Method, system and terminal for detecting components of hair care product

Abstract

The invention discloses a hair washing and caring product component detection method, system and terminal, and relates to the field of washing and caring product detection. The method comprises the following steps: acquiring identification component information of a to-be-detected product, wherein the identification component information comprises a component name and an addition amount; generating expected detection data of each component according to the component name and the additive amount; obtaining a product sample and carrying out component analysis to obtain actual detection data; comparing the expected detection data with the actual detection data to obtain a detection deviation value of each component; and generating a component conformity evaluation result according to the detection deviation value. Through quantitative comparative analysis of the expected detection data and the actual detection data, accurate evaluation of product component conformity is realized, and the detection efficiency and accuracy are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of testing of personal care products, specifically to a method, system, and terminal for testing the components of hair care products. Background Technology

[0002] Hair care products are an important part of daily personal care, and their ingredients directly affect the safety, efficacy, and compliance of the products.

[0003] Currently, hair care products are mostly complex systems with numerous ingredients and varying concentrations. Furthermore, manufacturing processes can introduce deviations, leading to significant discrepancies between the actual ingredients used and the labeled composition. Traditional testing methods primarily rely on laboratory wet chemical analysis or single-instrument testing, which are cumbersome, time-consuming, and dependent on human experience. This makes it difficult to comprehensively and efficiently assess the consistency of multiple components. Moreover, traditional testing methods lack the ability to build predictive models using historical data, resulting in insufficient intelligence in the testing process. Summary of the Invention

[0004] This invention addresses the technical problem in the prior art that it is impossible to effectively verify the conformity between the actual ingredient composition of hair care products and their labeled formula information, and provides a method, system and terminal for detecting the ingredients of hair care products.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a method for detecting the ingredients of hair care products, comprising: Obtain the labeling ingredient information of the hair care product to be tested, wherein the labeling ingredient information includes the ingredient names and amounts of multiple ingredients; Based on the ingredient names and amounts of multiple ingredients, generate the expected detection data for each ingredient; Obtain a sample of the hair care product to be tested, and perform component analysis on the hair care product sample according to the labeled ingredient information to obtain the actual test data of each component; By comparing the expected detection data and the actual detection data of each component, the detection deviation value of each component is obtained; Based on the detection deviation values ​​of each component, the component conformity evaluation results of the hair care product to be tested are generated.

[0006] Secondly, the present invention provides a system for detecting the ingredients of hair care products, comprising: The labeling ingredient information acquisition module is used to acquire the labeling ingredient information of the hair care product to be tested. The labeling ingredient information includes the ingredient names and amounts of multiple ingredients. The expected detection data generation module is used to generate expected detection data for each component based on the component name and amount added. The actual test data acquisition module is used to acquire the hair care product sample to be tested, perform component analysis on the hair care product sample according to the identified component information, and obtain the actual test data of each component. The detection deviation value calculation module is used to compare the expected detection data and the actual detection data of each component to obtain the detection deviation value of each component. The conformity evaluation result generation module is used to generate the conformity evaluation results of the components of the hair care product to be tested based on the detection deviation values ​​of each component.

[0007] Thirdly, the present invention provides a smart terminal, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the hair care product ingredient detection method described in the first aspect.

[0008] The beneficial effects of this invention are: Compared to existing technologies, this invention firstly improves the efficiency and consistency of test preparation and reduces human error by automatically acquiring component information and generating expected test data for each component. Secondly, it utilizes intelligent methods such as component testing simulators to predict instrument response values, enabling rapid and accurate simulation of components in complex mixtures and enhancing the intelligence and foresight of the testing process. Thirdly, by comparing expected and actual test data and calculating component deviation values, it achieves a comprehensive and objective quantitative evaluation of product component conformity. Finally, through quantitative comparative analysis of expected and actual test data, it achieves an accurate evaluation of product component conformity, significantly improving testing efficiency and accuracy. Attached Figure Description

[0009] Figure 1 A schematic flowchart illustrating a method for detecting the components of hair care products provided by this invention; Figure 2 This is a schematic diagram of the structure of a hair care product ingredient detection system provided by the present invention.

