Astragalus mongholicus producing area traceability system and algorithm

Abstract

The invention discloses a radix astragali seu hedysari origin traceability system and algorithm. The system comprises a data acquisition module, a quality evaluation module, a risk early warning module, an algorithm analysis module and a block chain evidence storage module. According to the method, geographical indication data, biodiversity characteristic analysis and the Internet of Things technology are integrated, data collection of the whole life cycle of astragalus mongholicus is achieved, and the problems that a traditional method depends on manual recording, tampering is prone to occurring, and the cost is high are solved; a weighted random data model is adopted to perform multi-source data fusion, and the weight is dynamically adjusted through a machine learning algorithm, so that the production area discrimination accuracy is remarkably improved, and the production area discrimination accuracy can reach more than 95%.

Description

Technical Field

[0001] The present invention relates to the technical field of Chinese medicinal material traceability, and in particular to a Hengshan Astragalus origin traceability system and algorithm. Background Art

[0002] Hengshan Astragalus, a traditional and precious Chinese medicinal material, is highly sought after in the market for its unique medicinal value. However, with growing market demand, counterfeit and substandard Hengshan Astragalus products are rampant, and traditional origin traceability methods present numerous challenges. Most traditional methods rely on manual record-keeping, which is not only inefficient but also prone to data errors and tampering, resulting in low credibility of traceability results. Furthermore, the high cost of manual record-keeping and verification makes it difficult to promote and apply in large-scale production and sales. Furthermore, existing traceability technologies lack comprehensiveness and precision in data collection and analysis, making it impossible to accurately determine the true origin of Hengshan Astragalus, seriously impacting its market reputation and consumer interests. Therefore, an efficient, accurate, and reliable Hengshan Astragalus origin traceability system and algorithm is urgently needed. Summary of the Invention

[0003] The purpose of the present invention is to provide a Hengshan Astragalus origin traceability system and algorithm to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: a Hengshan Astragalus origin traceability system, comprising a data acquisition module, a quality assessment module, a risk warning module, an algorithm analysis module, and a blockchain evidence storage module;

[0005] The data acquisition module includes:

[0006] The Geographical Indication Data Collection Unit is responsible for collecting geographical indication data related to the production areas of Astragalus in the Hengshan area, including but not limited to soil composition, altitude, longitude and latitude information, and climate characteristics;

[0007] The biometric data collection unit is responsible for collecting various biometric data of Hengshan Astragalus plants, including but not limited to plant morphology, leaf texture, flower characteristics, and growth years;

[0008] The IoT data collection unit is responsible for collecting various data from the Hengshan Astragalus cultivation environment in real time;

[0009] The quality assessment module includes:

[0010] The growth index evaluation unit comprehensively evaluates the growth status of Astragalus membranaceus based on the planting environment data provided by the IoT data collection unit and the characteristics of Astragalus membranaceus at different growth stages to determine whether it is in good condition.

[0011] The ingredient content detection unit is responsible for the precise detection and content analysis of various ingredients in Hengshan Astragalus samples;

[0012] The risk warning module includes:

[0013] The abnormal data monitoring unit is used to monitor the data collected from the Hengshan Astragalus origin traceability system in real time. By setting reasonable data thresholds and applying specific algorithm models, it can identify data that deviates from the normal range.

[0014] The market risk analysis unit collects information on Hengshan Astragalus price fluctuations, supply and demand changes, and the frequency of counterfeit products in the market, and uses big data analysis models to predict market risk trends;

[0015] The algorithm analysis module includes:

[0016] The data preprocessing submodule performs preprocessing operations such as cleaning, denoising, and normalizing the collected geographical indication data, biometric data, and IoT data to eliminate outliers and noise in the data and unify the data format and scale;

[0017] The multi-source data fusion submodule adopts a weighted random data model to perform multi-source data fusion;

[0018] The origin matching calculation submodule compares and analyzes the fused data with the benchmark data in the Hengshan geographical indication database, and calculates the matching degree between the target astragalus sample and the Hengshan origin through a similarity calculation algorithm;

[0019] The result output submodule outputs the traceability result based on the origin matching calculation result;

[0020] The blockchain evidence storage module includes:

[0021] The data hash processing unit performs hash operations on the traceability information of each link of planting, processing, and circulation (including data collected by the data acquisition module, processing results of the algorithm analysis module, etc.) to generate a unique hash value;

[0022] The blockchain storage unit stores the generated hash value on the blockchain, using the blockchain's distributed ledger technology to achieve decentralized storage of traceability information;

[0023] The verification interface unit provides mobile phone QR codes, NFC chips or other verification interfaces, supporting users to quickly read traceability information by scanning QR codes or using NFC devices.

