Hair care comb control system and method based on hair quality detection

Abstract

The present invention discloses a control system and method for a hair care comb based on hair quality detection, and relates to the technical field of hair quality detection. The hair care comb enables a personalized application mode based on the hair quality data and preference information of the user, and sends a multi-level alarm instruction to the outside when the hair quality data of the user is abnormal, and switches the alarm channel; the hair quality management knowledge map gives improvement measures based on the hair quality data of the user, and the hair care comb product database recommends corresponding products to the user, and dynamically adjusts the recommended matching of products and improvement measures; the hair care comb optimizes the improvement measures and sends them to the user. If there are still specific abnormalities in the hair quality, the hair care comb pushes content to the user and provides online consulting services, and constrains the push frequency of paid consulting services based on the recent continuous abnormalities. To prevent users from ignoring current hair quality problems, the alarm channels are updated and switched, and when users ignore the alarm instructions or do not receive them, it is ensured that users can give feedback.

Description

Technical Field

[0001] The present invention relates to the technical field of hair quality detection, and in particular to a hair care comb control system and method based on hair quality detection. Background Art

[0002] Hair quality testing is a comprehensive scalp and hair health assessment service that uses advanced chemical and physical analysis techniques to deeply test key indicators such as hair nutrition, strength, elasticity, oil balance, and degree of damage. Through this test, individuals can fully understand their hair quality and identify potential hair problems such as dryness, split ends, and hair loss, so as to develop a more accurate and personalized hair care and maintenance plan, laying a solid foundation for hair health.

[0003] The hair care comb is an innovative hair care tool that combines technology and aesthetics. It cleverly combines a variety of technological elements such as low-energy red light, microcurrent stimulation, and vibration massage to bring users an unprecedented hair care experience. Regular use of the hair care comb can effectively promote blood circulation in the scalp, activate the vitality of hair follicles, accelerate hair growth, and reduce scalp problems such as hair loss and dandruff. In addition, the exquisite design and portability of the hair care comb also make it an indispensable hair care companion in the daily life of modern people, making hair health and beauty within reach.

[0004] In the Chinese invention patent with application publication number CN115779274A, a control system, control method and hair care comb of a hair care comb are disclosed. The system includes: a plasma module, a microcurrent module, a power module and a control module; wherein the control module is connected to the plasma module, the control module is connected to the microcurrent module, the power module is connected to the plasma module, the power module is connected to the microcurrent module, and the control module is connected to the power module; the plasma module is used to output a first voltage to the discharge electrode to ionize the air to form plasma; the microcurrent module is used to generate microcurrent to electrically stimulate the scalp; the control module is used to control the working state of the plasma module and the microcurrent module, and adjust the plasma module and the microcurrent module to work at the same time. The system can realize multifunctional care of the head, increase the number of functional modules of the hair care comb, and thus improve the sterilization and hair growth effects of the hair care comb.

[0005] Combined with the above application and the contents of the prior art:

[0006] When the hair quality is poor, users will take a variety of different hair care measures to improve their hair quality, such as physical therapy or changing hair care products. As a more common and daily way, users will use a hair care comb to improve the hair quality with the help of the health care effect of the hair care comb.

[0007] Existing hair care combs usually have different functions and modes, such as massage function and heating function, etc., and also release some substances with hair care effects to the outside. When users control the hair care comb, they can switch the usage mode, but this control method is mainly performed manually by the user. Considering that it is difficult for users to accurately and effectively understand their own hair quality, and the hair quality is also in a constantly changing state, therefore, when the existing control method is applied, the effect of improving the user's hair quality will be limited to a certain extent. At the same time, when there may be certain abnormalities in the hair quality, the user may not be able to perceive it in time.

[0008] To this end, the present invention provides a hair care comb control system and method based on hair quality detection. Summary of the invention

[0009] 1. Technical issues to be resolved

[0010] In view of the deficiencies of the prior art, the present invention provides a hair care comb control system and method based on hair quality detection. The hair quality management knowledge graph provides improvement measures based on the user's hair quality data, and the hair care comb product database recommends corresponding products to the user, and dynamically adjusts the recommended matching of products and improvement measures; the hair care comb optimizes the improvement measures and sends them to the user. If there are still specific abnormalities in the hair quality, the hair care comb pushes content to the user and provides online consulting services, and constrains the push frequency of paid consulting services based on the recent continuous abnormalities. To prevent users from ignoring current hair quality problems, the alarm channel is updated and switched, and when the user ignores the alarm instruction or does not receive it, it is ensured that the user can give feedback, thereby solving the technical problems recorded in the background technology.