[0010] Figure 3 A schematic diagram of the structure of a smart terminal provided by the present invention; In the attached diagram, the components represented by each number are as follows: The system includes: component information acquisition module 11, expected test data generation module 12, actual test data acquisition module 13, test deviation value calculation module 14, conformity evaluation result generation module 15, smart terminal 200, memory 210, processor 220, and computer software program 211. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0013] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0014] Example 1, as Figure 1 As shown, this embodiment of the invention provides a method for detecting the ingredients of hair care products, including: S10: Obtain the labeling ingredient information of the hair care product to be tested, wherein the labeling ingredient information includes the ingredient names and amounts of multiple ingredients; Specifically, obtain the labeling ingredient information of the hair care product to be tested. This labeling ingredient information includes the ingredient names and amounts of multiple ingredients, including: Obtain the formula file corresponding to the hair care product to be tested; The formula file is parsed to extract the ingredient names and the amount added as a percentage by mass for each component, thus obtaining the identifying ingredient information.

[0015] Specifically, the hair care products to be tested refer to daily chemical products used for hair cleaning, conditioning, or styling, such as shampoos, conditioners, and hair masks. Their labeled ingredient information is a description of the composition clearly stated on the product label or in technical documentation, including the standard names of multiple ingredients and their amounts expressed as a percentage by mass. Obtaining the labeled ingredient information of the hair care products to be tested is the initial step in the testing process and provides a benchmark for subsequent ingredient compliance evaluation.

[0016] First, it is necessary to obtain the formula file corresponding to the hair care product to be tested. The formula file refers to the formal document containing all the ingredients of the product and their proportions. It can be in the form of an electronic document or a physical carrier, such as a digital version of product registration information, production records, or label markings.

[0017] Secondly, after obtaining the formula file, it needs to be parsed. The parsing process involves identifying the data structure and format of the formula file, locating and reading the data fields related to the ingredients. After parsing, the ingredient names and their amounts expressed as mass percentages are extracted. The extraction process must ensure the integrity and accuracy of the data to avoid omissions or misinterpretations.

[0018] Finally, all extracted ingredient names and dosage information are integrated to form a clearly structured and machine-readable ingredient identification information.

[0019] S20: Generate the expected detection data for each component based on the component name and amount added; Based on the ingredient names and amounts of multiple components, generate the expected detection data for each component, including: Determine the name and amount of a first component from the plurality of components, and retrieve the first component detection simulator based on the name of the first component; The first addition amount is input into the first component detection simulator to generate the expected chromatographic peak area of ​​the first component, which is used as the first expected detection data of the first component. The expected detection data for the remaining components are generated in the same manner as the first expected detection data for the first component, thus obtaining the expected detection data for each component.

[0020] First, one component is selected from the acquired component identification information as the first component, and its name and addition amount are specified. The first component refers to any component selected preferentially from multiple test components for the first round of prediction and modeling. The selection order can be determined by the component addition amount from high to low. Then, based on the name of the first component, a pre-trained first component detection simulator is invoked. This first component detection simulator is a computational model that can simulate the response behavior of a component in a detection instrument. It can accurately predict the instrument response value of the component under specific analytical conditions based on the input addition amount, and is used to generate the expected detection data of the component under theoretical conditions.

[0021] Specifically, the construction steps of the first component detection simulator include: Based on the name of the first component, historical detection records are retrieved, and multiple sets of training data are constructed according to the historical detection records. Each set of training data includes the amount of sample added and the chromatographic peak area of ​​the sample. Using the amount of sample added as input and the chromatographic peak area of ​​the sample as a supervision label, the first component detection simulator is trained and constructed based on the multiple sets of training data.

[0022] The construction of the first component detection simulator is a process of training a machine learning model based on historical data, aiming to establish an accurate mapping relationship between the amount of component added and the instrument response value. The specific construction steps are as follows: First, based on the name of the first ingredient, historical testing records related to that ingredient are retrieved from the historical database. The historical database is a structured data warehouse storing raw data and results from past testing of ingredients in various hair care products. The data originates from long-term accumulated actual testing experiments and compliance inspections. The historical testing records include different dosages of the ingredient used in previous experiments and the corresponding chromatographic peak areas actually measured in the chromatograph.

[0023] Then, multiple sets of training data were constructed based on the retrieved historical detection records. Each set of training data contains two key fields: sample addition amount and sample chromatographic peak area. The sample addition amount represents the actual amount of the component added in a certain historical experiment, while the sample chromatographic peak area represents the actual response value measured by the chromatograph at that addition amount.

