficient of the hairstyle is divided by the product of the hair quality comprehensive health degree and the skill index generated by the image construction unit to obtain the scheme physical risk index. The process of generating the AI trust deficit index by the risk assessment unit is as follows: obtain the scheme modification amplitude, the scheme execution timeout rate and the stress feedback value in the evaluation period; Based on the scheme modification amplitude, the scheme execution timeout rate, the stress feedback value and the preset weight, weighted average processing is performed to generate an AI trust deficit index , and the calculation formula is as follows: ； The setting of weights embodies a deep understanding of the trust loss behavior; the method of setting these weights is as follows: in an independent setting period, different weight combinations are set through A / B testing, and the corresponding stylist churn rate of the period is collected and customer complaint rate ; taking minimizing a loss function composed of and as the goal, the optimal weights are determined through an optimization algorithm ; The signal generation logic of the decision pushing unit is as follows: when the AI trust deficit index is less than the trust deficit evaluation boundary, and the scheme physical risk index is not greater than the maximum physical risk threshold, an optimal effect pushing signal is generated; when the AI trust deficit index is greater than or equal to the trust deficit evaluation boundary, or the scheme physical risk index is greater than the maximum physical risk threshold, a strategic weakening signal is generated; when the strategic weakening signal is generated, the decision pushing unit starts a constraint optimization algorithm to find a suboptimal solution scheme closest to the style feature vector of the original scheme under the constraint conditions that the physical risk of the alternative scheme is less than the maximum physical risk threshold and the skill index of the alternative scheme to the hair stylist is greater than the preset minimum requirement of the skill index, and the alternative scheme is pushed; the essence of this process is to find an alternative point closest to the target point, i.e., the original scheme, in a multi-dimensional hairstyle feature space, and the alternative point is located in a safe area, and the specific algorithm is as follows: ； wherein, is the alternative solution to be found, is the system hairstyle database; is the original solution to be minimized is the Euclidean distance between the style feature vector of the original solution and the style feature vector of the alternative solution .

2. A combiometer-based personalized haircare regimen push system according to claim 1, characterized in that, The process of generating the hair quality comprehensive health degree by the image construction unit is as follows: obtain the hair density, the elasticity coefficient and the pigmentation stability as the measured values of the hair quality; determine the normalized hair quality parameters based on the measured values of the hair quality and the preset reference benchmark values; perform weighted summation operation based on the normalized hair quality parameters and the preset weight coefficients to generate the hair quality comprehensive health degree.

3. A combiometer-based personalized haircare regimen push system according to claim 1, wherein, The process of generating the skill index by the image construction unit is as follows: obtain the specific hairstyle operation success rate, the average time consumption and the user score as the historical service data; determine the time efficiency item based on the average time consumption and the preset reference maximum time consumption; determine the score normalization item based on the user score and the preset full score value of the scoring system; combine the operation success rate, the time efficiency item and the score normalization item, and perform weighted summation operation according to the preset weight coefficients to generate the skill index.

4. A combiometer-based personalized haircare regimen push system according to claim 1, wherein, The process of the closed-loop correction unit correcting the AI trust deficit index is as follows: Obtain the user score of this service; Calculate the score difference between the user score and the preset average user score; Normalize the score difference with the preset score system full score value to obtain the normalized satisfaction degree; Multiply the normalized satisfaction degree by the preset learning rate to generate a trust compensation factor; Subtract the trust compensation factor from the AI trust deficit index before correction to obtain the corrected AI trust deficit index.

5. A combiometer-based personalized haircare regimen push system according to claim 1, wherein, The closed-loop correction unit is also used to gradually adjust the maximum physical risk threshold and the trust deficit evaluation boundary based on the feedback of the preset core operation indicators using the reinforcement learning algorithm.

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