[0011] (II) Technical solution

[0012] To achieve the above objectives, the present invention is implemented through the following technical solutions: a hair care comb control method based on hair quality detection, comprising: collecting hair quality data by a sensor network of the hair care comb, generating a hair quality index from a plurality of continuously acquired hair quality data; , if the hair quality obtained If the hair quality threshold is below the threshold, personalized maintenance instructions will be issued to the outside world;

[0013] The hair care comb enables a personalized application mode according to the user's hair quality data and preference information, and adjusts the length of the observation period according to the pre-set length of the observation period constrained by the user's hair quality;

[0014] Use the trained hair quality recognition model to detect abnormal data. When there is an abnormality in the user's hair quality data, issue a multi-level alarm command to the outside and switch the alarm channel.

[0015] After the hair quality management knowledge graph gives improvement measures based on the user's hair quality data, the hair care comb product database recommends corresponding products to the user and dynamically adjusts the recommended matching of products and improvement measures;

[0016] After the hair care comb optimizes the improvement measures, it will send them to the user. If there are still specific abnormalities in the hair quality, the hair care comb will push content to the user and provide online consulting services. The push frequency of paid consulting services will be restricted based on the number of recent consecutive abnormalities. Push frequency The constraints are as follows:

[0017] ;

[0018] Where: Is the basic push frequency, is the adjustment coefficient, with a value between 0 and 1. is an indicator function, which takes the value of 1 when the condition is true; , count the abnormality within the time window W Exceeding the abnormal threshold The number of times The current time node.

[0019] Furthermore, the sensor network of the hair care comb collects the user's hair quality data and aggregates them to generate a hair quality data set; the user's hair quality data is used as input, and the trained hair quality recognition model is used to identify the user's scalp health status and generate a hair quality score;

[0020] After performing several hair quality tests within a preset hair quality test cycle, the hair quality index is generated from several continuously obtained hair quality scores under dimensionless conditions. , as follows:

[0021] ;

[0022] Where: For the Hair quality rating, and Respectively Second and The time of the test, It is the detection constant, which is 2 to 5 times of the hair quality detection cycle. is the time decay constant, For the The weight coefficient of the test, is the forecast adjustment factor.

[0023] Furthermore, after receiving the personalized maintenance instruction, the user's hair quality data and preference information are collected, and the corresponding maintenance features are obtained through feature engineering;

[0024] A number of different application modes are pre-set for the hair care comb, and an application mode set is generated after being summarized; according to the correspondence between the maintenance characteristics and the application modes, the application mode set provides a personalized application mode for the user.

[0025] Furthermore, after enabling personalized usage mode for the user, the user's hair quality Constraining the pre-set observation period length , the length of the observation period is adjusted according to the constraints, and the constraints are as follows:

[0026] ;

[0027] Where: is the length of the basic observation period, is the current hair quality, k is the hair quality adjustment coefficient, is the rate of change of hair quality, is the rate of change adjustment coefficient, and its value is between 0 and 1.

[0028] Furthermore, after a preset observation period, the user's hair quality data is continuously collected when using the hair care comb. The user's hair quality data is used as input, and abnormal data detection is performed using the trained hair quality recognition model. When there is an abnormality in the user's hair quality data, a first-level alarm instruction is issued to the outside.

[0029] Furthermore, when the number of consecutive first-level alarm instructions exceeds expectations, an abnormality degree is constructed based on the status data of the first-level alarm instructions received. , if the abnormality When the preset abnormal threshold is exceeded, a secondary alarm command will be issued to the outside world, and an alarm notification will be made through mobile applications, vibration, sound or LED indicators.

[0030] Furthermore, the abnormality degree is constructed based on the status data of the received first-level alarm instruction. The way is as follows:

[0031] ;

[0032] Where: is the rate of change of the number of alarm instructions N, is the characteristic state function, T is the length of the alarm cycle for issuing a first-level alarm command, For hair strength, For hair elasticity, is the diameter of the hair; , and are the corresponding weight coefficients respectively, and the sum of the three is 1. and They are all weight coefficients, and their values ​​fall between 0 and 1.

[0033] Furthermore, after receiving the secondary alarm instruction, the hair quality improvement is used as the target word to pre-build the hair quality management knowledge graph; the user's hair quality data is predicted to obtain the corresponding prediction data;

[0034] Improved features are obtained after feature extraction based on historical data, real-time data and predicted data.

[0035] Furthermore, the user's hair quality data is re-collected and recorded, and combined with the component analysis in the product database, based on the pre-trained collaborative filtering algorithm, the corresponding products are recommended to the user in the product database;

[0036] Dynamically adjust the recommended matching of products and improvement measures based on users' feedback after long-term use and new hair quality data.

