Baijiu authenticity identification system and method
Through the authenticity and false identification system of liquor, machine learning models and feature comparison analysis are used to solve the problem of inconsistent offline labor and material resources and remote identification standards in liquor identification, achieving unified identification standards and improving the accuracy of the identification results.
Patent Information
- Application Number
- CN202510698907.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-28
- Publication Date
- 2025-07-04
AI Technical Summary
Among the existing methods of authenticity and false identification of liquor, offline identification costs are high and labor-intensive and material resources, and the remote identification standards are inconsistent, resulting in differences and controversy in the appraisal results.
The authenticity and false identification system of liquor is used to collect production information, sales data and logistics information through the data collection module, and train the machine learning model to obtain the circulation prediction model. Combined with the on-site identification module and feature comparison analysis module, the deviation between the current position of liquor and the predicted position is judged, and the authenticity results are output using the characteristic comparison analysis model.
It has achieved unified appraisal standards without increasing costs, improved the credibility and accuracy of appraisal results, and solved the problem of inconsistent manpower and material resources for offline appraisal and inconsistent remote appraisal standards.
Smart Images
Figure CN120258844A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of liquor authenticity identification, and particularly to a liquor authenticity identification system and method. Background Art
[0002] Existing liquor authenticity identification methods can be divided into two types: offline identification and remote identification according to different scenarios.
[0003] Offline identification refers to identifying liquor by on-site inspection, tasting and testing. The advantage of this method is that it can directly access the physical liquor, enabling more comprehensive and detailed identification, mainly including appearance inspection, color observation, odor identification, tasting experience, spectral detection analysis, intelligent sensory analysis, gas chromatography analysis, mass spectrometry analysis, isotope analysis and metabolomics data analysis, etc. Offline identification requires the appraisers to be present in person, which increases the consumption of manpower, material resources and time. Various professional equipment and reagents are needed during the identification process, resulting in high costs.
[0004] Remote identification refers to transmitting relevant information of liquor to appraisers for identification through digital means such as pictures, videos and audio. The advantage of this method is that it is convenient and fast, without being restricted by region and time, mainly including picture and video identification, audio analysis, online consultation, etc. Although remote identification technology has developed rapidly, there are still some limitations. For example, some chemical components or aroma components may not be accurately detected by remote identification technology. Remote identification may also involve multiple different appraisal institutions and platforms, and these institutions and platforms may adopt different identification standards and methods. The non-uniformity of identification standards may lead to differences and disputes in identification results, affecting the credibility and authority of the identification. Summary of the Invention
[0005] The technical problem to be solved by the present invention: The present invention provides a liquor authenticity identification system and method to solve the problem that the existing liquor authenticity identification is carried out by appraisers, resulting in non-uniform identification standards.
[0006] The present invention solves the above technical problems by adopting a technical solution: a liquor authenticity identification system, comprising a data acquisition module, a circulation prediction module, an on-site identification module, a location comparison module and a feature comparison analysis module; the data acquisition module is used to collect the production information, sales data and logistics information of the liquor; the circulation prediction module is used to take the production information of the liquor as input and the sales data and logistics information as output, train a machine learning model to obtain a circulation prediction model, and use the circulation prediction model to predict the sales data and logistics information of the liquor, and extract the corresponding relationship between the location range and time of the liquor from the predicted sales data and logistics information of the liquor; the on-site identification module is used to collect the production information, sales data and logistics information of the liquor; the circulation prediction module is used to train a machine learning model to obtain a circulation prediction model, and use the circulation prediction model to predict the sales data and logistics information of the liquor, and extract the corresponding relationship between the location range and time of the liquor from the predicted sales data and logistics information of the liquor; the on-site identification module is used to collect the production information, sales data and logistics information of the liquor; the circulation prediction model is used to train the machine learning model to obtain the ...; the on-site identification module is used to collect the production information, sales data and logistics information of the liquor; the circulation prediction model is used to train the machine learning model to obtain the circulation prediction model, and the circulation prediction model is used to predict the sales data and logistics information of the liquor; the on-site identification module is used to collect the production information, sales data and logistics information of the liquor; the circulation prediction model is used to train the machine learning model to obtain the circulation prediction model, and the circulation prediction model is used to predict the sales data and logistics information of the liquor; the on- The identification module is used to obtain the current time, the location of the liquor to be identified at the current time, and the feature data of the liquor to be identified; the position comparison module is used to obtain the predicted location range of the liquor at the current time from the corresponding relationship between the liquor location range and the time according to the current time, and to determine whether the deviation between the location of the liquor to be identified at the current time and the predicted location range of the liquor at the current time exceeds a preset value; the feature comparison and analysis module is used to use the feature comparison and analysis model to input the feature data of the liquor to be identified and output the authenticity of the liquor to be identified when the deviation between the current location of the liquor to be identified and the predicted area of the liquor exceeds a preset value.
