Cosmetics production dynamic monitoring system and method based on the whole life cycle

Abstract

The present invention relates to the field of cosmetic production monitoring technology, and specifically to a cosmetic production dynamic monitoring system and method based on the entire life cycle. The method comprises: the present invention obtains a cosmetic raw material safety index based on cosmetic raw material information and cosmetic raw material index information, performs principal component analysis on cosmetic production monitoring data to obtain a cosmetic production safety index, obtains cosmetic transportation information and cosmetic transportation index information, performs comparative analysis based on cosmetic transportation information and cosmetic transportation index information, and obtains a cosmetic transportation safety index; generates a cosmetic production early warning signal based on the cosmetic raw material safety index, the cosmetic production safety index, and the cosmetic transportation safety index, and can monitor the raw material procurement link, production link, and transportation link in the cosmetic production process in real time, can timely discover problems in each link, and improve product quality and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of cosmetic production monitoring, and in particular to a cosmetic production dynamic monitoring system and method based on the entire life cycle. Background Art

[0002] With the continuous expansion of the cosmetics market and the increasing diversification of consumer demand, the cosmetics industry is facing unprecedented development opportunities and challenges. However, the production process of cosmetics is complex and involves multiple links, from raw material procurement, formula design, production and manufacturing to finished product packaging, warehousing and transportation. Each link is directly related to the quality and safety of the product. Therefore, how to ensure the safety and compliance of cosmetics throughout the life cycle has become an important issue that the cosmetics industry needs to solve urgently.

[0003] Current status and challenges of cosmetics production:

[0004] Raw material safety: There are many kinds of raw materials for cosmetics, including chemical synthesis, natural extracts, etc. The safety of these raw materials is directly related to the quality of the final product. However, there may be problems such as uneven quality, counterfeiting and inferior products in the raw material procurement process, which brings safety risks to the production of cosmetics.

[0005] Production process control: The production process of cosmetics involves multiple steps, such as mixing, filling, packaging, etc. Each link needs to be strictly controlled to ensure product quality. However, traditional manual monitoring methods have problems such as incomplete monitoring and untimely response, making it difficult to effectively ensure product quality.

[0006] Transportation process control: The transportation process of cosmetics involves multiple steps, and traditional manual monitoring methods cannot monitor the transportation process in real time.

[0007] Therefore, there is an urgent need for a dynamic monitoring system and method for cosmetics production based on the entire life cycle to solve the above problems. Summary of the invention

[0008] The purpose of the present invention is to provide a dynamic monitoring system and method for cosmetic production based on the entire life cycle: to solve the technical problems in the existing solutions of raw material safety, production process and transportation process in the cosmetic production process, incomplete monitoring, untimely response, and difficulty in effectively ensuring product quality.

[0009] The purpose of the present invention can be achieved through the following technical solutions:

[0010] On the one hand, a dynamic monitoring method for cosmetics production based on the entire life cycle includes:

[0011] Obtaining raw material information and raw material index information of cosmetics, and performing comparative analysis on the raw material information of cosmetics and the raw material index information of cosmetics to obtain comparative analysis results, and inputting the comparative analysis results into a raw material safety assessment model to output a cosmetic raw material safety index;

[0012] Obtain the cosmetic production monitoring data of each cycle, where the number of cosmetic production monitoring data of each cycle is m, and the cosmetic production monitoring data of n cycles constitute a sample matrix ,in, for dimensional column vector, , through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space Perform principal component analysis to obtain a kernel matrix, calculate the eigenvalue of the kernel matrix, and calculate the square prediction error based on the eigenvalue, and record the square prediction error as the cosmetics production safety index;

[0013] Obtaining transportation information and transportation index information of cosmetics, performing comparative analysis based on the transportation information and transportation index information of cosmetics, and obtaining a cosmetics transportation safety index;

[0014] Generate cosmetics production early warning signals based on the cosmetics raw material safety index, cosmetics production safety index and cosmetics transportation safety index.