[0037] Furthermore, the observation period is re-determined based on the hair quality. After the new observation period, if the improvement rate of the user's hair quality does not exceed expectations, the user sets a hair quality goal and collects personal hair and scalp type information;

[0038] Improvement measures are given again based on the hair quality management knowledge graph, and improving hair quality is used as the optimization goal. The improvement measures are optimized based on the predicted optimization algorithm, and the optimized improvement measures are obtained and sent to the user.

[0039] Furthermore, after the user receives the optimized improvement measures, the hair quality data of the user will continue to be collected by the hair care comb during the current observation period. The hair quality data during the current observation period is used as input, and the trained anomaly recognition model is used to perform anomaly recognition on the hair quality data. If a specific type of anomaly is still detected and identified, it will be used as a target anomaly.

[0040] Furthermore, a number of articles and video contents related to scalp care are pre-collated and collected, and then summarized as push content. According to the correspondence between the target anomaly and the push content, the push content is pushed to the user by a recommendation algorithm based on similarity;

[0041] When the number of content pushes exceeds the preset threshold, the user is provided with online paid consulting services, and the push frequency of paid consulting services is restricted based on the number of recent consecutive abnormalities. , adjust the push time nodes of paid consulting services based on the push frequency constraints.

[0042] A hair quality comb control system and method based on hair quality detection includes a hair quality analysis unit, wherein a sensor network of the hair quality comb collects hair quality data, and generates a hair quality index based on a plurality of continuously acquired hair quality data. , if the hair quality obtained If the hair quality threshold is below the threshold, personalized maintenance instructions will be issued to the outside world;

[0043] A mode switching unit enables the hair care comb to activate a personalized application mode according to the user's hair quality data and preference information, and adjusts the length of the observation period according to the pre-set length of the observation period constrained by the user's hair quality;

[0044] The hair quality alarm unit uses the trained hair quality recognition model to detect abnormal data. When the user's hair quality data is abnormal, it issues a multi-level alarm command to the outside and switches the alarm channel.

[0045] The measure matching unit, after the hair quality management knowledge graph gives improvement measures based on the user's hair quality data, the hair care comb product database recommends corresponding products to the user and dynamically adjusts the recommended matching of products and improvement measures;

[0046] The push unit is sent to the user after the hair care comb optimizes the improvement measures. If there are still specific abnormalities in the hair quality, the hair care comb will push content to the user and provide online consulting services, and limit the push frequency of paid consulting services based on the recent consecutive abnormalities.

[0047] (III) Beneficial effects

[0048] The present invention provides a hair care comb control system and method based on hair quality detection, which has the following beneficial effects:

[0049] 1. Based on the obtained hair quality score, the user's current hair condition can be evaluated, and the corresponding application mode or care strategy can be selected for the user according to the user's hair condition; A comprehensive assessment can be made on the recent hair condition of the user. After initial use of the hair care comb, it can be confirmed and determined whether the user's hair condition requires maintenance, and the hair care comb can be controlled and mode switched.

[0050] 2. Recommend and enable corresponding application modes for users based on their hair quality data and preference data, so that users can use hair care combs in a targeted manner to improve their hair quality.

[0051] 3. Constrain the length of the observation period according to the user's hair quality, so that the observation period is in a variable state. When the user's current hair quality is poor, the matched personalized usage mode is enabled for a longer time, so that the user's hair quality can be processed and improved for a longer time, and the improvement of the user's hair quality is accelerated when the hair care comb is controlled.

[0052] 4. Carry out hair quality anomaly detection based on the collected hair quality data. If hair quality anomalies still exist, issue an alarm command to the outside to remind the user to deal with it in time; by setting up a secondary alarm command, it can prevent users from ignoring current hair quality problems; update and switch the alarm channel to ensure that users can give feedback when users ignore the alarm command or do not receive it.

[0053] 5. Provide users with targeted hair quality improvement measures to improve their hair quality; on the basis of providing improvement measures to users, after the hair care comb is in continuous use and hair quality data is collected, recommend corresponding products to users, ensure the hair quality of users, and conduct a certain degree of marketing.

[0054] 6. Optimize improvement measures based on collected user data to make personalized improvement measures more targeted to users and more helpful in improving users’ hair quality; push content based on users’ hair quality to increase users’ attention to hair quality improvement, or push similar products and services.