[0007] Furthermore, the data collection module is also used to clean the collected production information, sales data and logistics information of the liquor, and the data cleaning includes filling in missing data and correcting abnormal data.
[0008] Furthermore, the production information includes production date, delivery date, sales route and expected sales area.
[0009] Furthermore, the characteristic data includes batch number, packaging material, printing quality, label details, wine color, aroma, taste and ingredient data.
[0010] Furthermore, the machine learning model includes a time series analysis model, a linear regression model, a decision tree model or a neural network model.
[0011] Furthermore, the feature comparison analysis model is trained using feature data of genuine and fake wine samples and the corresponding authenticity.
[0012] The present invention also provides a method for identifying the authenticity of liquor, using the liquor authenticity identification system as described above, the method comprising the following steps: S1. Obtain the production information, historical sales data and actual logistics information of liquor; S2. Use the production information of baijiu as the input, and the sales data and logistics information as the output to train a machine learning model to obtain a circulation prediction model. Then use the circulation prediction model to predict the sales data and logistics information of baijiu, and extract the corresponding relationship between the location range of baijiu and time from the sales data and logistics data; S3. Obtain the current time, the location of the baijiu to be authenticated at the current time, and the characteristic data of the baijiu to be authenticated; S4. According to the current time, obtain the predicted location range of the baijiu corresponding to the current time from the corresponding relationship between the location range of baijiu and time, and determine whether the deviation between the location of the baijiu to be authenticated at the current time and the predicted location range of the baijiu corresponding to the current time exceeds a preset value; S5. When the deviation between the current location of the baijiu to be authenticated and the predicted area of the baijiu exceeds the preset value, use the characteristic comparison analysis model to input the characteristic data of the baijiu to be authenticated and output the authenticity of the baijiu to be authenticated.
[0013] Further, in S1, it also includes data cleaning of the obtained production information, historical sales data, and actual logistics information of baijiu. The data cleaning includes filling in missing data and correcting abnormal data.
[0014] Further, in S2, the machine learning model includes a time series analysis model, a linear regression model, a decision tree model, or a neural network model.
[0015] Further, in S5, the characteristic comparison analysis model is trained using the characteristic data and corresponding authenticity of genuine and fake baijiu samples.
[0016] Advantages of the present invention: The present invention provides a system and method for authenticating the authenticity of baijiu. Using the production information of baijiu as the input, and the sales data and logistics information as the output, train a machine learning model to obtain a circulation prediction model, and use the circulation prediction model to predict the sales data and logistics information of baijiu, so as to obtain the corresponding relationship between the location range of baijiu and time. Then obtain the current time, the location of the baijiu to be authenticated at the current time, and the characteristic data of the baijiu to be authenticated. According to the current time, obtain the predicted location range of the baijiu corresponding to the current time from the corresponding relationship between the location range of baijiu and time, and determine whether the deviation between the location of the baijiu to be authenticated at the current time and the predicted location range of the baijiu corresponding to the current time exceeds a preset value. When the deviation between the current location of the baijiu to be authenticated and the predicted area of the baijiu exceeds the preset value, use the characteristic comparison analysis model to input the characteristic data of the baijiu to be authenticated and output the authenticity of the baijiu to be authenticated, thereby solving the problem that the existing authenticity authentication of baijiu is carried out by appraisers, resulting in inconsistent authentication standards. Description of the Drawings
[0017] Figure 1It is a schematic structural diagram of a Baijiu authenticity identification system provided by the present invention; Figure 2 It is a schematic flowchart of a Baijiu authenticity identification method provided by the present invention. Detailed implementation manners
[0018] In view of the problem that the authenticity identification of existing Baijiu is carried out by appraisers, resulting in inconsistent identification standards, the present invention provides a Baijiu authenticity identification system and method. Taking the production information of Baijiu as input, and the sales data and logistics information as output, a machine learning model is trained to obtain a circulation prediction model, and the circulation prediction model is used to predict the sales data and logistics information of Baijiu, so as to obtain the corresponding relationship between the location range of Baijiu and time. Then, the current time, the location of the Baijiu to be identified at the current time, and the characteristic data of the Baijiu to be identified are obtained. According to the current time, the predicted location range of the Baijiu at the current time is obtained from the corresponding relationship between the location range of Baijiu and time, and it is judged whether the deviation between the location of the Baijiu to be identified at the current time and the predicted location range of the Baijiu at the current time exceeds a preset value. When the deviation between the current location of the Baijiu to be identified and the predicted area of the Baijiu exceeds the preset value, the characteristic comparison analysis model is used to input the characteristic data of the Baijiu to be identified and output the authenticity of the Baijiu to be identified.