[0024] Furthermore, a first-component detection simulator is constructed and trained. Machine learning refers to a class of computational methods that can automatically learn patterns from historical data and build predictive models. By analyzing the inherent statistical characteristics of a large amount of sample data, a mapping relationship between input variables and output targets is established, thereby achieving accurate prediction of unknown data. For example, algorithms such as linear regression, support vector regression, or neural networks can be used to construct a first-component detection simulator.

[0025] For example, since there is often a complex nonlinear relationship between the amount of component added and the response value of the chromatographic instrument, and neural networks have outstanding advantages in fitting high-dimensional nonlinear mappings and automatic feature learning, a neural network model was chosen to construct the first component detection simulator.

[0026] Specifically, the first component detection simulator mainly consists of an input layer, hidden layers, and an output layer. The input layer receives component addition data expressed as a mass percentage and eliminates the influence of dimensions through standardization. The hidden layers employ a multi-layer fully connected structure, with the number of neurons dynamically adjusted according to data complexity. Each layer uses the ReLU activation function to enhance non-linear expression. A Dropout layer is also embedded in the network, with a dropout rate set between 0.2 and 0.4 to mitigate overfitting and improve the model's generalization performance. The output layer uses a linear activation function to directly regress and predict the expected chromatographic peak area of ​​the component.

[0027] During training, key hyperparameters included a learning rate of 0.0005, 150 training epochs, and a batch size of 64. The learning rate setting balanced training stability and convergence speed, the number of training epochs ensured the model fully learned the nonlinear relationships in the samples, and the batch size balanced memory usage and gradient update efficiency. Specifically, a supervised learning approach was adopted, constructing a training sample set based on historical detection records. The sample addition quantity served as the input feature, and the sample chromatographic peak area served as the continuous value label. All samples were randomly divided proportionally into training, validation, and test sets.

[0028] Furthermore, the sample addition amounts from the training set are input into the network, with the corresponding chromatographic peak areas used as supervisory signals. The network weight parameters are iteratively adjusted using the Adam optimizer via backpropagation. The mean squared error loss function is used to quantify the difference between the predicted and actual peak areas, and the training process is monitored using a validation set. Training is terminated when the validation set loss no longer decreases after multiple consecutive rounds and the prediction accuracy reaches a preset level, such as 95%, resulting in the finally converged first-component detection simulator. This first-component detection simulator can accurately capture the complex relationship between the addition amount and the instrument response, providing reliable support for generating high-precision expected detection data.

[0029] Furthermore, the first addition amount is input into the first component detection simulator. The first component detection simulator processes the data based on the internally established mapping relationship between the addition amount and the instrument response value, and outputs the expected chromatographic peak area of ​​the component under simulated detection conditions, which is the first expected detection data of the first component.

[0030] Then, following the same method as for the first component, the corresponding component detection simulator is invoked for each of the remaining components in sequence. The amount of each component added is input into its dedicated simulator, generating the corresponding expected chromatographic peak area. Finally, the expected data for all components are summarized to form a complete set of expected detection data for each component, providing a theoretical basis for subsequent comparison with actual detection results.

[0031] S30: Obtain a sample of the hair care product to be tested, perform component analysis on the hair care product sample according to the identified component information, and obtain the actual test data of each component; After obtaining the expected test data for each ingredient, the actual response values ​​of hair care product samples need to be obtained through actual testing for comparison and verification. The specific process is as follows.

[0032] First, samples of the hair care product to be tested must be obtained. These samples should originate from the same production batch or a representative product unit to ensure the accuracy and repeatability of the test results. Then, based on the ingredient categories listed in the previously obtained ingredient labeling information, appropriate instrumental analysis methods are selected to perform qualitative and quantitative analysis on the samples. The instrumental methods used for the actual ingredient analysis should be consistent with the historical testing methods upon which the ingredient detection simulator is based.

[0033] During the detection process, for each identified component, its response value in the actual sample is obtained through instrumental analysis. For example, if chromatographic analysis is used, the chromatographic peaks corresponding to each component are accurately identified by analyzing the chromatogram, and their actual peak areas are calculated by integration. This actual peak area serves as the actual detection data for that component.

[0034] Finally, the actual detection data of all identified components are collected to form a set of actual detection data that is completely consistent with the actual sample component response, which is used for subsequent deviation calculation and compliance evaluation.