[0015] Furthermore, the raw material information of cosmetics and the raw material index information of cosmetics are compared and analyzed to obtain the comparative analysis results, and the comparative analysis results are input into the raw material safety assessment model to output the cosmetic raw material safety index, which specifically includes the following processes:

[0016] Obtain the number of oily raw material types in the raw material information of the cosmetics, obtain the number of oily raw material types that should be present in the raw material index information of the cosmetics, and calculate the ratio A of the number of oily raw material types to the number of oily raw material types that should be present;

[0017] Obtain the number of surfactant types in the raw material information of the cosmetics, obtain the number of surfactant types that should be present in the raw material index information of the cosmetics, and calculate the ratio B of the number of surfactant types to the number of surfactant types that should be present;

[0018] Obtain the number of solvent types in the raw material information of the cosmetics, obtain the number of solvent types that should be present in the raw material index information of the cosmetics, and calculate the ratio C of the number of solvent types to the number of solvent types that should be present;

[0019] Obtain the heavy metal content of the powdery raw materials in the raw material information of the cosmetics, and determine whether the heavy metal content of the powdery raw materials exceeds the heavy metal content threshold of the powdery raw materials in the raw material index information of the cosmetics. If so, the raw material safety factor R of the cosmetics is 0; if not, the raw material safety factor R of the cosmetics is 1;

[0020] The ratio A, ratio B, ratio C, and the cosmetic raw material safety factor R are recorded as the comparative analysis results, and the comparative analysis results are input into the raw material safety assessment model to output the cosmetic raw material safety index:

[0021] ;

[0022] in, Indicates the safety index of cosmetic raw materials. , is the weight coefficient, The content of oily raw materials in cosmetics exceeds the raw material index of cosmetics; The solvent content of the cosmetics exceeds the raw material index of the cosmetics; The content of surfactant in cosmetics exceeds the raw material index of cosmetics; the value of e is 2.72.

[0023] Furthermore, the sample matrix is ​​transformed into Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space The principal component analysis to obtain the kernel matrix specifically includes the following processes:

[0024] Calculate the sample matrix Covariance matrix in high-dimensional linear space :

[0025] ;

[0026] in, represents the centered eigenvector matrix, It is expressed as the mean of the samples after being mapped to the linear space. express The transpose of

[0027] Define the kernel matrix according to the kernel function , where the kernel matrix Each element of for:

[0028] ;

[0029] in, , The kernel matrix The element expression containing sample point i, the kernel matrix The element expression containing the sample point j.

[0030] Furthermore, calculating the eigenvalues ​​of the kernel matrix includes the following process:

[0031] Based on the covariance matrix The following equation is obtained:

[0032] ;

[0033] Among them, the principal element Represents samples in high-dimensional space The corresponding linear combination of mapping points, Represents the covariance matrix The eigenvalue matrix of .

[0034] Furthermore, calculating the square prediction error based on the eigenvalue specifically includes the following process:

[0035] Obtaining kernel matrix eigenvectors based on kernel matrix , the corresponding eigenvalue is ,in, , Represents the number of eigenvectors; and arranges the eigenvalues ​​in descending order: ;

[0036] Calculate the Eigenvalues Contribution :

[0037] ;

[0038] The contribution of the eigenvalues ​​is accumulated in descending order, and it is determined whether the contribution exceeds a preset contribution threshold. The number of eigenvalues ​​corresponding to the preset contribution threshold is recorded as the number of data dimensions H after dimensionality reduction.

[0039] Calculate the squared prediction error :

[0040] ;

[0041] in, , , , , Indicates confidence, Indicates the number of cosmetics production monitoring data in each cycle.

[0042] Furthermore, obtaining the transportation information and transportation index information of the cosmetics, and performing comparative analysis based on the transportation information and transportation index information of the cosmetics to obtain the cosmetics transportation safety index specifically includes the following process:

[0043] Obtain the storage time of the cosmetics in the transportation information of the cosmetics, and query the corresponding fission coefficient in the transportation index information of the cosmetics based on the storage time of the cosmetics, wherein the fission coefficient represents the ability of the cosmetics to resist the impact and extrusion of external forces during transportation, and the longer the storage time, the lower the fission coefficient;

[0044] Obtain the cosmetics stacking height in the transportation information of the cosmetics, and query the corresponding stacking height risk coefficient in the transportation index information of the cosmetics based on the cosmetics stacking height;

[0045] The sum of the fission coefficient and the stacking height risk factor is recorded as the cosmetics transportation safety index.