[0024] Preferably, the geographical indication data acquisition unit is connected to the geographical information database of soil, climate, and altitude in the Hengshan area, and acquires in real time the geographical indication data of soil type, fertility, pH, temperature, humidity, rainfall, sunshine duration, altitude, and topography in the Hengshan area; the geographical indication data acquisition unit uses high-precision geographical information acquisition equipment and sensors to ensure the accuracy and real-time nature of the data;

[0025] The biometric data collection unit uses professional testing instruments to measure the isotope ratio (such as δ 13 C, δ 15 N) detection and trace element spectral analysis; using non-destructive testing technology, without destroying the astragalus sample, quickly and accurately obtain the biological characteristic data of astragalus and establish a production area characteristic data model;

[0026] The IoT data acquisition unit deploys IoT devices such as temperature and humidity sensors, light sensors, and CO2 concentration sensors in the Hengshan Astragalus cultivation process to collect real-time temperature, humidity, light intensity, and CO2 concentration data of the cultivation environment, and uploads the data to the blockchain in real time via wireless networks; the IoT data acquisition unit uses low-power, high-stability sensors and communication modules to ensure the continuity and reliability of data collection.

[0027] Preferably, the component content detection unit uses high performance liquid chromatography-mass spectrometry analysis instruments to measure the content of active ingredients, such as calycosin glucoside and astragaloside IV, on the harvested astragalus samples. The measurement results are compared with the quality standards of Hengshan Astragalus to determine the quality grade of the astragalus.

[0028] Preferably, the abnormal data monitoring unit monitors the data of each unit of the data acquisition module in real time, and by setting a reasonable data threshold range, once abnormal fluctuations in geographical indication data, biometric data or Internet of Things data are found, such as a sudden and significant change in soil pH, or a significant deviation of the content of a certain trace element in astragalus from the normal level, an early warning signal is issued in a timely manner;

[0029] When market risks reach a certain level in the market risk analysis unit, an early warning will be issued to relevant production and sales companies and regulatory authorities so that response strategies can be formulated in advance.

[0030] Preferably, the fusion formula of the weighted random data model in the multi-source data fusion submodule is:

[0031]

[0032] Among them, S represents the origin similarity score, n is the number of data types, and w i is the weight of the i-th data, x iis the value of the i-th data after preprocessing; w i The importance of each feature data to the origin identification of Hengshan Astragalus is dynamically adjusted, and the optimal weight value is determined through machine learning algorithm, training and optimization based on a large amount of historical data.

[0033] Preferably, the calculation formula of the similarity calculation algorithm in the origin matching calculation submodule is:

[0034]

[0035] Among them, Sim represents the matching degree, m is the data dimension, x i is the target Astragalus sample data, y i It is the benchmark data in the Hengshan Geographical Indication Database;

[0036] If the matching degree in the result output submodule reaches the set threshold (such as above 95%), the astragalus sample is determined to be from Hengshan origin and a detailed traceability report is generated; otherwise, it is determined to be not from Hengshan origin; at the same time, the traceability result is sent to the blockchain evidence storage module.

[0037] Preferably, the hash algorithm in the data hash processing unit adopts the highly secure SHA-256 algorithm to ensure the integrity and non-tamperability of the data; its formula is:

[0038] H=SHA-256(M)

[0039] Where M is the input message. By performing complex operations on the input message M, a hash value H with a fixed length of 256 bits is generated.

[0040] Preferably, when verifying on the mobile phone side, the verification interface unit quickly verifies the authenticity of the traceability information through a simplified verification model and displays detailed traceability results.