[0055] 7. When the user's hair quality may still have abnormalities, provide the user with expert consultation and give corresponding improvement suggestions to improve the user's hair quality; by constraining the degree of abnormality and adjusting the push frequency to adapt it to the user's hair quality, fully avoid the user from ignoring the abnormality of hair quality when using the hair care comb. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic flow chart of a hair care comb control method based on hair quality detection according to the present invention;

[0057] Figure 2 The figure is a schematic diagram of the structure of a hair care comb control system based on hair quality detection according to the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 The present invention provides a hair care comb control method based on hair quality detection, comprising:

[0060] Step 1: The sensor network of the hair care comb collects hair quality data, and generates a hair quality index from the continuously acquired hair quality data. , if the hair quality obtained If the hair quality threshold is below the threshold, personalized maintenance instructions will be issued to the outside world;

[0061] The step 1 includes the following contents:

[0062] Step 101: When a user uses a hair care comb, a sensor network of the hair care comb, such as a temperature sensor, a conductivity sensor, a humidity sensor, an accelerometer, and a gyroscope, collects the user's hair quality data, such as temperature, oil level, humidity status, and hair tension data, and aggregates the acquired hair quality data to generate a hair quality data set;

[0063] After training the machine learning algorithm with the labeled sample data, a trained hair quality recognition model is obtained; the user's hair quality data is used as input, and the trained hair quality recognition model is used to identify the user's scalp health status and generate a hair quality score;

[0064] When the hair care comb is in use, the sensor network or module collects the user's hair quality data and performs detection to obtain a corresponding comprehensive score. The user's current hair quality condition can be evaluated based on the obtained hair quality score, and a corresponding application mode or care strategy can be selected for the user based on the user's hair quality condition.

[0065] Step 102: after performing a number of hair quality tests within a preset hair quality test cycle, a hair quality index is generated from a number of continuously obtained hair quality scores under dimensionless conditions. , as follows:

[0066] ;

[0067] Where: For the Hair quality rating, and Respectively Second and The time of the test, It is the detection constant, which is 2 to 5 times of the hair quality detection cycle. is the time decay constant, ranging from 0 to 1. For the The weight coefficient of the detection is between 0 and 1. is the prediction adjustment factor, with a value between 0 and 10;

[0068] Pre-set hair quality thresholds based on historical data and users’ hair quality management expectations;

[0069] If the hair quality is obtained If the hair quality is lower than the hair quality threshold, it means that the user's current hair quality is poor. When using the hair care comb, the user's hair needs to be cared for and personalized care instructions are issued.

[0070] When using, combine the contents in steps 101 and 102:

[0071] After several consecutive hair quality tests, a hair quality score is generated based on several hair quality scores. , according to hair quality It can comprehensively evaluate the hair quality of the user in the recent period. After the initial use of the hair care comb, it can confirm and judge whether the user's hair quality needs maintenance, so as to control the hair care comb and switch the mode;

[0072] Combined with the above application and the contents of the prior art:

[0073] When the hair quality is poor, users will take a variety of different hair care measures to improve their hair quality, such as physical therapy or changing hair care products. As a more common and daily way, users will use a hair care comb to improve the hair quality with the help of the health care effect of the hair care comb.

[0074] Existing hair care combs usually have different functions and modes, such as massage function and heating function, etc., and also release some substances with hair care effects to the outside. When the user controls the hair care comb, the usage mode can be switched, but this control method is mainly performed manually by the user. Considering that it is difficult for the user to accurately and effectively understand the condition of their own hair quality, and the hair quality is also in a constantly changing state, therefore, when the existing control method is applied, the effect of improving the user's hair quality will be limited to a certain extent. At the same time, when there may be certain abnormalities in the hair quality, the user may not be able to perceive it in time.

[0075] Step 2: The hair care comb activates a personalized application mode according to the user's hair quality data and preference information, and adjusts the length of the observation period according to the pre-set length of the observation period constrained by the user's hair quality;

[0076] The step 2 includes the following contents:

[0077] Step 201: after receiving the personalized care instruction, after collecting the user's hair quality data and the user's preference information, the corresponding maintenance features are obtained through feature engineering; a number of different application modes are pre-set for the hair care comb, such as vibration massage, temperature-controlled thermal therapy and light therapy functions, and different massage intensities and thermal therapy temperatures, etc., and an application mode set is generated after being summarized; according to the correspondence between the maintenance features and the application modes, a personalized application mode is given to the user by the application mode set;

[0078] During use, after the initial use phase, the corresponding application mode is recommended and enabled for the user based on the user's hair quality data and preference data, so that the user can use the hair care comb in a targeted manner to improve the user's hair quality;

[0079] Step 202: After enabling the personalized usage mode for the user, Constraining the pre-set observation period length , the constraints are as follows:

[0080] ;

[0081] Where: is the length of the basic observation period, is the current hair quality, k is the hair quality adjustment coefficient, and its value is between 0 and 1. is the rate of change of hair quality, is the change rate adjustment coefficient, which ranges from 0 to 1;

[0082] The length of the observation period is adjusted according to the constraints, so that after the user has passed the current personalized maintenance stage, they can enter the next observation period;

[0083] When using, combine the contents in steps 201 and 202:

[0084] By constraining the length of the observation period according to the user's hair quality, the observation period is made variable. When the user's current hair quality is poor, the matched personalized usage mode is enabled for a longer time, so that the user's hair quality can be processed and improved for a longer period of time, thereby accelerating the improvement of the user's hair quality when controlling the hair care comb.