[0046] Furthermore, the generation of early warning signals for cosmetic production based on the cosmetic raw material safety index specifically includes the following processes:

[0047] The cosmetic raw material safety index threshold is loaded to determine whether the cosmetic raw material safety index exceeds the cosmetic raw material safety index threshold. If so, a cosmetic production warning signal is generated; if not, no cosmetic production warning signal is generated.

[0048] Furthermore, generating a cosmetics production warning signal based on the cosmetics production safety index specifically includes the following processes:

[0049] Load the cosmetics production safety index threshold, determine whether the cosmetics production safety index exceeds the cosmetics production safety index threshold, if so, generate a cosmetics production warning signal, if not, do not generate a cosmetics production warning signal.

[0050] Furthermore, the generation of cosmetics production warning signals based on the cosmetics transportation safety index specifically includes the following processes:

[0051] The cosmetics transportation safety index threshold is loaded to determine whether the cosmetics transportation safety index exceeds the cosmetics transportation safety index threshold. If so, a cosmetics production warning signal is generated; if not, no cosmetics production warning signal is generated.

[0052] On the other hand, the dynamic monitoring system of cosmetic production based on the whole life cycle includes:

[0053] A cosmetic raw material monitoring unit is used to obtain raw material information and raw material index information of cosmetics, and compare and analyze the raw material information of cosmetics and the raw material index information of cosmetics to obtain a comparative analysis result, and input the comparative analysis result into a raw material safety assessment model to output a cosmetic raw material safety index;

[0054] The cosmetic production process monitoring unit is used to obtain the cosmetic production monitoring data of each cycle, wherein the number of cosmetic production monitoring data of each cycle is m, and the cosmetic production monitoring data of n cycles constitutes a sample matrix ,in, for dimensional column vector, , through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space Perform principal component analysis to obtain a kernel matrix, calculate the eigenvalue of the kernel matrix, and calculate the square prediction error based on the eigenvalue, and record the square prediction error as the cosmetics production safety index;

[0055] A cosmetics transportation process monitoring unit, used to obtain the transportation information and transportation index information of the cosmetics, and to obtain a cosmetics transportation safety index based on the comparative analysis of the transportation information and transportation index information of the cosmetics;

[0056] The early warning unit is used to generate a cosmetics production early warning signal based on the cosmetics raw material safety index, the cosmetics production safety index and the cosmetics transportation safety index.

[0057] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0058] The present invention obtains a cosmetic raw material safety index based on cosmetic raw material information and cosmetic raw material index information, performs principal component analysis on cosmetic production monitoring data to obtain a cosmetic production safety index, obtains cosmetic transportation information and cosmetic transportation index information, and performs comparative analysis based on the cosmetic transportation information and the cosmetic transportation index information to obtain a cosmetic transportation safety index; generates a cosmetic production early warning signal based on the cosmetic raw material safety index, the cosmetic production safety index and the cosmetic transportation safety index, can monitor the raw material procurement link, the production link and the transportation link in the cosmetic production process in real time, can promptly discover problems in each link, improve product quality and safety, accurately manage each link in the production process, and ensure that each step meets preset standards.

[0059] Furthermore, the present invention can adopt automated and intelligent monitoring means, which can reduce manual intervention and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0061] Figure 1 is a workflow diagram of a method for dynamic monitoring of cosmetic production based on the entire life cycle according to an embodiment of the present invention;

[0062] Figure 2 is a workflow diagram of another method for dynamic monitoring of cosmetic production based on the entire life cycle according to an embodiment of the present invention;

[0063] Figure 3 This is a system block diagram of a cosmetics production dynamic monitoring system based on the entire life cycle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] 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.