[0041] An algorithm for tracing the origin of Hengshan Astragalus membranaceus comprises the following steps:

[0042] Step 1: Obtain biometric data of the target Astragalus sample, including isotopes and trace elements, and retrieve benchmark data from the Hengshan Geographical Indication Database;

[0043] Step 2: Preprocess the collected geographical indication data, biometric data and IoT data;

[0044] Step 3: Use the weighted random data model to perform multi-source data fusion on the pre-processed data and calculate the origin similarity score;

[0045] Step 4: Calculate the matching degree between the target Astragalus sample and the Hengshan origin through a similarity calculation algorithm;

[0046] Step 5: Output the traceability results based on the matching degree, and store the traceability information on the blockchain after hashing.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] This invention integrates geographical indication data, biodiversity feature analysis, and Internet of Things technology to achieve data collection for the entire life cycle of Hengshan Astragalus, solving the problems of traditional methods that rely on manual recording, are prone to tampering, and are costly.

[0049] The weighted random data model is used to fuse multi-source data, and the weights are dynamically adjusted through a machine learning algorithm, which significantly improves the accuracy of origin identification, reaching over 95%.

[0050] The use of blockchain technology to store traceability information ensures the immutability and security of the data. It also supports traceability throughout the entire life cycle, from planting to consumer hands. Consumers can quickly verify the authenticity of the origin of the astragalus through various means such as mobile phones, protecting their legitimate rights and interests and maintaining the market reputation of Hengshan Astragalus.

[0051] The newly added quality assessment module evaluates the quality of astragalus from the aspects of growth indicators and ingredient content, which helps to control product quality; the risk warning module detects potential problems in advance through abnormal data monitoring and market risk analysis, providing guarantees for the stable development of the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a system principle diagram of the present invention;

[0053] Figure 2 It is the algorithm flow chart of the present invention;

[0054] Figure 3 It is a module diagram of the data acquisition module of the present invention;

[0055] Figure 4 is a module diagram of the quality assessment module of the present invention;

[0056] Figure 5 It is a module diagram of the risk warning module of the present invention;

[0057] Figure 6 It is a module diagram of the algorithm analysis module of the present invention;

[0058] Figure 7 This is a module diagram of the blockchain evidence storage module of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] See also Figure 1-7 , the present invention provides a Hengshan Astragalus origin traceability system, including a data acquisition module, a quality assessment module, a risk warning module, an algorithm analysis module and a blockchain evidence storage module;

[0061] The data acquisition module includes:

[0062] The Geographical Indication Data Collection Unit is responsible for collecting geographical indication data related to the production areas of Astragalus in the Hengshan area, including but not limited to soil composition, altitude, longitude and latitude information, and climate characteristics;

[0063] The biometric data collection unit is responsible for collecting various biometric data of Hengshan Astragalus plants, including but not limited to plant morphology, leaf texture, flower characteristics, and growth years;

[0064] The IoT data collection unit is responsible for collecting various data from the Hengshan Astragalus cultivation environment in real time;

[0065] The quality assessment module includes:

[0066] The growth index evaluation unit comprehensively evaluates the growth status of Astragalus membranaceus based on the planting environment data provided by the IoT data collection unit and the characteristics of Astragalus membranaceus at different growth stages to determine whether it is in good condition.

[0067] The ingredient content detection unit is responsible for the precise detection and content analysis of various ingredients in Hengshan Astragalus samples;

[0068] The risk warning module includes:

[0069] The abnormal data monitoring unit is used to monitor the data collected from the Hengshan Astragalus origin traceability system in real time. By setting reasonable data thresholds and applying specific algorithm models, it can identify data that deviates from the normal range.

[0070] The market risk analysis unit collects information on Hengshan Astragalus price fluctuations, supply and demand changes, and the frequency of counterfeit products in the market, and uses big data analysis models to predict market risk trends;

[0071] The algorithm analysis module includes:

[0072] The data preprocessing submodule performs preprocessing operations such as cleaning, denoising, and normalizing the collected geographical indication data, biometric data, and IoT data to eliminate outliers and noise in the data and unify the data format and scale;

[0073] The multi-source data fusion submodule adopts a weighted random data model to perform multi-source data fusion;

[0074] The origin matching calculation submodule compares and analyzes the fused data with the benchmark data in the Hengshan geographical indication database, and calculates the matching degree between the target astragalus sample and the Hengshan origin through a similarity calculation algorithm;

[0075] The result output submodule outputs the traceability result based on the origin matching calculation result;

[0076] The blockchain evidence storage module includes:

[0077] The data hash processing unit performs hash operations on the traceability information of each link of planting, processing, and circulation (including data collected by the data acquisition module, processing results of the algorithm analysis module, etc.) to generate a unique hash value;

[0078] The blockchain storage unit stores the generated hash value on the blockchain, using the blockchain's distributed ledger technology to achieve decentralized storage of traceability information;

[0079] The verification interface unit provides mobile phone QR codes, NFC chips or other verification interfaces, supporting users to quickly read traceability information by scanning QR codes or using NFC devices.