[0035] S40: Compare the expected detection data and actual detection data of each component to obtain the detection deviation value of each component; Specifically, by comparing the expected detection data and the actual detection data for each component, the detection deviation value for each component is obtained, including: Obtain the first expected detection data and the first actual detection data of the first component; Calculate the difference between the first expected detection data and the first actual detection data to obtain the first detection deviation value of the first component; The detection deviation values ​​of the remaining components are calculated in the same way as the first detection deviation value of the first component, thus obtaining the detection deviation value of each component.

[0036] First, obtain the first expected detection data and the first actual detection data for the first component. The first expected detection data represents the instrument response value that the component should produce under ideal conditions, i.e., the chromatographic peak area predicted by the first component detection simulator. The first actual detection data represents the response value actually measured by instrument analysis from the hair care product sample, i.e., the true peak area calculated by integration in the chromatographic analysis.

[0037] The difference between the first expected detection data and the first actual detection data is calculated to obtain the first detection deviation value of the first component. The first detection deviation value = |(first actual detection data - first expected detection data)| / first expected detection data × 100%, which represents the absolute deviation of the actual instrument response value of the component from the theoretical prediction value. It is used to objectively measure the degree of deviation between the theoretical prediction and the actual measurement of the component.

[0038] Then, following the same method used to calculate the deviation value of the first component, the expected and actual detection data of each of the remaining components are processed sequentially, and their detection deviation values ​​are calculated separately. Finally, a set of detection deviation values ​​for all components is obtained, providing a quantitative basis for generating subsequent conformity evaluation results.

[0039] S50: Based on the detection deviation values ​​of each component, generate the component conformity evaluation results of the hair care product to be tested.

[0040] Specifically, based on the detection deviation values ​​of each component, the component conformity evaluation results of the hair care product under test are generated, including: Obtain the baseline addition amount and preset deviation threshold for each component; Calculate the ratio of the amount of each component added to the corresponding baseline amount to obtain the correction factor for each component; The corresponding preset deviation threshold is adjusted according to the correction factor of each component to obtain the adjustment deviation threshold of each component; The detection deviation value of each component is compared with the corresponding adjustment deviation threshold to generate the compliance judgment result of each component; When the detection deviation values ​​of each component are all less than or equal to the corresponding adjustment deviation threshold, the component conformity evaluation result of the hair care product to be tested is qualified.

[0041] When the detection deviation value of any component exceeds the corresponding adjustment deviation threshold range, the component conformity evaluation result of the hair care product under test is deemed unqualified.

[0042] First, obtain the reference addition amounts and preset deviation thresholds for each component. Among them, the reference addition amount refers to the specified addition amount of the component in the standard formula or relevant regulations; the preset deviation threshold is the initially set maximum allowable deviation range, which is comprehensively set according to the physical and chemical properties of the component, the precision of the detection method, and industry regulatory requirements. For example, the preset deviation threshold of the main active ingredient is usually set at ±5% of the nominal addition amount, while non-critical components such as excipients or fragrances can be appropriately relaxed to ±10% or higher.

[0043] Secondly, calculate the ratio of the actual addition amount of each component to the corresponding reference addition amount to obtain the correction factor for each component. Specifically, the correction factor = actual addition amount / reference addition amount. This correction factor is used to reflect the deviation ratio of the actual addition amount relative to the standard value and is then used to adjust the subsequent evaluation criteria.

[0044] Furthermore, adjust the corresponding preset deviation threshold according to the correction factor of each component to obtain the adjusted deviation threshold for each component. Specifically, the adjusted deviation threshold = preset deviation threshold × correction factor. This adjustment process enables the evaluation criteria to adapt to the actual needs of components at different addition levels. By introducing a dynamic adjustment mechanism proportional to the addition amount, it effectively improves the ability to distinguish the deviation tolerance of components with different contents, thus significantly enhancing the scientificity and rationality of the evaluation results.

[0045] Further, compare the detection deviation value of each component with the corresponding adjusted deviation threshold. If the detection deviation value of a certain component is less than or equal to its adjusted deviation threshold, it is determined that the component meets the requirements; if the detection deviation value of a certain component exceeds the threshold range, it is determined that the component does not meet the requirements.