[0065] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0066] This embodiment provides a method for dynamic monitoring of cosmetic production based on the entire life cycle. Figure 1 is a workflow diagram of a method for dynamic monitoring of cosmetic production based on the entire life cycle according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0067] Step S101: obtaining raw material information and raw material index information of cosmetics, and performing comparative analysis on the raw material information and raw material index information of cosmetics to obtain comparative analysis results, and inputting the comparative analysis results into a raw material safety assessment model to output a cosmetic raw material safety index;

[0068] Step S102: Obtain the cosmetic production monitoring data of each cycle, and form a sample matrix with the cosmetic production monitoring data of n cycles , through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space Perform principal component analysis to obtain a kernel matrix, calculate the eigenvalue of the kernel matrix, and calculate the square prediction error based on the eigenvalue, and record the square prediction error as the cosmetics production safety index;

[0069] Specifically, the number of cosmetic production monitoring data in each cycle is m, and the cosmetic production monitoring data of n cycles constitutes a sample matrix ,in, for dimensional column vector, ;

[0070] Step S103: obtaining transportation information and transportation index information of cosmetics, and performing comparative analysis based on the transportation information and transportation index information of cosmetics to obtain a cosmetics transportation safety index;

[0071] Step S104: Generate a cosmetics production warning signal based on the cosmetics raw material safety index, the cosmetics production safety index and the cosmetics transportation safety index.

[0072] In summary, the present invention obtains a cosmetic raw material safety index based on cosmetic raw material information and cosmetic raw material index information, performs principal component analysis on cosmetic production monitoring data to obtain a cosmetic production safety index, obtains cosmetic transportation information and cosmetic transportation index information, and performs comparative analysis based on the cosmetic transportation information and the cosmetic transportation index information to obtain a cosmetic transportation safety index; generates a cosmetic production early warning signal based on the cosmetic raw material safety index, the cosmetic production safety index and the cosmetic transportation safety index, can monitor the raw material procurement link, the production link and the transportation link in the cosmetic production process in real time, can promptly discover problems in each link, improve product quality and safety, accurately manage each link in the production process, and ensure that each step meets the preset standards.

[0073] In some embodiments, Figure 2 is another workflow diagram of a method for dynamic monitoring of cosmetic production based on the entire life cycle according to an embodiment of the present invention, such as Figure 2 As shown, the raw material information of cosmetics and the raw material index information of cosmetics are compared and analyzed to obtain the comparative analysis results, and the comparative analysis results are input into the raw material safety assessment model. The output of the cosmetic raw material safety index specifically includes the following steps:

[0074] Step S201: obtaining the number of oily raw material types in the raw material information of the cosmetics, obtaining the number of oily raw material types that should be present in the raw material index information of the cosmetics, and calculating the ratio A of the number of oily raw material types to the number of oily raw material types that should be present;

[0075] Step S202: Obtain the number of surfactant types in the raw material information of the cosmetics, obtain the number of required surfactant types in the raw material index information of the cosmetics, and calculate the ratio B of the number of surfactant types to the number of required surfactant types;

[0076] Step S203: obtaining the number of solvent types in the raw material information of the cosmetics, obtaining the number of expected solvent types in the raw material index information of the cosmetics, and calculating the ratio C of the number of solvent types to the number of expected solvent types;

[0077] Step S204: obtaining the heavy metal content of the powdery raw materials in the raw material information of the cosmetics, and determining whether the heavy metal content of the powdery raw materials exceeds the heavy metal content threshold of the powdery raw materials in the raw material index information of the cosmetics. If so, the raw material safety factor R of the cosmetics is 0; if not, the raw material safety factor R of the cosmetics is 1;

[0078] Step S205: Record the ratio A, ratio B, ratio C, and the raw material safety factor R of the cosmetics as the comparative analysis results, and input the comparative analysis results into the raw material safety assessment model to output the cosmetic raw material safety index:

[0079] ;

[0080] in, Indicates the safety index of cosmetic raw materials. , is the weight coefficient, The content of oily raw materials in cosmetics exceeds the raw material index of cosmetics; The solvent content of the cosmetics exceeds the raw material index of the cosmetics; The content of surfactant in cosmetics exceeds the raw material index of cosmetics; the value of e is 2.72.

[0081] In some embodiments, the sample matrix is ​​transformed into Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space The principal component analysis to obtain the kernel matrix specifically includes the following processes:

[0082] Calculate the sample matrix Covariance matrix in high-dimensional linear space :

[0083] ;

[0084] in, represents the centered eigenvector matrix, It is expressed as the mean of the samples after being mapped to the linear space. express The transpose of

[0085] Define the kernel matrix according to the kernel function , where the kernel matrix Each element of for:

[0086] ;

[0087] in, , The kernel matrix The element expression containing sample point i, the kernel matrix The element expression containing the sample point j.