[0019] As Figure 1 shown, a Baijiu authenticity identification system provided by the present invention includes a data acquisition module, a circulation prediction module, a on-site identification module, a location comparison module, and a characteristic comparison analysis module; the data acquisition module is used to collect the production information, sales data, and logistics information of Baijiu; the circulation prediction module is used to take the production information of Baijiu as input, and the sales data and logistics information as output, train a machine learning model to obtain a circulation prediction model, and use the circulation prediction model to predict the sales data and logistics information of Baijiu, and extract the corresponding relationship between the location range of Baijiu and time from the predicted sales data and logistics information of Baijiu; the on-site identification module is used to obtain the current time, the location of the Baijiu to be identified at the current time, and the characteristic data of the Baijiu to be identified; the location comparison module is used to obtain the predicted location range of the Baijiu at the current time from the corresponding relationship between the location range of Baijiu and time according to the current time, and judge whether the deviation between the location of the Baijiu to be identified at the current time and the predicted location range of the Baijiu at the current time exceeds a preset value; the characteristic comparison analysis module is used to, when the deviation between the current location of the Baijiu to be identified and the predicted area of the Baijiu exceeds the preset value, use the characteristic comparison analysis model to input the characteristic data of the Baijiu to be identified and output the authenticity of the Baijiu to be identified.
[0020] Specifically, to ensure the accuracy and integrity of the data, the data collection module is also used to clean the production information, sales data, and logistics information of the collected baijiu. The data cleaning includes filling in missing data and correcting abnormal data.
[0021] The production information includes the production date, the outbound date, the sales path, and the estimated sales area. The production date and the outbound date can help determine the timeline of the baijiu during production and circulation, while the estimated sales area provides the general flow direction and scope of the baijiu in the market.
[0022] The machine learning model includes a time series analysis model, a linear regression model, a decision tree model, or a neural network model. In this way, the machine learning model can learn the circulation patterns and possible regions of the baijiu at different time periods, that is, predict the sales data and logistics information of the baijiu.
[0023] The characteristic data includes multiple types such as batch number, packaging material, printing quality, label details, liquor body color, aroma and taste, and composition data. The characteristic comparison and analysis model is trained using the characteristic data and corresponding authenticity of genuine and fake baijiu samples. In this way, for the baijiu to be identified when the deviation between the current position and the predicted region of the baijiu exceeds the preset value, further authenticity identification is performed.
[0024] The present invention also provides a method for identifying the authenticity of baijiu, which uses the above-mentioned baijiu authenticity identification system. The method, as Figure 2 shown, includes the following steps: S1. Obtain the production information, historical sales data, and actual logistics information of the baijiu.
[0025] Specifically, the production information includes the production date, the outbound date, the sales path, and the estimated sales area. The production date and the outbound date can help determine the timeline of the baijiu during production and circulation, while the estimated sales area provides the general flow direction and scope of the baijiu in the market. The historical sales data includes information such as past sales records, sales volumes, and sales channels, which helps the machine learning model learn the sales patterns and circulation laws of the baijiu. The logistics information includes information such as logistics speed, transportation path, and logistics company, enabling the machine learning model to consider the impact of logistics factors on the circulation of the baijiu.
[0026] S2. Use the production information of the baijiu as the input, and the sales data and logistics information as the output to train the machine learning model to obtain a circulation prediction model, and use the circulation prediction model to predict the sales data and logistics information of the baijiu, and extract the corresponding relationship between the location range and time of the baijiu from the sales data and logistics data.
[0027] Specifically, both the sales data and logistics information are related to time. Therefore, the location range of the baijiu can be determined by the time point, and the corresponding relationship between the location range of the baijiu and time can be extracted.
[0028] S3. Obtain the current time, the location of the baijiu to be identified at the current time, and the characteristic data of the baijiu to be identified.
[0029] Specifically, the characteristic data includes multiple types among the batch number, packaging material, printing quality, label details, liquor body color, aroma and taste, and ingredient data.
