Quantitative evaluation method and system for safety of buccal product
By using risk matrix analysis and analytic hierarchy process to conduct multi-factor safety assessments of oral products, the problem of strong subjectivity in assessment results in existing technologies has been solved, and scientific and quantitative safety assessment and quality control have been achieved.
Patent Information
- Application Number
- CN202510744003.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-04
- Publication Date
- 2025-10-28
AI Technical Summary
Existing technologies lack comprehensive, scientific, and quantitative methods for assessing the safety of oral products, making it difficult to objectively measure the interactions between different safety factors. This results in highly subjective assessment results and makes it difficult to form quantifiable risk levels.
Risk matrix analysis was used to classify and score different safety factors, and the weight allocation was optimized by combining the analytic hierarchy process. By obtaining the detection data of the target sample, a multi-factor scientific quantitative assessment was carried out, including weighted processing of indicators such as formulation safety, heavy metal safety, physicochemical safety and cytotoxicity.
It enables multi-factor scientific quantitative evaluation of oral products, resulting in more accurate evaluation results, clear classification of product safety levels, and benefits from product quality control.
Smart Images
Figure CN120851676A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of food safety assessment technology, specifically to a quantitative assessment method and system for the safety of oral products. Background Technology
[0002] In the field of safety assessment of oral tobacco products, there is currently a lack of a comprehensive, scientific, and quantitative risk assessment methodology. Existing assessment methods mainly rely on the detection of single indicators, such as heavy metal content, formulation safety, or cytotoxicity testing, but lack a systematic and comprehensive assessment framework. Furthermore, traditional assessment methods struggle to objectively measure the interactions between different safety factors, leading to highly subjective assessment results and making it difficult to establish quantifiable risk levels. Particularly for complex safety data, existing methods lack effective mathematical models for weight allocation and risk grading, making it difficult for assessment results to support precise decisions in product development and quality control. Summary of the Invention
[0003] This application provides a quantitative assessment method and system for the safety of oral products, which effectively solves the problem that existing technologies rely on a single safety performance assessment index for oral products and cannot achieve a balance between different risk factors.
[0004] In one embodiment, a method for quantitatively assessing the safety of oral products is provided, comprising:
[0005] Obtain detection data for the target sample;
[0006] The data to be estimated is obtained based on the detection data, wherein the data to be estimated includes several types of first-level risk data and several types of second-level risk data;
[0007] The data to be estimated is compared and analyzed with a preset evaluation module to obtain a data risk module. The preset evaluation module includes a first preset evaluation indicator and a second preset evaluation indicator. The first preset evaluation indicator includes a first preset risk indicator corresponding to each type of the first data to be estimated risk. The second preset evaluation indicator includes a second preset risk indicator corresponding to each type of the second data to be estimated risk. The data risk module includes a first data risk and a second data risk. The first data risk includes a first sub-data risk obtained by comparing and analyzing each type of the first data to be estimated risk with the corresponding first preset risk indicator. The second data risk includes a second sub-data risk obtained by comparing and analyzing each type of the second data to be estimated risk with the corresponding second preset risk indicator.
[0008] The data risk module is weighted to obtain the quantitative safety risk of the target sample.
[0009] Furthermore, the preset assessment module includes several assessment sub-modules, and each assessment sub-module includes several first preset risk indicators and several second preset risk indicators;
[0010] The data risk module includes a number of risk sub-modules equal to the number of evaluation sub-modules, wherein the weighting of the data risk module includes weighting the risk sub-modules.
[0011] Furthermore, the preset evaluation module includes a formulation safety submodule, a heavy metal safety submodule, a physicochemical safety submodule, and a cytotoxicity submodule;
[0012] The formulation safety submodule, the heavy metal safety submodule, the physicochemical safety submodule, and the cytotoxicity submodule each independently include a number of first preset risk indicators and a number of second preset risk indicators.
[0013] Furthermore, obtaining the data to be estimated based on the detection data includes:
[0014] The estimated data can be obtained directly from the detection data, and / or the estimated data can be obtained by statistical analysis or calculation of the detection data.
[0015] Furthermore, the data risk module is weighted to obtain the quantitative safety risk of the target sample, including:
[0016] The first quantitative comprehensive index is obtained by weighting each type of the first sub-data risk according to the preset weights, and the second quantitative comprehensive index is obtained by weighting each type of the second sub-data risk.