[0088] In some embodiments, calculating the eigenvalues ​​of the kernel matrix includes the following process:

[0089] Based on the covariance matrix The following equation is obtained:

[0090] ;

[0091] Among them, the principal element Represents samples in high-dimensional space The corresponding linear combination of mapping points, Represents the covariance matrix The eigenvalue matrix of .

[0092] In some embodiments, calculating the square prediction error based on the eigenvalue specifically includes the following process:

[0093] Obtaining kernel matrix eigenvectors based on kernel matrix , the corresponding eigenvalue is ,in, , Represents the number of eigenvectors; and arranges the eigenvalues ​​in descending order: ;

[0094] Calculate the Eigenvalues Contribution :

[0095] ;

[0096] The contribution of the eigenvalues ​​is accumulated in descending order, and it is determined whether the contribution exceeds a preset contribution threshold. The number of eigenvalues ​​corresponding to the preset contribution threshold is recorded as the number of data dimensions H after dimensionality reduction.

[0097] Calculate the squared prediction error :

[0098] ;

[0099] in, , , , , Indicates confidence, Indicates the number of cosmetics production monitoring data in each cycle.

[0100] In some embodiments, obtaining the transportation information of the cosmetics and the transportation index information of the cosmetics, and performing comparative analysis based on the transportation information of the cosmetics and the transportation index information of the cosmetics to obtain the cosmetics transportation safety index specifically includes the following process:

[0101] Obtain the storage time of the cosmetics in the transportation information of the cosmetics, and query the corresponding fission coefficient in the transportation index information of the cosmetics based on the storage time of the cosmetics, wherein the fission coefficient represents the ability of the cosmetics to resist the impact and extrusion of external forces during transportation, and the longer the storage time, the lower the fission coefficient;

[0102] Obtain the cosmetics stacking height in the transportation information of the cosmetics, and query the corresponding stacking height risk coefficient in the transportation index information of the cosmetics based on the cosmetics stacking height;

[0103] The sum of the fission coefficient and the stacking height risk factor is recorded as the cosmetics transportation safety index.

[0104] In some embodiments, generating a cosmetic production warning signal based on the cosmetic raw material safety index, the cosmetic production safety index, and the cosmetic transportation safety index includes:

[0105] The cosmetic raw material safety index threshold is loaded to determine whether the cosmetic raw material safety index exceeds the cosmetic raw material safety index threshold. If so, a cosmetic production warning signal is generated; if not, no cosmetic production warning signal is generated.

[0106] Load the cosmetics production safety index threshold, determine whether the cosmetics production safety index exceeds the cosmetics production safety index threshold, if so, generate a cosmetics production warning signal, if not, do not generate a cosmetics production warning signal.

[0107] The cosmetics transportation safety index threshold is loaded to determine whether the cosmetics transportation safety index exceeds the cosmetics transportation safety index threshold. If so, a cosmetics production warning signal is generated; if not, no cosmetics production warning signal is generated.

[0108] In some embodiments, Figure 3 is a system block diagram of a cosmetics production dynamic monitoring system based on the entire life cycle according to an embodiment of the present invention, such as Figure 3 As shown, the system includes:

[0109] A cosmetic raw material monitoring unit is used to obtain raw material information and raw material index information of cosmetics, and compare and analyze the raw material information of cosmetics and the raw material index information of cosmetics to obtain a comparative analysis result, and input the comparative analysis result into a raw material safety assessment model to output a cosmetic raw material safety index;

[0110] The cosmetic production process monitoring unit is used to obtain the cosmetic production monitoring data of each cycle, wherein the number of cosmetic production monitoring data of each cycle is m, and the cosmetic production monitoring data of n cycles constitutes a sample matrix ,in, for dimensional column vector, , through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space Perform principal component analysis to obtain a kernel matrix, calculate the eigenvalue of the kernel matrix, and calculate the square prediction error based on the eigenvalue, and record the square prediction error as the cosmetics production safety index;

[0111] A cosmetics transportation process monitoring unit, used to obtain the transportation information and transportation index information of the cosmetics, and to obtain a cosmetics transportation safety index based on the comparative analysis of the transportation information and transportation index information of the cosmetics;

[0112] The early warning unit is used to generate a cosmetics production early warning signal based on the cosmetics raw material safety index, the cosmetics production safety index and the cosmetics transportation safety index.