[0046] Since the proportion of each component in hair care products has undergone strict scientific verification and safety assessment, any deviation exceeding the standard of a component may disrupt the overall balance of the product formula, resulting in reduced product efficacy, increased irritation, or unforeseen adverse reactions. Therefore, to ensure the safety, effectiveness, and quality consistency of the product, strict control must be implemented on the content of each component. Specifically, when the detection deviation values of all components are less than or equal to the corresponding adjusted deviation thresholds, the final component compliance evaluation result of the hair care product to be tested is qualified. When the detection deviation value of any component exceeds the corresponding adjusted deviation threshold, the component compliance evaluation result of the product is unqualified.

[0047] Furthermore, when the component compliance evaluation result of the hair care product to be tested is unqualified, obtain a prohibited ingredient library, which contains multiple prohibited ingredients in hair care products; According to the multiple prohibited ingredients, conduct prohibited ingredient detection on the hair care product sample to obtain the prohibited ingredient detection result; When the test results for the prohibited ingredients show that prohibited ingredients are detected, it is determined that the reason for the failure of the hair care product to be tested is that it contains prohibited ingredients. When the test results for the prohibited ingredients show that no prohibited ingredients are detected, the reason for the failure of the hair care product to be tested is determined to be excessive deviation of ingredients.

[0048] When the conformity evaluation result of the ingredients of a hair care product being tested is unqualified, further analysis of the specific reasons for the unqualified product is required. Specifically, by introducing a prohibited ingredient screening mechanism, in-depth diagnosis and tracing of the causes of unqualified products can be achieved.

[0049] First, a pre-established database of prohibited ingredients is obtained. This database includes multiple prohibited ingredients for use in hair care products, such as hormones, antibacterial agents, or other restricted substances explicitly prohibited by relevant regulations. Based on the prohibited ingredients listed in the database, the hair care product sample undergoes targeted testing for these prohibited ingredients. Specifically, high-sensitivity analytical techniques, such as liquid chromatography-mass spectrometry (LC-MS) or gas chromatography-mass spectrometry (GC-MS), are used to obtain the detection results for the presence of prohibited ingredients in the sample.

[0050] If the test results show that a prohibited ingredient is detected, the reason for the failure of the tested hair care product is determined to be the presence of a prohibited ingredient. If the test results show that no prohibited ingredient is detected, it indicates that the failure of the tested hair care product is not due to prohibited substances, but rather to the deviation of the added ingredient from the allowable range found in previous tests. Therefore, the reason for the failure is determined to be excessive deviation of the ingredient.

[0051] This step-by-step diagnostic mechanism not only clarifies the specific problem types of non-conforming products, but also provides precise basis for subsequent processing, improving the pertinence and effectiveness of quality control.

[0052] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application firstly improves the processing efficiency and data consistency of the early stages of the testing process by automatically acquiring product identification component information and intelligently generating expected detection data for each component, effectively avoiding errors that may be introduced by manual operation. Secondly, by employing a component detection simulator, it can accurately predict the instrument response value based on the component name and dosage, thereby achieving rapid and accurate simulation of complex mixture systems and significantly enhancing the intelligence and predictive capabilities of the testing process. Thirdly, by comparing expected theoretical data with actual test results, it automatically calculates the quantitative deviation of each component, achieving a comprehensive, objective, and quantifiable scientific evaluation of the product's component compliance.

[0053] In summary, this application, through quantitative comparison and analysis of expected and actual test data, achieves an accurate evaluation of the product composition compliance, significantly improving testing efficiency and accuracy.

[0054] Example 2, as Figure 2 As shown, based on the same inventive concept as the hair care product ingredient detection method provided in Embodiment 1, this embodiment of the invention also provides a hair care product ingredient detection system, including: The labeling ingredient information acquisition module 11 is used to acquire the labeling ingredient information of the hair care product to be tested, wherein the labeling ingredient information includes the ingredient names and amounts of multiple ingredients; The expected detection data generation module 12 is used to generate expected detection data for each component based on the component name and amount added of multiple components. The actual test data acquisition module 13 is used to acquire the hair care product sample of the hair care product to be tested, perform component analysis on the hair care product sample according to the identified component information, and obtain the actual test data of each component. The detection deviation value calculation module 14 is used to compare the expected detection data and the actual detection data of each component to obtain the detection deviation value of each component. The conformity evaluation result generation module 15 is used to generate the conformity evaluation result of the components of the hair care product to be tested based on the detection deviation value of each component.