[0085] Step 3: Use the trained hair quality recognition model to detect abnormal data, issue a multi-level alarm command to the outside when there is an abnormality in the user's hair quality data, and switch the alarm channel;

[0086] The step three includes the following contents:

[0087] Step 301: After a preset observation period, continuously collect the user's hair quality data when using the hair care comb, such as hair strength, hair scale state, hair gloss, hair elasticity, hair diameter and hair moisture content; use the labeled sample data to train a machine learning algorithm to obtain a trained hair quality recognition model;

[0088] The user's hair quality data is used as input, and the trained hair quality recognition model is used to detect abnormal data. When the user's hair quality data is abnormal, a first-level alarm instruction is issued to the outside.

[0089] When in use, after the hair care comb has been used for a period of time, hair quality abnormality detection is carried out based on the collected hair quality data. If hair quality abnormality still exists, an alarm instruction is sent to the outside to remind the user to deal with it in time;

[0090] Step 302: When the number of consecutive acquisitions of the first-level alarm instruction exceeds the expected number, an abnormality degree is constructed based on the state data of the received first-level alarm instruction. The way is as follows:

[0091] ;

[0092] Where: is the rate of change of the number of alarm instructions N, is the characteristic state function, T is the length of the alarm cycle for issuing a first-level alarm command, For hair strength, For hair elasticity, is the diameter of the hair; , and are the corresponding weight coefficients respectively, and the sum of the three is 1. and They are all weight coefficients, and their values ​​fall between 0 and 1.

[0093] Based on historical data and management expectations of user hair quality, abnormal thresholds are set in advance; if the abnormality is If the hair quality exceeds the preset abnormal threshold, it means that the user's current hair quality is still poor and needs further processing. At this time, a secondary alarm instruction is issued to the outside, and a multi-level alarm mechanism is established for the user's hair quality detection;

[0094] When in use, a second-level alarm command is constructed on the basis of the first-level alarm command. Considering that users may feel that the first-level alarm command is not serious in daily use and may ignore the first-level alarm command to a certain extent, by constructing a second-level alarm command, users can be further prevented from ignoring the current hair quality problem;

[0095] Alarm notification via mobile app, vibration, sound or LED indicator, switching alarm channel if secondary alarm command fails to receive response;

[0096] When used, combine the contents in steps 301 and 302:

[0097] As a further aspect, updating and switching the alarm channel when an alarm instruction is issued can ensure that the user can give feedback when the user ignores the alarm instruction or does not receive it.

[0098] Step 4: After the hair quality management knowledge graph gives improvement measures based on the user's hair quality data, the hair care comb product database recommends corresponding products to the user and dynamically adjusts the recommended matching of products and improvement measures;

[0099] The step 4 includes the following contents:

[0100] Step 401: after receiving the secondary alarm instruction, taking hair quality improvement as the target word, a hair quality management knowledge graph is pre-built through deep retrieval and entity relationship building; a time recurrent neural network is used to predict the user's hair quality data to obtain corresponding prediction data; and feature extraction is performed based on historical data, real-time data and prediction data to obtain improvement features;

[0101] According to the correspondence between the improvement features and the hair quality improvement measures, the hair quality management knowledge graph provides corresponding improvement measures for the user's hair quality;

[0102] When in use, after continuously acquiring the user's hair quality data, the corresponding hair quality features are obtained through feature extraction, and targeted hair quality improvement measures can be provided to the user. When the user uses this as a reference, the user's hair quality can be improved;

[0103] Step 402: re-collect and record the user's hair quality data such as scalp type, oil level and humidity, and combine the component analysis in the product database to recommend corresponding products to the user in the product database based on the pre-trained collaborative filtering algorithm;

[0104] Dynamically adjust the recommended matching of products and improvement measures based on long-term user feedback and new hair quality data;

[0105] When using, combine the contents in steps 401 and 402:

[0106] As a further way to conduct in-depth control over the hair care comb, on the basis of providing improvement measures to users (or not on this basis), corresponding products can be recommended to users after the hair care comb is in continuous use and hair quality data is collected. This can not only ensure the hair quality of users, but also conduct a certain degree of marketing on this basis to increase additional revenue.