[0017] The quantitative safety risk of the target sample is obtained based on the first and second quantitative comprehensive indicators.
[0018] Furthermore, the quantitative evaluation method also includes:
[0019] A consistency check is performed on the preset weights to obtain the check results;
[0020] Based on the test results, determine whether the preset weights need to be updated.
[0021] Furthermore, when it is determined based on the test results that the preset weights need to be updated, updating the preset weights includes:
[0022] Establish a weight matrix based on the data risk module;
[0023] The weight matrix is standardized to obtain the standardized matrix;
[0024] The new weights are calculated based on the standardized matrix to obtain the updated weights.
[0025] Furthermore, when the target samples include multiple samples, a two-dimensional coordinate scatter plot is established and output based on the first and second quantitative comprehensive indices of each target sample.
[0026] In one embodiment, a quantitative assessment system for the safety of oral products is provided, comprising:
[0027] The acquisition module is used to acquire the detection data of the target sample;
[0028] The data processing module is used to obtain the data to be estimated based on the detection data, wherein the data to be estimated includes several types of first risk data to be estimated and several types of second risk data to be estimated.
[0029] A comparison module is used to compare and analyze the data to be estimated with a preset evaluation module to obtain a data risk module. The preset evaluation module includes a first preset evaluation indicator and a second preset evaluation indicator. The first preset evaluation indicator includes a first preset risk indicator corresponding to each type of the first data to be estimated risk. The second preset evaluation indicator includes a second preset risk indicator corresponding to each type of the second data to be estimated risk. The data risk module includes a first data risk and a second data risk. The first data risk includes a first sub-data risk obtained by comparing and analyzing each type of the first data to be estimated risk with the corresponding first preset risk indicator. The second data risk includes a second sub-data risk obtained by comparing and analyzing each type of the second data to be estimated risk with the corresponding second preset risk indicator.
[0030] The risk assessment module is used to weight the data risk module to obtain the quantitative safety risk of the target sample.
[0031] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, the computer program being executable by a processor to implement the method described above.
[0032] According to the above-described embodiment, a method / system for quantitatively assessing the safety of oral products involves acquiring detection data of a target sample, obtaining several types of first-assessed risk data and several types of second-assessed risk data based on this monitoring data, and comparing and analyzing the first-assessed risk data and second-assessed risk data with a preset assessment module to obtain a data risk module. Then, the data risk module is weighted to obtain the quantitative safety risk of the target sample. By adopting the solution of this application, a multi-factor scientific quantitative assessment is achieved, resulting in a more comprehensive assessment and a clearer classification of the product's safety level, which is beneficial for product quality control. Attached Figure Description
[0033] Figure 1 A flowchart illustrating a method for quantitatively assessing the safety of oral products provided in this embodiment;
[0034] Figure 2 A flowchart for quantifying the safety risks of obtaining the target sample provided in this embodiment;
[0035] Figure 3 Another flowchart for the quantitative assessment method of the safety of oral products provided in this embodiment;
[0036] Figure 4 This is a flowchart of updating the preset weights provided in this embodiment;
[0037] Figure 5 A two-dimensional scatter plot of the quantitative comprehensive index of the target sample provided in this embodiment;
[0038] Figure 6 This is a structural block diagram of the oral product safety quantitative assessment system provided in this embodiment.
[0039] Attached reference numerals: 10, Acquisition module; 20, Data processing module; 30, Comparison module; 40, Risk assessment module. Detailed Implementation
[0040] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.
[0041] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.
[0042] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).
[0043] Currently, the commonly used safety assessment method in the industry is the single-index assessment method: it assesses product safety solely based on whether the heavy metal content exceeds the standard, without considering other key factors (such as physicochemical properties, formulation ingredients, cytotoxicity, etc.). Therefore, existing methods cannot provide a holistic risk assessment and easily overlook the trade-offs between different risk factors. In view of this, this application proposes a quantitative assessment method and system for the safety of oral products to achieve a scientific quantitative assessment of multiple factors. It classifies and scores different safety factors using risk matrix analysis and combines this with the Analytic Hierarchy Process (AHP) to optimize weight allocation, making the assessment results more accurate. Specific implementation methods are described below.