[0113] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 method for dynamic monitoring of cosmetic production based on the entire life cycle, characterized in that: Methods include: Obtaining raw material information and raw material index information of cosmetics, and performing comparative analysis on the raw material information and raw material index information of cosmetics to obtain comparative analysis results, and inputting the comparative analysis results into a raw material safety assessment model to output a cosmetic raw material safety index; Obtain the cosmetic production monitoring data of each cycle, where the number of cosmetic production monitoring data of each cycle is m, and the cosmetic production monitoring data of n cycles constitute a sample matrix ,in, for dimensional column vector, , through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space Perform principal component analysis to obtain a kernel matrix, calculate the eigenvalue of the kernel matrix, and calculate the square prediction error based on the eigenvalue, and record the square prediction error as the cosmetics production safety index; Obtaining transportation information and transportation index information of cosmetics, performing comparative analysis based on the transportation information and transportation index information of cosmetics, and obtaining a cosmetics transportation safety index; Generate early warning signals for cosmetic production based on the cosmetic raw material safety index, cosmetic production safety index and cosmetic transportation safety index; Comparative analysis is performed on the raw material information of cosmetics and the raw material index information of cosmetics to obtain comparative analysis results, and the comparative analysis results are input into the raw material safety assessment model to output the cosmetic raw material safety index. The specific process includes the following: Obtain the number of oily raw material types in the raw material information of the cosmetics, obtain the number of oily raw material types that should be present in the raw material index information of the cosmetics, and calculate the ratio A of the number of oily raw material types to the number of oily raw material types that should be present; Obtain the number of surfactant types in the raw material information of the cosmetics, obtain the number of surfactant types that should be present in the raw material index information of the cosmetics, and calculate the ratio B of the number of surfactant types to the number of surfactant types that should be present; Obtain the number of solvent types in the raw material information of the cosmetics, obtain the number of solvent types that should be present in the raw material index information of the cosmetics, and calculate the ratio C of the number of solvent types to the number of solvent types that should be present; Obtain the heavy metal content of the powdery raw materials in the raw material information of the cosmetics, and determine whether the heavy metal content of the powdery raw materials exceeds the heavy metal content threshold of the powdery raw materials in the raw material index information of the cosmetics. If so, the raw material safety factor R of the cosmetics is 0; if not, the raw material safety factor R of the cosmetics is 1; The ratio A, ratio B, ratio C, and the cosmetic raw material safety factor R are recorded as the comparative analysis results, and the comparative analysis results are input into the raw material safety assessment model to output the cosmetic raw material safety index: ； in, Indicates the safety index of cosmetic raw materials. , is the weight coefficient, The content of oily raw materials in cosmetics exceeds the raw material index of cosmetics; The solvent content of the cosmetics exceeds the raw material index of the cosmetics; The content of surfactant in cosmetics exceeds the raw material index of cosmetics; the value of e is 2.72; Obtaining the transportation information and transportation index information of cosmetics, and performing comparative analysis based on the transportation information and transportation index information of cosmetics to obtain the cosmetics transportation safety index specifically includes the following processes: Obtain the storage time of the cosmetics in the transportation information of the cosmetics, and query the corresponding fission coefficient in the transportation index information of the cosmetics based on the storage time of the cosmetics, wherein the fission coefficient represents the ability of the cosmetics to resist the impact and extrusion of external forces during transportation, and the longer the storage time, the lower the fission coefficient; Obtain the cosmetics stacking height in the transportation information of the cosmetics, and query the corresponding stacking height risk coefficient in the transportation index information of the cosmetics based on the cosmetics stacking height; The sum of the fission coefficient and the stacking height risk factor is recorded as the cosmetics transportation safety index.