[0055] The component identification information acquisition module 11 is specifically used for: Obtain the labeling ingredient information of the hair care product to be tested. This labeling ingredient information includes the ingredient names and amounts of multiple ingredients, including: Obtain the formula file corresponding to the hair care product to be tested; The formula file is parsed to extract the ingredient names and the amount added as a percentage by mass for each component, thus obtaining the identifying ingredient information.

[0056] Specifically, the expected detection data generation module 12 is used for: Based on the ingredient names and amounts of multiple components, generate the expected detection data for each component, including: Determine the name and amount of a first component from the plurality of components, and retrieve the first component detection simulator based on the name of the first component; The first addition amount is input into the first component detection simulator to generate the expected chromatographic peak area of ​​the first component, which is used as the first expected detection data of the first component. The expected detection data for the remaining components are generated in the same manner as the first expected detection data for the first component, thus obtaining the expected detection data for each component.

[0057] Specifically, the construction steps of the first component detection simulator include: Based on the name of the first component, historical detection records are retrieved, and multiple sets of training data are constructed according to the historical detection records. Each set of training data includes the amount of sample added and the chromatographic peak area of ​​the sample. Using the amount of sample added as input and the chromatographic peak area of ​​the sample as a supervision label, the first component detection simulator is trained and constructed based on the multiple sets of training data.

[0058] The actual detection data acquisition module 13 is specifically used for: This method is used to obtain a sample of the hair care product to be tested, and to perform component analysis on the hair care product sample based on the identified component information to obtain actual test data for each component.

[0059] The detection deviation value calculation module 14 is specifically used for: By comparing the expected detection data and the actual detection data for each component, the detection deviation value for each component is obtained, including: Obtain the first expected detection data and the first actual detection data of the first component; Calculate the difference between the first expected detection data and the first actual detection data to obtain the first detection deviation value of the first component; The detection deviation values ​​of the remaining components are calculated in the same way as the first detection deviation value of the first component, thus obtaining the detection deviation value of each component.

[0060] The conformity evaluation result generation module 15 is specifically used for: Based on the detection deviation values ​​of each component, the component conformity evaluation results of the hair care product under test are generated, including: Obtain the baseline addition amount and preset deviation threshold for each component; Calculate the ratio of the amount of each component added to the corresponding baseline amount to obtain the correction factor for each component; The corresponding preset deviation threshold is adjusted according to the correction factor of each component to obtain the adjustment deviation threshold of each component; The detection deviation value of each component is compared with the corresponding adjustment deviation threshold to generate the compliance judgment result of each component; When the detection deviation values ​​of each component are all less than or equal to the corresponding adjustment deviation threshold, the component conformity evaluation result of the hair care product to be tested is qualified.

[0061] Specifically, when the detection deviation value of any component exceeds the corresponding adjustment deviation threshold range, the component conformity evaluation result of the hair care product under test is deemed unqualified.

[0062] In addition, when the conformity evaluation result of the hair care product to be tested is unqualified, a prohibited ingredient library is obtained, which contains multiple prohibited ingredients that are not allowed to be used in hair care products; Based on the aforementioned multiple contraindicated ingredients, the hair care product samples were tested for contraindicated ingredients, and the test results were obtained. When the test results for the prohibited ingredients show that prohibited ingredients are detected, it is determined that the reason for the failure of the hair care product to be tested is that it contains prohibited ingredients. When the test results for the prohibited ingredients show that no prohibited ingredients are detected, the reason for the failure of the hair care product to be tested is determined to be excessive deviation of ingredients.

[0063] Example 3, as Figure 3 As shown, this embodiment of the invention also provides a smart terminal 200, including a memory 210, a processor 220, and a computer software program 211 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer software program 211, it implements a method for detecting the ingredients of hair care products provided in Embodiment 1.

[0064] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0065] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0066] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for detecting the ingredients in hair care products, characterized in that, The method includes: Obtain the labeling ingredient information of the hair care product to be tested, wherein the labeling ingredient information includes the ingredient names and amounts of multiple ingredients; Based on the ingredient names and amounts of multiple ingredients, generate the expected detection data for each ingredient; Obtain a sample of the hair care product to be tested, and perform component analysis on the hair care product sample according to the labeled ingredient information to obtain the actual test data of each component; By comparing the expected detection data and the actual detection data of each component, the detection deviation value of each component is obtained; Based on the detection deviation values ​​of each component, the component conformity evaluation results of the hair care product to be tested are generated.