[0044] Before implementing the quantitative assessment method of this application, it is necessary to establish and determine the preset assessment module, which includes various preset assessment indicators and a preset data risk module. The preset data risk module includes preset risk indicators and their level assessment tables.
[0045] The evaluation parameters for various preset evaluation indicators are shown in Table 1 below:
[0046]
[0047]
[0048] Table 1
[0049] The pre-defined risk indicators and their level assessment tables include: a severity assessment logic table (as shown in Table 2) and a probability assessment logic table (as shown in Table 3), as detailed below:
[0050]
[0051]
[0052] Table 2
[0053]
[0054]
[0055] Table 3
[0056] refer to Figure 1 This embodiment provides a method for quantitatively assessing the safety of oral products, comprising the following steps:
[0057] Step 100: The acquisition module acquires the detection data of the target sample.
[0058] Specifically, the acquisition module obtains the detection data of the target sample. This data includes qualitative detection data from GC-MS and quantitative detection data from ICP-MS of various heavy metal contents, pH value, water activity, and other parameters. Based on this data, various risk assessment data can be obtained directly or indirectly for evaluating probability and severity. Furthermore, the various risk assessment data for the target sample include multiple indicators from formulation safety, heavy metal safety, physicochemical safety, and cytotoxicity.
[0059] In practical applications, the acquisition module will separately acquire the detection data corresponding to the formulation safety indicators, heavy metal safety indicators, physicochemical safety indicators, and cytotoxicity indicators. By comprehensively considering the formulation, heavy metals, physicochemical properties, and cytotoxicity, a multi-dimensional assessment is achieved, overcoming the limitations of traditional methods that rely on a single indicator. The specific acquired detection data are shown in Table 1 above.
[0060] Step 200: The data processing module obtains the data to be estimated based on the detection data, which includes several types of first-level risk data and several types of second-level risk data.
[0061] In practical applications, the data to be estimated can be obtained directly from the test data, or it can be obtained after statistical analysis or calculation of the test data. Specifically, in the aforementioned test data, the first risk data to be estimated includes: the potential risk data for formula safety indicators, the potential risk data for heavy metal safety indicators, the potential risk data for physicochemical safety indicators, and the potential risk data for cytotoxicity indicators; the second risk data to be estimated includes: the severity risk data for formula safety indicators, the severity risk data for heavy metal safety indicators, the severity risk data for physicochemical safety indicators, and the severity risk data for cytotoxicity indicators.
[0062] Step 300: The comparison module compares and analyzes the data to be estimated with the preset evaluation module to obtain the data risk module. The preset evaluation module includes a first preset evaluation indicator and a second preset evaluation indicator. The first preset evaluation indicator includes a first preset risk indicator corresponding to each type of first risk data to be estimated. The second preset evaluation indicator includes a second preset risk indicator corresponding to each type of second risk data to be estimated. The data risk module includes a first data risk and a second data risk. The first data risk includes a first sub-data risk obtained by comparing and analyzing each type of first risk data to be estimated with the corresponding first preset risk indicator. The second data risk includes a second sub-data risk obtained by comparing and analyzing each type of second risk data to be estimated with the corresponding second preset risk indicator.
[0063] In this step, the pre-set assessment module includes several assessment sub-modules, each of which includes several first pre-set risk indicators and several second pre-set risk indicators. In practical applications, the first pre-set risk indicators are specifically probability indicators, and the second pre-set risk indicators are specifically severity indicators. Furthermore, the pre-set assessment module also includes a formulation safety sub-module, a heavy metal safety sub-module, a physicochemical safety sub-module, and a cytotoxicity sub-module; and each of these sub-modules independently includes several first pre-set risk indicators and several second pre-set risk indicators.
[0064] In other words, the probability indicators include a first pre-set risk indicator (probability indicator) corresponding to each assessment sub-module, and the severity indicators include a second pre-set risk indicator (severity indicator) corresponding to each assessment sub-module. Specifically, in the pre-set assessment modules, the probability indicators include: the probability of the formulation safety sub-module, the probability of the heavy metal safety sub-module, the probability of the physicochemical safety sub-module, and the probability of the cytotoxicity sub-module; the severity indicators include: the severity of the formulation safety sub-module, the severity of the heavy metal safety sub-module, the severity of the physicochemical safety sub-module, and the severity of the cytotoxicity sub-module.