2. The method for dynamic monitoring of cosmetic production based on the entire life cycle according to claim 1, characterized in that: Through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space The principal component analysis to obtain the kernel matrix specifically includes the following processes: Calculate the sample matrix Covariance matrix in high-dimensional linear space : ； in, represents the centered eigenvector matrix, It is expressed as the mean of the samples after being mapped to the linear space. express The transpose of Define the kernel matrix according to the kernel function , where the kernel matrix Each element of for: ； in, , The kernel matrix The element expression containing sample point i, the kernel matrix The element expression containing the sample point j.

3. The method for dynamic monitoring of cosmetic production based on the entire life cycle according to claim 2, characterized in that: Calculating the eigenvalues ​​of the kernel matrix includes the following process: Based on the covariance matrix The following equation is obtained: ； Among them, the principal element Represents samples in high-dimensional space The corresponding linear combination of mapping points, Represents the covariance matrix The eigenvalue matrix of .

4. The method for dynamic monitoring of cosmetic production based on the entire life cycle according to claim 3, characterized in that: The calculation of square prediction error based on eigenvalues ​​specifically includes the following process: Obtaining kernel matrix eigenvectors based on kernel matrix , the corresponding eigenvalue is ,in, , Represents the number of eigenvectors; and arranges the eigenvalues ​​in descending order: ; Calculate the Eigenvalues Contribution : ； The contribution of the eigenvalues ​​is accumulated in descending order, and it is determined whether the contribution exceeds a preset contribution threshold. The number of eigenvalues ​​corresponding to the preset contribution threshold is recorded as the number of data dimensions H after dimensionality reduction. Calculate the squared prediction error : ； in, , , , , Indicates confidence, Indicates the number of cosmetics production monitoring data in each cycle.

5. The method for dynamic monitoring of cosmetic production based on the entire life cycle according to claim 1, characterized in that: Generate early warning signals for cosmetic production based on the cosmetic raw material safety index The process includes: The cosmetic raw material safety index threshold is loaded to determine whether the cosmetic raw material safety index exceeds the cosmetic raw material safety index threshold. If so, a cosmetic production warning signal is generated; if not, no cosmetic production warning signal is generated.

6. The method for dynamic monitoring of cosmetic production based on the entire life cycle according to claim 1, characterized in that: Generate cosmetics production early warning signals based on cosmetics production safety index The process includes: Load the cosmetics production safety index threshold, determine whether the cosmetics production safety index exceeds the cosmetics production safety index threshold, if so, generate a cosmetics production warning signal, if not, do not generate a cosmetics production warning signal.

7. The method for dynamic monitoring of cosmetic production based on the entire life cycle according to claim 1, characterized in that: Generate cosmetics production early warning signals based on cosmetics transportation safety index The process includes: The cosmetics transportation safety index threshold is loaded to determine whether the cosmetics transportation safety index exceeds the cosmetics transportation safety index threshold. If so, a cosmetics production warning signal is generated; if not, no cosmetics production warning signal is generated.

8. A dynamic monitoring system for cosmetic production based on the entire life cycle, characterized in that: The method for dynamic monitoring of cosmetic production based on the entire life cycle is applicable to any one of claims 1 to 7, the system comprising: A cosmetic raw material monitoring unit is used to obtain raw material information and raw material index information of cosmetics, and compare and analyze the raw material information of cosmetics and the raw material index information of cosmetics to obtain a comparative analysis result, and input the comparative analysis result into a raw material safety assessment model to output a cosmetic raw material safety index; The cosmetic production process monitoring unit is used to obtain the cosmetic production monitoring data of each cycle, wherein the number of cosmetic production monitoring data of each cycle is m, and the cosmetic production monitoring data of n cycles constitutes a sample matrix ,in, for dimensional column vector, , through nonlinear mapping, the sample matrix Mapped into a high-dimensional linear space, and the sample matrix in the high-dimensional linear space Perform principal component analysis to obtain a kernel matrix, calculate the eigenvalue of the kernel matrix, and calculate the square prediction error based on the eigenvalue, and record the square prediction error as the cosmetics production safety index; A cosmetics transportation process monitoring unit, used to obtain the transportation information and transportation index information of the cosmetics, and to obtain a cosmetics transportation safety index based on the comparative analysis of the transportation information and transportation index information of the cosmetics; The early warning unit is used to generate a cosmetics production early warning signal based on the cosmetics raw material safety index, the cosmetics production safety index and the cosmetics transportation safety index.

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