[0107] Step 5: The hair care comb optimizes the improvement measures and sends them to the user. If the hair quality still has certain abnormalities, the hair care comb pushes content to the user and provides online consulting services. The push frequency of paid consulting services is restricted based on the number of recent consecutive abnormalities.

[0108] The step five includes the following contents:

[0109] Step 501: re-determine the observation period based on the hair quality degree. After the new observation period, if the improvement ratio of the user's hair quality degree does not exceed expectations, the user sets a hair quality goal and collects personal hair and scalp type information;

[0110] Improvement measures are given again based on the hair quality management knowledge graph, and improving hair quality is used as the optimization goal. The improvement measures are optimized based on the predicted optimization algorithm, and the optimized improvement measures are sent to users, thereby providing more personalized suggestions;

[0111] When in use, as a further control of the hair care comb, the improvement measures are optimized based on the collection of user data, so that the personalized improvement measures are more targeted to the user and more helpful in improving the user's hair quality.

[0112] Step 502: After the user receives the optimized improvement measures, the hair care comb continues to collect the user's hair quality data during the current observation period; the machine learning algorithm is trained with the labeled sample data to obtain a trained hair quality abnormality recognition model;

[0113] The hair quality data in the current observation period is used as input, and the trained anomaly recognition model is used to identify anomalies in the hair quality data. If a specific type of anomaly is still detected, it is used as the target anomaly.

[0114] Step 503: Preliminarily collect and sort out a number of articles and video contents related to scalp care, summarize them as push contents, and push the push contents to the user using a recommendation algorithm based on similarity according to the correspondence between the target anomaly and the push contents;

[0115] When in use, as further content, after the user has used the hair care comb for a period of time, as a feedback method after collecting data, content is pushed based on the user's hair quality. This can be used as a reference to increase the user's attention to hair quality improvement, or to push similar products and services.

[0116] When the number of content pushes exceeds the preset threshold, users are provided with online paid consultation services. Users can directly ask for advice from scalp health experts, and the push frequency of paid consultation services is restricted based on the number of recent consecutive abnormalities. , the constraints are as follows:

[0117] ;

[0118] Where: Is the basic push frequency, is the adjustment coefficient, with a value between 0 and 1. is an indicator function, which takes the value of 1 when the condition is true; , count the abnormality within the time window W Exceeding the abnormal threshold The number of times is the current time node;

[0119] Adjust the push time nodes of paid consulting services based on the push frequency constraint method;

[0120] When using, combine the contents in steps 501 to 503:

[0121] As a further content, considering that the previous multiple consecutive personalized application modes, response measures and content push have failed to achieve the expected results, when the user's hair quality may still be abnormal, if the hair care comb can communicate and exchange data with the outside, it can provide the user with expert consultation, give corresponding improvement suggestions, and improve the user's hair quality; at the same time, as a further supplementary solution, by constraining the degree of abnormality and adjusting the push frequency to adapt it to the user's hair quality, the user can be fully prevented from ignoring the abnormality of hair quality when using the hair care comb.

[0122] The construction method of the hair quality management knowledge graph can refer to the following:

[0123] Clarify the construction goal - The construction goal of the hair quality management knowledge graph is to integrate knowledge related to hair quality, including hair composition, structure, causes of damage, maintenance methods, etc., in order to facilitate applications such as intelligent question answering and recommendation systems.

[0124] Data collection and preprocessing - Data collection: Collect data related to hair quality management, including structured data (such as tabular data in professional literature and research reports), semi-structured data (such as tables and lists in web pages), and unstructured data (such as text descriptions, user comments, etc.). Data preprocessing: Clean, remove duplicates, format, and other operations on the collected data to facilitate subsequent knowledge extraction and knowledge fusion.

[0125] Knowledge extraction - entity extraction: Extract entities related to hair quality management from the text, such as hair type, shampoo products, hair care methods, etc.

[0126] Relationship extraction: Extract the relationship between entities, such as "a certain shampoo product is suitable for a certain hair type", "a certain hair care method can improve a certain hair problem", etc. Attribute extraction: Extract the attribute information of entities, such as the composition, structure, and cause of damage of hair.

[0127] Knowledge fusion and entity alignment - Knowledge fusion: Integrate knowledge from multiple knowledge sources to form a comprehensive, accurate and complete knowledge system. This includes the alignment of identical or similar entities in different knowledge sources, as well as the integration of entity relationships in different knowledge sources. Entity alignment: Align identical or similar entities in different knowledge sources through similarity calculation, aggregation, clustering and other technologies.