[0030] S4. According to the current time, obtain the predicted location range of the baijiu corresponding to the current time from the corresponding relationship between the location range of the baijiu and time, and determine whether the deviation between the location of the baijiu to be identified at the current time and the predicted location range of the baijiu corresponding to the current time exceeds a preset value.
[0031] Specifically, the preset value can be 30 kilometers.
[0032] S5. When the deviation between the current location of the baijiu to be identified and the predicted area of the baijiu exceeds the preset value, use the characteristic comparison analysis model to input the characteristic data of the baijiu to be identified and output the authenticity of the baijiu to be identified.
[0033] Specifically, the characteristic comparison analysis model is trained with the characteristic data and corresponding authenticity of genuine and fake liquor samples. Thus, the authenticity of the baijiu to be identified can be determined through the characteristic data of the baijiu to be identified.
Claims
1. Baijiu authenticity identification system, characterized in that, The invention comprises a data collection module, a circulation prediction module, an on-site identification module, a location comparison module and a feature comparison analysis module; the data collection module is used to collect the production information, sales data and logistics information of liquor; the circulation prediction module is used to take the production information of liquor as input and the sales data and logistics information as output, train the machine learning model, obtain the circulation prediction model, and use the circulation prediction model to predict the sales data and logistics information of liquor, and extract the corresponding relationship between the location range and time of liquor from the predicted sales data and logistics information of liquor; the on-site identification module is used to obtain the current time, the current time and the current time. The location of the liquor to be identified at the previous time and the feature data of the liquor to be identified; the location comparison module is used to obtain the predicted location range of the liquor at the current time from the corresponding relationship between the location range of the liquor and the time according to the current time, and to judge whether the deviation between the location of the liquor to be identified at the current time and the predicted location range of the liquor at the current time exceeds a preset value; the feature comparison and analysis module is used to use the feature comparison and analysis model to input the feature data of the liquor to be identified and output the authenticity of the liquor to be identified when the deviation between the current location of the liquor to be identified and the predicted area of the liquor exceeds a preset value.
2. The liquor authenticity identification system according to claim 1, characterized in that, The data collection module is also used to clean the collected liquor production information, sales data and logistics information, and the data cleaning includes filling in missing data and correcting abnormal data.
3. The liquor authenticity identification system according to claim 1, characterized in that, The production information includes production date, delivery date, sales route and expected sales area.
4. The liquor authenticity identification system according to claim 1, wherein The characteristic data include batch number, packaging material, printing quality, label details, wine color, aroma, taste and ingredient data.
5. The liquor authenticity identification system according to claim 1, characterized in that, The machine learning model includes a time series analysis model, a linear regression model, a decision tree model or a neural network model.
6. The liquor authenticity identification system according to claim 1, wherein The feature comparison analysis model is trained using the feature data of the real and fake wine samples and the corresponding authenticity.
7. Method for authenticating the genuineness of Chinese liquor, characterized in that, Using the liquor authenticity identification system as claimed in claim 1, the method comprises the following steps: S1. Obtain the production information, historical sales data and actual logistics information of liquor; S2. Using the production information of liquor as input and the sales data and logistics information as output, training a machine learning model to obtain a circulation prediction model, and using the circulation prediction model to predict the sales data and logistics information of liquor, and extracting the corresponding relationship between the location range and time of liquor from the sales data and logistics data; S3, obtaining the current time, the location of the liquor to be identified at the current time, and characteristic data of the liquor to be identified; S4, obtaining the predicted location range of the liquor at the current time from the correspondence between the liquor location range and time according to the current time, and judging whether the deviation between the location of the liquor to be identified at the current time and the predicted location range of the liquor at the current time exceeds a preset value; S5. When the deviation between the current position of the liquor to be identified and the predicted area of the liquor exceeds a preset value, the feature comparison analysis model is used to input feature data of the liquor to be identified and output the authenticity of the liquor to be identified.
8. The method for authenticating the genuineness of Chinese liquor according to claim 7, wherein In S1, it also includes data cleaning of the obtained production information, historical sales data, and actual logistics information of the liquor, and the data cleaning includes filling in missing data and correcting abnormal data.
9. The method for authenticating the genuineness of liquor according to claim 7, characterized in that, In S2, the machine learning model includes a time series analysis model, a linear regression model, a decision tree model, or a neural network model.
10. The method for authenticating the genuineness of liquor according to claim 7, characterized in that, In S5, the feature comparison and analysis model is trained using the feature data and corresponding authenticity of genuine and fake liquor samples.
Citation Information
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