2. The method according to claim 1, characterized in that, Obtain the labeling ingredient information of the hair care product to be tested. This labeling ingredient information includes the ingredient names and amounts of multiple ingredients, including: Obtain the formula file corresponding to the hair care product to be tested; The formula file is parsed to extract the ingredient names and the amount added as a percentage by mass for each component, thus obtaining the identifying ingredient information.

3. The method according to claim 1, characterized in that, Based on the ingredient names and amounts of multiple components, generate the expected detection data for each component, including: Determine the name and amount of a first component from the plurality of components, and retrieve the first component detection simulator based on the name of the first component; The first addition amount is input into the first component detection simulator to generate the expected chromatographic peak area of ​​the first component, which is used as the first expected detection data of the first component. The expected detection data for the remaining components are generated in the same manner as the first expected detection data for the first component, thus obtaining the expected detection data for each component.

4. The method according to claim 3, characterized in that, The construction steps of the first component detection simulator include: Based on the name of the first component, historical detection records are retrieved, and multiple sets of training data are constructed according to the historical detection records. Each set of training data includes the amount of sample added and the chromatographic peak area of ​​the sample. Using the amount of sample added as input and the chromatographic peak area of ​​the sample as a supervision label, the first component detection simulator is trained and constructed based on the multiple sets of training data.

5. The method according to claim 4, characterized in that, By comparing the expected detection data and the actual detection data for each component, the detection deviation value for each component is obtained, including: Obtain the first expected detection data and the first actual detection data of the first component; Calculate the difference between the first expected detection data and the first actual detection data to obtain the first detection deviation value of the first component; The detection deviation values ​​of the remaining components are calculated in the same way as the first detection deviation value of the first component, thus obtaining the detection deviation value of each component.

6. The method according to claim 1, characterized in that, Based on the detection deviation values ​​of each component, the component conformity evaluation results of the hair care product under test are generated, including: Obtain the baseline addition amount and preset deviation threshold for each component; Calculate the ratio of the amount of each component added to the corresponding baseline amount to obtain the correction factor for each component; The corresponding preset deviation threshold is adjusted according to the correction factor of each component to obtain the adjustment deviation threshold of each component; The detection deviation value of each component is compared with the corresponding adjustment deviation threshold to generate the compliance judgment result of each component; When the detection deviation values ​​of each component are all less than or equal to the corresponding adjustment deviation threshold, the component conformity evaluation result of the hair care product to be tested is qualified.

7. The method according to claim 6, characterized in that, When the detection deviation value of any component exceeds the corresponding adjustment deviation threshold range, the component conformity evaluation result of the hair care product under test is deemed unqualified.

8. The method according to claim 7, characterized in that, The method further includes: When the conformity evaluation result of the ingredients of the hair care product to be tested is unqualified, a prohibited ingredient library is obtained, which contains multiple prohibited ingredients that are prohibited from being used in hair care products. Based on the aforementioned multiple contraindicated ingredients, the hair care product samples were tested for contraindicated ingredients, and the test results were obtained. When the test results for the prohibited ingredients show that prohibited ingredients are detected, it is determined that the reason for the failure of the hair care product to be tested is that it contains prohibited ingredients. When the test results for the prohibited ingredients show that no prohibited ingredients are detected, the reason for the failure of the hair care product to be tested is determined to be excessive deviation of ingredients.

9. A system for detecting the ingredients of hair care products, characterized in that, For performing the method according to any one of claims 1-8, comprising: The labeling ingredient information acquisition module is used to acquire the labeling ingredient information of the hair care product to be tested. The labeling ingredient information includes the ingredient names and amounts of multiple ingredients. The expected detection data generation module is used to generate expected detection data for each component based on the component name and amount added. The actual test data acquisition module is used to acquire the hair care product sample to be tested, perform component analysis on the hair care product sample according to the identified component information, and obtain the actual test data of each component. The detection deviation value calculation module is used to compare the expected detection data and the actual detection data of each component to obtain the detection deviation value of each component. The conformity evaluation result generation module is used to generate the conformity evaluation results of the components of the hair care product to be tested based on the detection deviation values ​​of each component.

10. A smart terminal, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, thereby implementing the method for detecting the ingredients of a hair care product as described in any one of claims 1-8.

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