[0128] Data model construction - select data model: According to the characteristics of hair quality management, select a suitable data model to organize the data in the knowledge graph. Commonly used data models include RDF (Resource Description Framework), OWL (Web Ontology Language), etc. Define data model: Based on the selected data model, define concepts such as classes, attributes, relationships, and the constraints and rules between them in the hair quality management knowledge graph.

[0129] Quality assessment and optimization - Quality assessment: Conduct quality assessment on the constructed knowledge graph, including data accuracy, completeness, consistency, etc. Optimization and iteration: Based on the quality assessment results, optimize and iterate the knowledge graph to improve its quality and usability.

[0130] See also Figure 2 The present invention provides a hair care comb control system based on hair quality detection, comprising:

[0131] The hair quality analysis unit collects hair quality data through the sensor network of the hair care comb, and generates a hair quality index based on the continuously acquired hair quality data. , if the hair quality obtained If the hair quality threshold is below the threshold, personalized maintenance instructions will be issued to the outside world;

[0132] A mode switching unit enables the hair care comb to activate a personalized application mode according to the user's hair quality data and preference information, and adjusts the length of the observation period according to the pre-set length of the observation period constrained by the user's hair quality;

[0133] The hair quality alarm unit uses the trained hair quality recognition model to detect abnormal data. When the user's hair quality data is abnormal, it issues a multi-level alarm command to the outside and switches the alarm channel.

[0134] The measure matching unit, after the hair quality management knowledge graph gives improvement measures based on the user's hair quality data, the hair care comb product database recommends corresponding products to the user and dynamically adjusts the recommended matching of products and improvement measures;

[0135] The push unit is sent to the user after the hair care comb optimizes the improvement measures. If there are still specific abnormalities in the hair quality, the hair care comb will push content to the user and provide online consulting services, and limit the push frequency of paid consulting services based on the recent consecutive abnormalities.

[0136] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0137] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0138] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A hair care comb control method based on hair quality detection, characterized in that: include, The sensor network of the hair care comb collects hair quality data, and generates a hair quality index based on the continuously acquired hair quality data. , if the hair quality obtained If the hair quality is below the threshold, a personalized maintenance instruction is issued to the outside. After several hair quality tests are performed within the preset hair quality test cycle, the hair quality degree is generated by the continuously obtained hair quality scores under dimensionless conditions. , as follows: ； Where: Score the hair quality of the i-th hair, and Respectively Second and The time of the test, It is the detection constant, which is 2 to 5 times of the hair quality detection cycle. is the time decay constant, is the weight coefficient of the i-th detection, is the forecast adjustment factor; The hair care comb enables a personalized application mode based on the user's hair quality data and preference information, and the user's hair quality constrains the pre-set observation period length, thereby adjusting the length of the observation period; wherein, after the personalized use mode is enabled for the user, the hair quality of the user is constrained by the pre-set observation period length. Constraining the pre-set observation period length , the length of the observation period is adjusted according to the constraints, and the constraints are as follows: ； Where: is the length of the basic observation period, For the current hair quality, is the hair quality adjustment coefficient, is the rate of change of hair quality, is the change rate adjustment coefficient, which ranges from 0 to 1; Use the trained hair quality recognition model to detect abnormal data. When there is an abnormality in the user's hair quality data, issue a multi-level alarm command to the outside and switch the alarm channel. After the hair quality management knowledge graph gives improvement measures based on the user's hair quality data, the hair care comb product database recommends corresponding products to the user and dynamically adjusts the recommended matching of products and improvement measures; After the hair care comb optimizes the improvement measures, it will send them to the user. If there are still specific abnormalities in the hair quality, the hair care comb will push content to the user and provide online consulting services. The push frequency of paid consulting services will be restricted based on the recent number of consecutive abnormalities. Push frequency The constraints are as follows; ； Where: Is the basic push frequency, is the adjustment coefficient, with a value between 0 and 1. is an indicator function, which takes the value of 1 when the condition is true; , in the time window Internal statistical abnormality Exceeding the abnormal threshold The number of times The current time node.

2. The hair care comb control method based on hair quality detection according to claim 1, characterized in that: The sensor network of the hair care comb collects the user's hair quality data and aggregates them to generate a hair quality data set; Taking the user's hair quality data as input, the trained hair quality recognition model is used to identify the user's scalp health status and generate a hair quality score.

3. The hair care comb control method based on hair quality detection according to claim 2, characterized in that: After receiving personalized maintenance instructions, the user's hair quality data and preference information are collected, and the corresponding maintenance features are obtained through feature engineering; A number of different application modes are pre-set for the hair care comb, and an application mode set is generated after being summarized; according to the correspondence between the maintenance characteristics and the application modes, the application mode set provides a personalized application mode for the user.

