Snail bioactive peptide quality tracing system and method based on big data

Abstract

The invention provides a snail bioactive peptide quality tracing system and method based on big data, and belongs to the technical field of snail bioactive peptides. Comprising a data acquisition module used for acquiring various data in the production process of the snail bioactive peptide, a big data processing module used for integrating and cleaning the data, and an intelligent quality analysis module used for analyzing the quality of the snail bioactive peptide. The intelligent quality analysis module is used for carrying out quality prediction and abnormal early warning on the snail bioactive peptide, and the block chain tracing module is used for carrying out quality tracing on the snail bioactive peptide. According to the method, data islands of cultivation, production and detection links can be broken, cross-link association analysis is realized, manual sampling inspection is upgraded to AI-driven real-time early warning and process optimization, data authenticity is guaranteed through a block chain technology, international compliance requirements are met, and consumer trust and brand price are improved by adopting three-dimensional visualization and scientific data.

Description

Technical Field

[0001] The present invention relates to the technical field of snail active peptides, and in particular to a snail active peptide quality tracing system and method based on big data. Background Art

[0002] Snail bioactive peptides, as high-value-added bioactive substances, have a wide range of applications in medicine, cosmetics, and functional foods, such as promoting wound healing, anti-aging, and enhancing immunity. Their quality is highly dependent on the source of raw materials, production process, and storage conditions, particularly key indicators such as molecular weight distribution and microbial safety. However, the quality management of snail bioactive peptides in existing technologies faces the following key issues:

[0003] Snail farming environmental parameters, production and processing data, and quality inspection results are usually stored in independent systems, lacking cross-link correlation analysis. In addition, existing technologies mostly use QR codes to provide basic information, but lack dynamic data support, such as real-time quality scores, exception handling records, or supply chain environmental parameters. At the same time, key information stored in paper records or centralized databases is easily tampered with. Therefore, this application provides a snail active peptide quality traceability system and method based on big data to meet the needs. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a snail active peptide quality traceability system and method based on big data to solve the problem that the existing snail breeding environment parameters, production and processing data and quality inspection results are usually stored in independent systems, lacking cross-link correlation analysis, and the existing technology mostly uses QR codes to provide basic information, but lacks dynamic data support, such as real-time quality scores, exception handling records or supply chain environmental parameters. At the same time, the key information stored in paper records or centralized databases is easily tampered with.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A snail active peptide quality traceability system based on big data includes a data acquisition module, which is used to collect various data in the production process of snail active peptides; a big data processing module, which is used to integrate and clean data; an intelligent quality analysis module, which is used to predict the quality of snail active peptides and provide abnormal warnings; and a blockchain traceability module, which is used to trace the quality of snail active peptides.

[0007] Preferably, the data acquisition module includes a breeding environment monitoring module: the breeding environment monitoring module is used to deploy temperature and humidity sensors, light intensity meters, and soil pH meters to collect snail growth environment data in real time;

[0008] Production process monitoring module: The production process monitoring module is used to obtain the enzymatic hydrolysis process parameters through the equipment interface and link them with the technical indicators in the "Study on Process Optimization of Preparation of Snail Active Peptides by Enzymatic Hydrolysis";

[0009] Quality detection integrated module: The quality detection integrated module is used to detect the purity, molecular weight distribution and microbial indicators of active peptides in conjunction with high performance liquid chromatography, and the data is automatically uploaded to the cloud.

[0010] Preferably, the big data processing module includes a multi-source data fusion module: the multi-source data fusion module builds a distributed database based on Hadoop / Spark to integrate structured data and unstructured data;

[0011] Data cleaning and labeling module: The data cleaning and labeling module automatically filters outliers through threshold judgment and associates unique batch numbers.

[0012] Preferably, the intelligent quality analysis module includes a quality prediction model: the quality prediction model uses an LSTM neural network to analyze historical data, predict the yield of active peptides and the molecular weight distribution trend, and dynamically generate a quality score. The scoring algorithm is: S = 0.3X1 + 0.4X2 + 0.3X3, where X1 is purity, X2 is molecular weight compliance rate, and X3 is microbial safety;

[0013] Abnormal warning system: If the abnormal warning system detects excessive microorganisms or abnormal molecular weight distribution, it will automatically trigger an early warning and locate the source of pollution.

[0014] Preferably, the blockchain traceability module includes a hybrid chain architecture: a private chain stores the core process data of the enterprise, and a public chain opens up information for consumers to query;

[0015] Dynamic traceability code: Generates a unique QR code, which users can scan to view full life cycle information, including: 3D visualization of dynamic simulation of active peptide molecular structure; real-time updated clinical trial data; supply chain path map.