[0080] The geographical indication data collection unit is connected to the Hengshan area's geographical information database on soil, climate, and altitude, and obtains real-time geographical indication data on the Hengshan area's soil type, fertility, pH, temperature, humidity, rainfall, hours of sunshine, as well as altitude and topography. The geographical indication data collection unit uses high-precision geographical information collection equipment and sensors to ensure data accuracy and real-time availability.

[0081] The biometric data collection unit uses professional testing instruments to measure the isotope ratio (such as δ 13 C, δ 15 N) detection and trace element spectral analysis; using non-destructive testing technology, without destroying the astragalus sample, quickly and accurately obtain the biological characteristic data of astragalus and establish a production area characteristic data model;

[0082] The IoT data acquisition unit deploys IoT devices such as temperature and humidity sensors, light sensors, and CO2 concentration sensors in the Hengshan Astragalus cultivation process to collect real-time temperature, humidity, light intensity, and CO2 concentration data of the cultivation environment, and uploads the data to the blockchain in real time via wireless networks; the IoT data acquisition unit uses low-power, high-stability sensors and communication modules to ensure the continuity and reliability of data collection.

[0083] The ingredient content detection unit uses high-performance liquid chromatography-mass spectrometry to measure the active ingredient content of harvested astragalus samples, including key medicinal ingredients such as caerulean glucoside and astragaloside IV. The results are compared with the quality standards for Hengshan astragalus to determine the quality grade of the astragalus.

[0084] The abnormal data monitoring unit monitors the data of each unit of the data acquisition module in real time. By setting a reasonable data threshold range, it will issue a warning signal in time if it finds abnormal fluctuations in geographical indication data, biometric data or IoT data, such as sudden and significant changes in soil pH or significant deviations from normal levels of certain trace elements in astragalus.

[0085] When market risks reach a certain level in the market risk analysis unit, an early warning will be issued to relevant production and sales companies and regulatory authorities so that response strategies can be formulated in advance.

[0086] The fusion formula of the weighted random data model in the multi-source data fusion submodule is:

[0087]

[0088] Among them, S represents the origin similarity score, n is the number of data types, and w i is the weight of the i-th data, x i is the value of the i-th data after preprocessing; w i The importance of each feature data to the origin identification of Hengshan Astragalus is dynamically adjusted, and the optimal weight value is determined through machine learning algorithm, training and optimization based on a large amount of historical data.

[0089] The calculation formula of the similarity calculation algorithm in the origin matching calculation submodule is:

[0090]

[0091] Among them, Sim represents the matching degree, m is the data dimension, x i is the target Astragalus sample data, y i It is the benchmark data in the Hengshan Geographical Indication Database;

[0092] If the matching degree in the result output submodule reaches the set threshold (such as above 95%), the astragalus sample is determined to be from Hengshan origin and a detailed traceability report is generated; otherwise, it is determined to be not from Hengshan origin; at the same time, the traceability result is sent to the blockchain evidence storage module.

[0093] The hash algorithm in the data hash processing unit uses the highly secure SHA-256 algorithm to ensure data integrity and non-tampering; its formula is:

[0094] H=SHA-256(M)

[0095] Where M is the input message. By performing complex operations on the input message M, a hash value H with a fixed length of 256 bits is generated.

[0096] When verifying on the mobile phone, the verification interface unit quickly verifies the authenticity of the traceability information through a simplified verification model and displays detailed traceability results.

[0097] An algorithm for tracing the origin of Hengshan Astragalus membranaceus comprises the following steps:

[0098] Step 1: Obtain biometric data of the target Astragalus sample, including isotopes and trace elements, and retrieve benchmark data from the Hengshan Geographical Indication Database;

[0099] Step 2: Preprocess the collected geographical indication data, biometric data and IoT data;

[0100] Step 3: Use the weighted random data model to perform multi-source data fusion on the pre-processed data and calculate the origin similarity score;

[0101] Step 4: Calculate the matching degree between the target Astragalus sample and the Hengshan origin through a similarity calculation algorithm;

[0102] Step 5: Output the traceability results based on the matching degree, and store the traceability information on the blockchain after hashing.