[0065] In practical applications, each type of first risk data to be estimated is compared and analyzed with the corresponding first preset risk indicator to obtain the probability level score of each data risk; each type of second risk data to be estimated is compared and analyzed with the corresponding second preset risk indicator to obtain the severity level score of each data risk.
[0066] For example, the detection data of 6 sets of samples were obtained, as shown in Table 4 below:
[0067]
[0068] Table 4
[0069] Then, based on the data in Table 4, the severity and probability of each estimated risk data (formula safety, heavy metal safety, physicochemical safety, and cytotoxicity) for each sample group were analyzed. The specific analysis principle is as follows:
[0070] Formula safety (CMR substances):
[0071] Severity: Based on the proportion of CMR substances, calculate the percentage of CMR substances in the composition and assign a corresponding severity score (1-4 points).
[0072] Probability: Assess the probability (1-4 points) based on the presence of Class 1A, 1B, and 2 components in the GCMS report.
[0073] Heavy metal safety:
[0074] Severity: The severity score (1-4 points) is assigned based on whether the heavy metal content exceeds the standard and the amount exceeding the standard.
[0075] Probability: Assess the probability (1-4 points) based on the ratio of the content of each heavy metal to the limit.
[0076] Physical and chemical safety:
[0077] pH value: Assess the severity and likelihood based on the pH range (1-4 points).
[0078] Water activity: Assess severity and likelihood based on the water activity range (1-4 points).
[0079] The average of the severity and likelihood assessments based on pH and water activity is taken as the result of the severity and likelihood of physicochemical safety (1-4 points).
[0080] Cytotoxicity:
[0081] Severity: The severity is assessed based on the average survival rate of the three cell types (1-4 points).
[0082] Probability: Assess the probability (1-4 points) based on the number of cells with a survival rate of less than 60%.
[0083] Based on the test data of the above samples, they were compared with the preset level assessment table to determine the severity level score and probability level score of each risk data to be estimated for the samples in Table 4 above, as shown in Table 5 below:
[0084]
[0085] Table 5
[0086] After obtaining the severity score and probability score of each risk data to be estimated for the above 6 groups of samples, the severity level and probability level of each sub-risk data of the sample group can be obtained by referring to the preset level assessment table.
[0087] Step 400: The risk assessment module performs weighted processing on the data risk module to obtain the quantitative safety risk of the target sample.
[0088] The data risk module includes a number of risk sub-modules equal to the number of assessment sub-modules. The data risk module is weighted, which includes weighting the risk sub-modules. Each assessment sub-module corresponds to one risk sub-module. Specifically, for example... Figure 2 As shown, the risk submodule is weighted to obtain the quantitative safety risk of the target sample, including:
[0089] Step 410: Weight each type of first sub-data risk according to preset weights to obtain the first quantitative comprehensive index, and weight each type of second sub-data risk to obtain the second quantitative comprehensive index.
[0090] In practical applications, the importance of various risk sub-modules of the target sample relative to the overall assessment result is determined independently based on the actual security control needs of the enterprise and conventional industry experience. Therefore, each assessment sub-module corresponds to a preset weight relative to the preset assessment module, and this preset weight can be determined independently based on the actual security control needs of the enterprise and conventional industry experience.
[0091] Specifically, a risk matrix method is used to construct a pairwise comparison matrix using two-dimensional coordinates (probability of risk occurrence and severity of risk occurrence). For example, if the severity of formulation safety is considered more important than the probability of cytotoxicity, then a higher score is given to it in the comparison. Based on the enterprise's safety control needs and general industry experience, the following pairwise comparison matrix of the relative importance of formulation safety, heavy metal safety, physicochemical safety, and cytotoxicity is obtained:
[0092] Standard comparison Formula safety Heavy metal safety Physical and chemical safety Cytotoxicity Formula safety 1.00 3.00 5.00 5.00 Heavy metal safety 0.33 1.00 3.00 3.00 Physical and chemical safety 0.20 0.33 1.00 2.00 Cytotoxicity 0.20 0.33 0.50 1.00
[0093] Table 6
[0094] The above pairwise comparison matrices are standardized by dividing each element in Table 6 by the sum of the elements in that column to standardize each element. The resulting standardized matrix is shown in Table 7 below.