[0016] A snail active peptide quality traceability method based on big data, comprising the following steps:

[0017] S1. Encode the breeding environment data, production process parameters and quality inspection results according to ISO 22000 standards and build a database containing 120 quality control indicators;

[0018] S2. Use random forest model to analyze the correlation between environmental parameters and the quality of active peptides;

[0019] S3: Calculate the quality score in real time. If the score is lower than the threshold (e.g. S < 80 points), a level 3 warning will be automatically triggered.

[0020] S4. Use machine learning models to dynamically assess quality risks and generate process optimization recommendations;

[0021] S5. Use blockchain technology to achieve credible data storage and transparent traceability on the consumer side.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects:

[0023] This invention can break the data silos in the breeding, production, and testing links, realize cross-link correlation analysis, and upgrade from manual sampling to AI-driven real-time early warning and process optimization. It also uses blockchain technology to ensure data authenticity and meet international compliance requirements. At the same time, it uses three-dimensional visualization and scientific data to enhance consumer trust and brand value. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which are incorporated herein and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure and to enable one skilled in the relevant art to make and use the present disclosure.

[0025] Figure 1 This is a schematic diagram of the system structure of a snail active peptide quality traceability system based on big data;

[0026] Figure 2 This is a flow chart of a method for mass tracking of snail active peptides based on big data.

[0027] As shown in the figure, in order to clearly implement the structure of the embodiment of the present invention, specific structures and devices are marked in the figure, but this is only for illustrative purposes and is not intended to limit the present invention to the specific structure, device and environment. According to specific needs, ordinary technicians in this field can adjust or modify these devices and environments, and the adjustments or modifications made are still included in the scope of the appended claims. DETAILED DESCRIPTION

[0028] The following describes in detail a system and method for tracing the quality of snail active peptides based on big data provided by the present invention, in conjunction with the accompanying drawings and specific embodiments. It is also noted that, in order to make the embodiments more detailed, the following embodiments are listed as the best and preferred embodiments, and those skilled in the art may also adopt other alternative methods for implementation; and the accompanying drawings are only for the purpose of describing the embodiments in more detail, and are not intended to specifically limit the present invention.

[0029] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of those skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0030] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0031] It will be understood that the meanings of “on,” “over,” and “above” in this disclosure should be interpreted in the broadest manner, such that “on” means not only “directly on” something, but also includes being “on” something with intervening features or layers, and “on” or “over” means not only “on” or “above” something, but also includes being “on” or “above” something with no intervening features or layers.

[0032] Additionally, spatially relative terms such as "below," "beneath," "lower," "above," "upper," and the like may be used herein for descriptive convenience to describe the relationship of one element or feature to another element or features, as illustrated in the accompanying drawings. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. The device may be oriented in other ways, and the spatially relative descriptors used herein should be similarly interpreted accordingly.

[0033] Example 1

[0034] like Figure 1 As shown, an embodiment of the present invention provides a snail active peptide quality traceability system based on big data, including a data acquisition module, which is used to collect various data in the production process of snail active peptides, and also includes a big data processing module, which is used to integrate and clean data, and also includes an intelligent quality analysis module, which is used to perform quality prediction and abnormal warning of snail active peptides, and also includes a blockchain tracing module, which is used to trace the quality of snail active peptides.

[0035] Furthermore, the data acquisition module includes a breeding environment monitoring module: the breeding environment monitoring module is used to deploy temperature and humidity sensors, light intensity meters, and soil pH meters to collect snail growth environment data in real time (sampling frequency: 5 minutes / time);

[0036] Production process monitoring module: The production process monitoring module is used to obtain the enzymatic hydrolysis process parameters (enzyme type, temperature, time, pH value) through the equipment interface, and link them with the technical indicators in the "Study on Optimization of the Process for Preparation of Snail Active Peptides by Enzymatic Hydrolysis";

[0037] Quality detection integrated module: The quality detection integrated module is used to detect the purity of active peptides (≥98%), molecular weight distribution (<1000Da proportion ≥95%) and microbial indicators (total colony count ≤1000CFU / g) in conjunction with high performance liquid chromatography (HPLC), and the data is automatically uploaded to the cloud.

[0038] Furthermore, the big data processing module includes a multi-source data fusion module: the multi-source data fusion module builds a distributed database based on Hadoop / Spark to integrate structured data (sensor values) and unstructured data (quality inspection report images);

[0039] Data cleaning and labeling module: The data cleaning and labeling module automatically filters outliers through threshold judgment (such as the allowable fluctuation range of breeding humidity is ±5%), and associates the batch unique number (format: SNP-YYYYMMDD-XXX).