[0103] Example:

[0104] Data collection:

[0105] At the Hengshan Astragalus cultivation base, a geographical indication data collection unit regularly retrieves soil, climate, and altitude data from a geographic information database. A biometric data collection unit selects representative samples at different stages of astragalus growth for isotope ratio and trace element spectral analysis. An IoT data collection unit collects real-time data on the cultivation environment, including temperature, humidity, and light intensity, and uploads it to the blockchain.

[0106] Quality Assessment:

[0107] The Growth Index Assessment Unit evaluates the growth status of astragalus root using IoT data, quantifying growth trends across multiple dimensions, including plant height, stem thickness, and leaf luxuriance, to produce a growth stage assessment report. The Component Content Detection Unit measures the content of astragalus root samples after harvest, focusing on the proportions of active ingredients such as polysaccharides, flavonoids, and saponins, and comparing these to high-quality astragalus root standards.

[0108] Risk Warning:

[0109] The Abnormal Data Monitoring Unit constantly monitors various data for anomalies, such as environmental data deviating from optimal ranges, growth indicators showing stagnation or unusual fluctuations, and issues timely warning signals. The Market Risk Analysis Unit simultaneously collects market information for risk forecasting, analyzing factors such as price fluctuations, changes in supply and demand, and policy adjustments to proactively identify potential risks to the Hengshan Astragalus industry.

[0110] Algorithm analysis:

[0111] The data preprocessing submodule cleans and normalizes the collected data. The multi-source data fusion submodule performs weighted fusion on the preprocessed data based on weights derived from historical data training to calculate an origin similarity score. The origin matching calculation submodule uses the cosine similarity algorithm to calculate the match between the target astragalus sample and the Hengshan origin. The result output submodule determines the origin of the astragalus based on the match and generates a traceability report.

[0112] Blockchain evidence storage:

[0113] The blockchain evidence storage module hashes traceability information from planting, processing, and distribution, and then stores it on the blockchain. When purchasing astragalus, consumers can verify the product's origin and authenticity by scanning the QR code on the packaging with their mobile phone or using an NFC device to read the traceability information. They can also view quality assessment results and any risk warnings.

[0114] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A Hengshan Astragalus origin traceability system, characterized by: It includes data collection module, quality assessment module, risk warning module, algorithm analysis module and blockchain evidence storage module; The data acquisition module includes: The Geographical Indication Data Collection Unit is responsible for collecting geographical indication data related to the production areas of Astragalus in the Hengshan area, including but not limited to soil composition, altitude, longitude and latitude information, and climate characteristics; The biometric data collection unit is responsible for collecting various biometric data of Hengshan Astragalus plants, including but not limited to plant morphology, leaf texture, flower characteristics, and growth years; The IoT data collection unit is responsible for collecting various data from the Hengshan Astragalus cultivation environment in real time; The quality assessment module includes: The growth index evaluation unit comprehensively evaluates the growth status of Astragalus membranaceus based on the planting environment data provided by the IoT data collection unit and the characteristics of Astragalus membranaceus at different growth stages to determine whether it is in good condition. The ingredient content detection unit is responsible for the precise detection and content analysis of various ingredients in Hengshan Astragalus samples; The risk warning module includes: The abnormal data monitoring unit is used to monitor the data collected from the Hengshan Astragalus origin traceability system in real time. By setting reasonable data thresholds and applying specific algorithm models, it can identify data that deviates from the normal range. The market risk analysis unit collects information on Hengshan Astragalus price fluctuations, supply and demand changes, and the frequency of counterfeit products in the market, and uses big data analysis models to predict market risk trends; The algorithm analysis module includes: The data preprocessing submodule performs preprocessing operations such as cleaning, denoising, and normalizing the collected geographical indication data, biometric data, and IoT data to eliminate outliers and noise in the data and unify the data format and scale; The multi-source data fusion submodule adopts weighted random data model to carry out multi-source data fusion; The origin matching calculation submodule compares and analyzes the fused data with the benchmark data in the Hengshan geographical indication database, and calculates the matching degree between the target astragalus sample and the Hengshan origin through a similarity calculation algorithm; The result output submodule outputs the traceability result based on the origin matching calculation result; The blockchain evidence storage module includes: The data hash processing unit performs hash operations on the traceability information of each link of planting, processing, and circulation to generate a unique hash value; The blockchain storage unit stores the generated hash value on the blockchain, using the blockchain's distributed ledger technology to achieve decentralized storage of traceability information; The verification interface unit provides mobile phone QR codes, NFC chips or other verification interfaces, supporting users to quickly read traceability information by scanning QR codes or using NFC devices.

2. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: The geographical indication data acquisition unit is connected to the geographical information database of the Hengshan region's soil, climate, and altitude, and acquires in real time the geographical indication data of the Hengshan region, including soil type, fertility, pH, temperature, humidity, rainfall, sunshine duration, altitude, and topography. The geographical indication data acquisition unit uses high-precision geographical information acquisition equipment and sensors to ensure the accuracy and real-time nature of the data. The biometric data collection unit uses specialized testing instruments to perform isotope ratio testing and trace element spectral analysis on astragalus samples. Through non-destructive testing technology, without damaging the astragalus samples, it quickly and accurately obtains the astragalus's biometric data and establishes a production area characteristic data model. The IoT data acquisition unit deploys IoT devices such as temperature and humidity sensors, light sensors, and CO2 concentration sensors in the Hengshan Astragalus cultivation process to collect real-time temperature, humidity, light intensity, and CO2 concentration data of the cultivation environment, and uploads the data to the blockchain in real time via wireless networks; the IoT data acquisition unit uses low-power, high-stability sensors and communication modules to ensure the continuity and reliability of data collection.

3. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: The component content detection unit uses high performance liquid chromatography and mass spectrometry analysis instruments to determine the effective component content of the harvested astragalus samples, compares the determination results with the quality standards of Hengshan astragalus, and determines the quality grade of the astragalus.

4. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: The abnormal data monitoring unit monitors the data of each unit of the data acquisition module in real time and, by setting a reasonable data threshold range, issues an early warning signal in a timely manner once abnormal fluctuations in geographical indication data, biometric data or IoT data are detected; When market risk reaches a certain level in the market risk analysis unit, an early warning will be issued to relevant production and sales companies and regulatory authorities so that response strategies can be formulated in advance.

5. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: The fusion formula of the weighted random data model in the multi-source data fusion submodule is: Among them, S represents the origin similarity score, n is the number of data types, and w i is the weight of the i-th data, x i is the value of the i-th data after preprocessing; w i The importance of each feature data to the origin identification of Hengshan Astragalus is dynamically adjusted, and the optimal weight value is determined through machine learning algorithm, training and optimization based on a large amount of historical data.

6. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: The calculation formula of the similarity calculation algorithm in the origin matching calculation submodule is: Among them, Sim represents the matching degree, m is the data dimension, x i is the target Astragalus sample data, y i It is the benchmark data in the Hengshan Geographical Indication Database; If the matching degree in the result output submodule reaches the set threshold, the astragalus sample is determined to be from Hengshan origin and a detailed traceability report is generated; otherwise, it is determined to be not from Hengshan origin; at the same time, the traceability result is sent to the blockchain evidence storage module.

7. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: The hash algorithm in the data hash processing unit adopts the highly secure SHA-256 algorithm to ensure the integrity and non-tampering of the data; its formula is: H=SHA-256(M) Where M is the input message. By performing complex operations on the input message M, a hash value H with a fixed length of 256 bits is generated.

8. The Hengshan Astragalus origin traceability system according to claim 1, characterized in that: When verifying on the mobile phone side, the verification interface unit quickly verifies the authenticity of the traceability information through a simplified verification model and displays detailed traceability results.

9. A Hengshan Astragalus origin tracing algorithm, characterized by: The following steps are involved: Step 1: Obtain biometric data of the target Astragalus sample, including isotopes and trace elements, and retrieve benchmark data from the Hengshan Geographical Indication Database; Step 2: Preprocess the collected geographical indication data, biometric data and IoT data; Step 3: Use the weighted random data model to perform multi-source data fusion on the pre-processed data and calculate the origin similarity score; Step 4: Calculate the matching degree between the target Astragalus sample and the Hengshan origin through a similarity calculation algorithm; Step 5: Output the traceability results based on the matching degree, and store the traceability information on the blockchain after hashing.

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