[0095]
[0096]
[0097] Table 7
[0098] Specifically, the analytic hierarchy process (AHP) is used to calculate the weights of the standardized matrix above to obtain the weight values of each assessment sub-module, thereby improving the scientific nature of the assessment, ensuring that the weight allocation of different safety factors conforms to the actual degree of harm, and making the assessment results more reasonable.
[0099] Calculate the weights in Table 7: Average the values in each row to obtain the preset weight value corresponding to each type of risk data to be estimated.
[0100]
[0101] After obtaining the preset weight value for each type of risk data to be estimated, the first quantitative comprehensive index is obtained by weighting each type of first sub-data risk according to the preset weight value, and the second quantitative comprehensive index is obtained by weighting each type of second sub-data risk.
[0102] Specifically, the risk of each second sub-data point for each type of risk data to be estimated is calculated separately. That is, the severity level score of each type of risk data to be estimated is calculated by multiplying it by the corresponding preset weight value to obtain the severity weighted score of each second sub-data point risk; then the severity weighted scores of each second sub-data point risk are added together to obtain the first quantitative comprehensive index.
[0103] Calculate the risk of each first sub-data point for each type of risk data to be estimated. That is, calculate the product of the probability level score of each type of risk data and the corresponding preset weight value to obtain the probability weighted score of each first sub-data point risk; then add the probability weighted scores of each first sub-data point risk to obtain the second quantitative comprehensive index.
[0104] The formula for calculating the first quantitative comprehensive index X is:
[0105] X=(X1×0.5506)+(X2×0.2485)+(X3×0.1184)+(X4×0.0825);
[0106] The formula for calculating the second quantitative comprehensive index Y is:
[0107] Y = (Y1 × 0.5506) + (Y2 × 0.2485) + (Y3 × 0.1184) + (Y4 × 0.0825);
[0108] Where, X1 is the formula safety severity, X2 is the heavy metal safety severity, X3 is the physical and chemical safety severity, X4 is the cytotoxicity severity, Y1 is the formula safety possibility, Y2 is the heavy metal safety possibility, Y3 is the physical and chemical safety possibility, and Y4 is the cytotoxicity possibility.
[0109] Taking the sample data in Table 4 and Table 5 above as an example, according to the above calculation formula, the first quantitative comprehensive index and the second quantitative comprehensive index of the above 6 groups of samples are obtained as shown in Table 8 below:
[0110]
[0111] Table 8
[0112] Step 420: Obtain the quantitative safety risk of the target sample based on the first quantitative comprehensive index and the second quantitative comprehensive index.
[0113] Specifically, compare the first quantitative comprehensive index and / or the second quantitative comprehensive index with the preset risk index and its grade evaluation table to determine that the quantitative safety risk of the target sample is low risk, medium risk or high risk.
[0114] The preset risk grade evaluation table is:
[0115] Low risk: X ≤ 2 and Y ≤ 2;
[0116] Medium risk: 2 < X ≤ 3 and 2 < Y ≤ 3;
[0117] High risk: X > 3 and Y > 3.
[0118] Compare the comprehensive severity and comprehensive possibility of each group of samples in Table 8 with the above preset risk grade evaluation to obtain the final comprehensive evaluation result and output it.
[0119] Furthermore, when there are multiple target samples, establish a two-dimensional coordinate scatter plot based on the first quantitative comprehensive index and the second quantitative comprehensive index of each target sample and output it.
[0120] When the sample size is large, that is, for the detection data of multiple groups of samples, a two-dimensional scatter plot can be drawn based on the first quantitative comprehensive index and the second quantitative comprehensive index for visual analysis, which can provide an intuitive safety evaluation result for the quality or safety department to make a quick decision. For example Figure 5 For the two-dimensional scatter plot corresponding to Table 8, it can be quickly seen that the safety risk of KHOD000341 in this batch of samples is the highest, and the risk of KHOD000344 is the lowest.
[0121] Furthermore, before weighting the risk sub-modules according to preset weights, the preset weights need to be verified, referring to... Figure 3 Specifically, it includes the following steps:
[0122] Step 500: Perform a consistency check on the preset weights and obtain the check results.
[0123] Specifically, to ensure consistency in judgments, a consistency check needs to be performed on the preset weights. First, the consistency index CI is calculated:
[0124]
[0125] Where, λ max Let be the largest eigenvalue of the normalized matrix, and n be the order of the normalized matrix.