4. The hair care comb control method based on hair quality detection according to claim 3, characterized in that: After a pre-set observation period, the hair quality data of the user is continuously collected when using the hair care comb. The hair quality data of the user is used as input, and abnormal data detection is performed using the trained hair quality recognition model. When there is an abnormality in the hair quality data of the user, a first-level alarm instruction is issued to the outside.

5. The hair care comb control method based on hair quality detection according to claim 4, characterized in that: When the number of consecutive first-level alarm instructions exceeds expectations, the abnormality degree is constructed based on the status data of the first-level alarm instructions received. , if the abnormality When the preset abnormal threshold is exceeded, a secondary alarm command will be issued to the outside world, and an alarm notification will be made through mobile applications, vibration, sound or LED indicators.

6. The hair care comb control method based on hair quality detection according to claim 5, characterized in that: Construct the abnormality degree based on the status data of the received first-level alarm command The way is as follows: ； Where: is the rate of change of the number of alarm instructions N, is the characteristic state function, T2 is the length of the alarm cycle for issuing a first-level alarm command, For hair strength, For hair elasticity, is the diameter of the hair; and The corresponding weight coefficients are as follows, and the sum of the three is 1. and They are all weight coefficients, and their values ​​fall between 0 and 1.

7. The hair care comb control method based on hair quality detection according to claim 6, characterized in that: After receiving the second-level alarm command, hair quality improvement is used as the target word, a hair quality management knowledge graph is pre-built, the user's hair quality data is predicted, and the corresponding prediction data is obtained; Improved features are obtained after feature extraction based on historical data, real-time data and predicted data.

8. The hair care comb control method based on hair quality detection according to claim 7, characterized in that: Re-collect and record the user's hair quality data, and combine it with the component analysis in the product database, and recommend corresponding products to users in the product database based on the pre-trained collaborative filtering algorithm; Dynamically adjust the recommended matching of products and improvement measures based on users' feedback after long-term use and new hair quality data.

9. The hair care comb control method based on hair quality detection according to claim 8, characterized in that: The observation period is re-determined based on the hair quality. After the new observation period, if the improvement rate of the user's hair quality does not exceed expectations, the user sets a hair quality goal and collects personal hair and scalp type information; Improvement measures are given again based on the hair quality management knowledge graph, and improving hair quality is used as the optimization goal. The improvement measures are optimized based on the predicted optimization algorithm, and the optimized improvement measures are obtained and sent to the user.

10. The hair care comb control method based on hair quality detection according to claim 9, characterized in that: After the user receives the optimized improvement measures, the hair quality data of the user is collected by the hair care comb during the current observation period; The hair quality data in the current observation period is used as input, and the trained anomaly recognition model is used to identify anomalies in the hair quality data. If a specific type of anomaly is still detected and identified, it is used as the target anomaly.

11. The hair care comb control method based on hair quality detection according to claim 10, characterized in that: Pre-organize and collect a number of articles and video contents related to scalp care, summarize them as push content, and push the push content to users based on the correspondence between the target anomaly and the push content using a similarity-based recommendation algorithm; When the number of content pushes exceeds the preset threshold, the user is provided with online paid consulting services, and the push frequency of paid consulting services is restricted based on the number of recent consecutive abnormalities. , adjust the push time nodes of paid consulting services based on the push frequency constraints.

12. A hair care comb control system based on hair quality detection, applying the control method according to any one of claims 1 to 11, characterized in that: include, The hair quality analysis unit collects hair quality data through the sensor network of the hair care comb, and generates a hair quality index based on the continuously acquired hair quality data. , if the hair quality obtained If the hair quality threshold is below the threshold, personalized maintenance instructions will be issued to the outside world; A mode switching unit enables the hair care comb to activate a personalized application mode according to the user's hair quality data and preference information, and adjusts the length of the observation period according to the pre-set length of the observation period constrained by the user's hair quality; The hair quality alarm unit uses the trained hair quality recognition model to detect abnormal data. When the user's hair quality data is abnormal, it issues a multi-level alarm command to the outside and switches the alarm channel. The measure matching unit, after the hair quality management knowledge graph gives improvement measures based on the user's hair quality data, the hair care comb product database recommends corresponding products to the user and dynamically adjusts the recommended matching of products and improvement measures; The push unit is sent to the user after the hair care comb optimizes the improvement measures. If there are still specific abnormalities in the hair quality, the hair care comb will push content to the user and provide online consulting services, and limit the push frequency of paid consulting services based on the recent consecutive abnormalities.

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