[0040] Furthermore, the intelligent quality analysis module includes a quality prediction model: the quality prediction model uses an LSTM neural network to analyze historical data, predict the yield of active peptides and the molecular weight distribution trend, and dynamically generate a quality score (0-100 points). The scoring algorithm is: S = 0.3X1+0.4X2+0.3X3, where X1 is purity, X2 is the molecular weight compliance rate, and X3 is microbial safety;

[0041] Abnormal warning system: If the abnormal warning system detects excessive microorganisms or abnormal molecular weight distribution, it will automatically trigger an early warning and locate the source of pollution (such as excessive aquaculture humidity or enzymatic hydrolysis time deviation on a certain day).

[0042] Furthermore, the blockchain traceability module includes a hybrid chain architecture: a private chain stores the company's core process data (such as enzymatic hydrolysis parameters), and a public chain opens up consumer query information (such as quality inspection reports and logistics routes);

[0043] Dynamic traceability code: Generates a unique QR code, which users can scan to view full life cycle information, including: three-dimensional visualization of dynamic simulation of active peptide molecular structure; real-time updated clinical trial data (such as immunoglobulin IgG improvement rate ≥40%); supply chain route map (based on GIS technology to mark raw material origin, processing plant and logistics nodes).

[0044] Example 2

[0045] like Figure 2 As shown, this embodiment provides a method for tracing the quality of snail active peptides based on big data, comprising the following steps:

[0046] S1. Encode the breeding environment data, production process parameters and quality inspection results according to ISO 22000 standards and build a database containing 120 quality control indicators;

[0047] S2. Use random forest model to analyze the correlation between environmental parameters and the quality of active peptides;

[0048] S3: Calculate the quality score in real time. If the score is lower than the threshold (e.g. S < 80 points), a level 3 warning will be automatically triggered.

[0049] S4. Use machine learning models to dynamically assess quality risks and generate process optimization recommendations;

[0050] S5. Use blockchain technology to achieve credible data storage and transparent traceability on the consumer side.

[0051] Furthermore, the S1 is specifically:

[0052] S101, deploy temperature and humidity sensors (accuracy ±0.5°C), light intensity meters (range 0-2000 Lux), and soil pH meters (accuracy ±0.1), collecting data every 5 minutes;

[0053] S102. Monitor the snail mucus secretion cycle and record the mucus collection time and degreasing pretreatment parameters (ethanol concentration, ultrasonic power, degreasing rate ≥ 95%);

[0054] S103. Obtain the enzymatic hydrolysis reactor parameters in real time through the device interface: enzyme type (alkaline protease / papain), temperature (±1°C), time (±1 minute), and pH value (±0.2);

[0055] S104. Record the intermediate product indicators (e.g., peptide chain length) of the two-step enzymatic hydrolysis process (first step: 50°C, pH 8.0, 2 hours; second step: 45°C, pH 7.0, 1.5 hours);

[0056] S105, high performance liquid chromatography (HPLC) was used to detect the purity (≥98%) and molecular weight distribution (<1000 Da accounted for ≥95%) of the active peptides;

[0057] S106. Microbial testing: total colony count (≤1000 CFU / g), Escherichia coli (not detectable).

[0058] Furthermore, the S2 is specifically:

[0059] Encode data according to ISO 22000 standards and build a standardized database containing the following fields:

[0060] Unique batch identifier: SNP-YYYYMMDD-XXX (SNP is the product code, XXX is the serial number);

[0061] Environmental parameters: temperature, humidity, light, soil pH;

[0062] Process parameters: enzymatic hydrolysis temperature, time, pH value, enzyme type;

[0063] Quality inspection results: purity, molecular weight distribution, and microbial indicators.

[0064] Furthermore, the S3 is specifically:

[0065] S301, dynamic threshold determination (e.g., when the aquaculture humidity is greater than 85%, an alarm is triggered, and when the enzymatic hydrolysis time deviation is greater than 10%, it is marked as abnormal);

[0066] S302, using the median absolute deviation (MAD) algorithm to eliminate sensor noise data;

[0067] S303. Associate breeding, production, and testing data through batch numbers to build a full-chain data view;

[0068] S04. Use Apache Kafka to implement real-time data stream processing and ensure data synchronization.

[0069] Furthermore, the S4 is specifically:

[0070] The quality prediction model was trained using an LSTM neural network (64 hidden layer nodes, 7-day time window). The input parameters included environmental data and process parameters, and the output was the probability of molecular weight distribution meeting the standard.

[0071] The random forest model analyzes key factors (e.g., when soil pH is greater than 6.5, molecular weight stability increases by 18%), predicts the yield of active peptides and molecular weight distribution trends, and dynamically generates a quality score (0-100 points). The scoring algorithm is: S = 0.3X1 + 0.4X2 + 0.3X3, where X1 is purity, X2 is molecular weight compliance rate, and X3 is a real-time calculation score for microbial safety. If S is less than 80 points, a third-level warning is triggered:

[0072] Yellow warning (80≥S≥70): internally notify quality control personnel for re-inspection;

[0073] Red alert (70>S≥60): Suspend production and trace back to the source of pollution;

[0074] Black warning (S<60): Initiate product recall procedure.