[0126] After obtaining the consistency index, calculate the consistency ratio CR:
[0127]
[0128] Here, RI is the random consistency index, which is 0.9 when n=4. It should be noted that RI is calculated through numerous simulation experiments (usually using Monte Carlo simulations), simulating the average CI value of the largest eigenvalue λ of an n-order random consistency matrix. RI depends only on the order n of the matrix; each n corresponds to a unique RI value.
[0129] In practical applications, the consistency ratio (CR) is compared to a preset value of 0.1. If CR < 0.1, the consistency of the established standardized matrix is considered acceptable. If CR > 0.1, it indicates significant inconsistency in the established standardized matrix, requiring adjustment of the pairwise comparison matrix and re-establishment of the standardized matrix. The purpose of this adjustment is to make the logic of the pairwise comparisons more consistent, thereby improving the reliability of the judgment. Specifically, this requires a reassessment of the relative importance of risks as defined internally by the company.
[0130] Step 600: Determine whether the preset weights need to be updated based on the test results.
[0131] As mentioned above, when the calculated CR > 0.1, the preset weights need to be updated, refer to... Figure 4 The specific update method includes the following steps:
[0132] Step 610: Establish a weight matrix based on the data risk module.
[0133] Step 620: Standardize the weight matrix to obtain the standardized matrix.
[0134] Step 630: Calculate the new weights based on the standardized matrix to obtain the updated weights.
[0135] Specifically, based on the enterprise's security control needs and general industry experience, the standardized matrix corresponding to the current preset weights is adjusted to establish a new weight matrix. Then, the new weight matrix is standardized. The method of standardizing the matrix and calculating the new weights based on the standardized matrix is the same as the method of establishing the standardized matrix and calculating the weights described above. For details, please refer to the above method. This embodiment will not elaborate further here.
[0136] like Figure 6 As shown in the figure, this embodiment provides a quantitative assessment system for the safety of oral products, including an acquisition module 10, a data processing module 20, a comparison module 30, and a risk assessment module 40. The system includes: an acquisition module 10 for acquiring detection data of the target sample; a data processing module 20 for obtaining estimated data based on the detection data, wherein the estimated data includes several types of first estimated risk data and several types of second estimated risk data; a comparison module 30 for comparing and analyzing the estimated data with a preset evaluation module to obtain a data risk module, wherein the preset evaluation module includes a first preset evaluation index and a second preset evaluation index, the first preset evaluation index including a first preset risk index corresponding to each type of first estimated risk data, the second preset evaluation index including a second preset risk index corresponding to each type of second estimated risk data, the data risk module including a first data risk and a second data risk, the first data risk including a first sub-data risk obtained by comparing and analyzing each type of first estimated risk data with the corresponding first preset risk index, and the second data risk including a second sub-data risk obtained by comparing and analyzing each type of second estimated risk data with the corresponding second preset risk index; and a risk assessment module 40 for weighting the data risk module to obtain the quantitative safety risk of the target sample.
[0137] The quantitative assessment system for the safety of oral products in this embodiment acquires test data of the target sample through the acquisition module 10. Then, the data processing module 20 obtains several types of first and second risk assessment data based on the monitoring data. The comparison module 30 compares and analyzes the first and second risk assessment data with a preset assessment module to obtain a data risk module. Finally, the risk assessment module 40 performs weighted processing on the data risk module to obtain the quantitative safety risk of the target sample. By adopting the quantitative assessment system of this application, a multi-factor scientific quantitative assessment is achieved, resulting in more comprehensive assessment results and a clearer classification of product safety levels, which is beneficial for product quality control.
[0138] Since the acquisition module 10, data processing module 20, comparison module 30 and risk assessment module 40 have been described in detail in the above embodiments, they will not be described in detail here.
[0139] This embodiment provides a computer-readable storage medium storing a computer program that can be executed by a processor to implement the method described above. Since the above embodiments have already described in detail a method for quantitatively assessing the safety of inhaled cigarettes, this embodiment will not repeat those details further.
[0140] This embodiment provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the method described above. Since the above embodiments have already described in detail a method for quantitatively assessing the safety of inhaled cigarettes, this embodiment will not elaborate further.
[0141] Those skilled in the art will understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a server, another computer, disk, optical disk, flash drive, or external hard drive, etc., and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.
[0142] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.
Claims
1. A method for quantitatively assessing the safety of oral products, characterized in that, include: Obtain detection data for the target sample; The data to be estimated is obtained based on the detection data, wherein the data to be estimated includes several types of first-level risk data and several types of second-level risk data; The data to be estimated is compared and analyzed with a preset evaluation module to obtain a data risk module. The preset evaluation module includes a first preset evaluation indicator and a second preset evaluation indicator. The first preset evaluation indicator includes a first preset risk indicator corresponding to each type of the first data to be estimated risk. The second preset evaluation indicator includes a second preset risk indicator corresponding to each type of the second data to be estimated risk. The data risk module includes a first data risk and a second data risk. The first data risk includes a first sub-data risk obtained by comparing and analyzing each type of the first data to be estimated risk with the corresponding first preset risk indicator. The second data risk includes a second sub-data risk obtained by comparing and analyzing each type of the second data to be estimated risk with the corresponding second preset risk indicator. The data risk module is weighted to obtain the quantitative safety risk of the target sample.
2. The quantitative evaluation method according to claim 1, characterized in that, The preset assessment module includes several assessment sub-modules, and each assessment sub-module includes several first preset risk indicators and several second preset risk indicators; The data risk module includes a number of risk sub-modules equal to the number of evaluation sub-modules, wherein the weighting of the data risk module includes weighting the risk sub-modules.
3. The quantitative evaluation method according to claim 1, characterized in that, The preset evaluation module includes a formulation safety submodule, a heavy metal safety submodule, a physicochemical safety submodule, and a cytotoxicity submodule; The formulation safety submodule, the heavy metal safety submodule, the physicochemical safety submodule, and the cytotoxicity submodule each independently include a number of first preset risk indicators and a number of second preset risk indicators.
4. The quantitative evaluation method according to claim 1, characterized in that, The step of obtaining the estimated data based on the detection data includes: The estimated data can be obtained directly from the detection data, and / or the estimated data can be obtained by statistical analysis or calculation of the detection data.
5. The quantitative evaluation method according to claim 1, characterized in that, The data risk module is weighted to obtain the quantitative safety risk of the target sample, including: The first quantitative comprehensive index is obtained by weighting each type of the first sub-data risk according to the preset weights, and the second quantitative comprehensive index is obtained by weighting each type of the second sub-data risk. The quantitative safety risk of the target sample is obtained based on the first and second quantitative comprehensive indicators.
6. The quantitative evaluation method according to claim 5, characterized in that, Also includes: A consistency check is performed on the preset weights to obtain the check results; Based on the test results, determine whether the preset weights need to be updated.
7. The quantitative evaluation method according to claim 6, characterized in that, When it is determined based on the test results that the preset weights need to be updated, updating the preset weights includes: Establish a weight matrix based on the data risk module; The weight matrix is standardized to obtain the standardized matrix; The new weights are calculated based on the standardized matrix to obtain the updated weights.
8. The quantitative evaluation method according to claim 5, characterized in that, When there are multiple target samples, a two-dimensional coordinate scatter plot is established and output based on the first and second quantitative comprehensive indices of each target sample.
9. A quantitative assessment system for the safety of oral products, characterized in that, include: The acquisition module is used to acquire the detection data of the target sample; The data processing module is used to obtain the data to be estimated based on the detection data, wherein the data to be estimated includes several types of first risk data to be estimated and several types of second risk data to be estimated. A comparison module is used to compare and analyze the data to be estimated with a preset evaluation module to obtain a data risk module. The preset evaluation module includes a first preset evaluation indicator and a second preset evaluation indicator. The first preset evaluation indicator includes a first preset risk indicator corresponding to each type of the first data to be estimated risk. The second preset evaluation indicator includes a second preset risk indicator corresponding to each type of the second data to be estimated risk. The data risk module includes a first data risk and a second data risk. The first data risk includes a first sub-data risk obtained by comparing and analyzing each type of the first data to be estimated risk with the corresponding first preset risk indicator. The second data risk includes a second sub-data risk obtained by comparing and analyzing each type of the second data to be estimated risk with the corresponding second preset risk indicator. The risk assessment module is used to weight the data risk module to obtain the quantitative safety risk of the target sample.
10. A computer-readable storage medium, characterized in that, The medium stores a computer program that can be executed by a processor to implement the method as described in any one of claims 1-8.