[0075] Furthermore, the S5 is specifically as follows:

[0076] Hybrid chain architecture:

[0077] Private chain: stores core enterprise data (such as enzymatic process parameters and supplier audit reports);

[0078] Public chain (Ethereum): Open to consumers to query data (such as quality inspection reports, logistics routes).

[0079] Hash value evidence:

[0080] Generate SHA-256 hash values for key data (such as HPLC test results and production date) and upload them to the blockchain to ensure that the data cannot be tampered with;

[0081] Support regulatory authorities to access private chain data through keys to meet cross-border compliance reviews (such as FDA and ECOCERT certification).

[0082] Traceability code design:

[0083] Generate a unique QR code and embed it into the blockchain query interface URL;

[0084] Support NFC chip (optional) to achieve offline verification in a network-free environment.

[0085] The technical solution provided by this invention can break the data silos in the breeding, production, and testing links, realize cross-link correlation analysis, and upgrade from manual sampling to AI-driven real-time early warning and process optimization. It also uses blockchain technology to ensure data authenticity and meet international compliance requirements. At the same time, it uses three-dimensional visualization and scientific data to enhance consumer trust and brand value.

[0086] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0087] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0088] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A snail active peptide quality traceability system based on big data, characterized in that: It includes a data acquisition module, which is used to collect various data in the production process of snail active peptides; a big data processing module, which is used to integrate and clean data; an intelligent quality analysis module, which is used to predict the quality of snail active peptides and issue abnormal warnings; and a blockchain traceability module, which is used to trace the quality of snail active peptides.

2. A snail active peptide quality tracing system based on big data according to claim 1, characterized in that: The data acquisition module includes a breeding environment monitoring module: the breeding environment monitoring module is used to deploy temperature and humidity sensors, light intensity meters, and soil pH meters to collect snail growth environment data in real time; Production process monitoring module: The production process monitoring module is used to obtain the enzymatic hydrolysis process parameters through the equipment interface and link them with the technical indicators in the "Study on Process Optimization of Preparation of Snail Active Peptides by Enzymatic Hydrolysis"; Quality detection integrated module: The quality detection integrated module is used to detect the purity, molecular weight distribution and microbial indicators of active peptides in conjunction with high performance liquid chromatography, and the data is automatically uploaded to the cloud.

3. The snail active peptide quality tracing system based on big data according to claim 1, characterized in that: The big data processing module includes a multi-source data fusion module: the multi-source data fusion module builds a distributed database based on Hadoop / Spark to integrate structured data and unstructured data; Data cleaning and labeling module: The data cleaning and labeling module automatically filters outliers through threshold judgment and associates unique batch numbers.

4. The snail active peptide quality tracing system based on big data according to claim 1, characterized in that: The intelligent quality analysis module includes a quality prediction model: the quality prediction model uses an LSTM neural network to analyze historical data, predict the yield of active peptides and the molecular weight distribution trend, and dynamically generate a quality score. The scoring algorithm is: S = 0.3X1 + 0.4X2 + 0.3X3, where X1 is purity, X2 is the molecular weight compliance rate, and X3 is microbial safety; Abnormal warning system: If the abnormal warning system detects excessive microorganisms or abnormal molecular weight distribution, it will automatically trigger an early warning and locate the source of pollution.

5. The snail active peptide quality tracing system based on big data according to claim 1, characterized in that: The blockchain traceability module includes a hybrid chain architecture: a private chain stores the company's core process data (such as enzymatic hydrolysis parameters), and a public chain opens up consumer query information (such as quality inspection reports and logistics routes); Dynamic traceability code: Generates a unique QR code, which users can scan to view full life cycle information, including: three-dimensional visualization of dynamic simulation of active peptide molecular structure; real-time updated clinical trial data (such as immunoglobulin IgG improvement rate ≥40%); supply chain route map (based on GIS technology to mark raw material origin, processing plant and logistics nodes).

6. A method for tracing the quality of snail active peptides based on big data, applied to a system for tracing the quality of snail active peptides based on big data as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Encode the breeding environment data, production process parameters and quality inspection results according to ISO 22000 standards and build a database containing 120 quality control indicators; S2. Use random forest model to analyze the correlation between environmental parameters and the quality of active peptides; S3: Calculate the quality score in real time. If the score is lower than the threshold (e.g. S < 80 points), a level 3 warning will be automatically triggered. S4. Use machine learning models to dynamically assess quality risks and generate process optimization recommendations; S5. Use blockchain technology to achieve credible data storage and transparent traceability on the consumer side.

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