Block chain-based corn breeding material electronic tag tracing method and system

Abstract

The invention relates to the technical field of application of block chains to agricultural breeding, and discloses a block chain-based corn breeding material electronic tag tracing method and system, and the method comprises the steps: obtaining the basic attribute and complete-cycle cultivation data of a corn breeding material, and generating an electronic tag code; standardizing the cultivation data to obtain a stage feature data set; calling reference breeding material data from the block chain node, counting feature identification frequency and calculating an association weight value; generating an identification matching value of the reference material based on the weight value; calculating the stage feature correlation degree of the reference material and the current material; and calculating the final traceability credibility in combination with the correlation degree and the identification matching value, and selecting the reference material with the highest credibility as the traceability basis. The system comprises an information acquisition module, a data processing module, a stored evidence calling module and the like. According to the method, breeding data non-tampering evidence storage is realized through a block chain technology, the traceability accuracy is improved by combining dynamic weight and correlation analysis, and an efficient and credible solution is provided for breeding material management.

Description

Technical Field

[0001] This invention relates to the field of blockchain application in agricultural breeding technology, specifically to a blockchain-based electronic tag traceability method and system for maize breeding materials. Background Technology

[0002] In the development of modern agriculture, maize, as a globally important food, feed, and industrial raw material crop, relies heavily on the scientific and standardized breeding practices to ensure food security and improve agricultural production efficiency. Accurate traceability of maize breeding materials provides solid data support for variety selection, quality control, and intellectual property protection, thus becoming a key research direction in the field of breeding.

[0003] Traditional methods of tracing maize breeding materials rely primarily on paper records and manual management. In practice, paper records are susceptible to environmental factors such as dampness and pests, leading to blurred or even lost data, severely impacting data integrity and accuracy. Simultaneously, manual management suffers from arbitrary operations and non-standard recording practices, making it difficult to effectively guarantee data authenticity. Furthermore, storing paper records requires significant space, and as the volume of breeding material data continues to increase, management difficulty and costs rise exponentially.

[0004] With the development of information technology, some breeding units have begun to use electronic data management systems for the traceability of maize breeding materials. However, most of these systems are built on centralized databases, which pose risks such as data tampering and poor security. Once the database is attacked maliciously or due to operational errors by administrators, the authenticity and reliability of the data will be seriously threatened. Furthermore, centralized systems have significant limitations in data sharing; data between different breeding units and research institutions is difficult to exchange efficiently, creating "data silos" that hinder the integration of breeding resources and collaborative innovation.

[0005] Blockchain technology, with its decentralized, immutable, and traceable characteristics, has brought new solutions to the traceability of maize breeding materials. However, existing blockchain-based traceability technologies for maize breeding materials still have shortcomings in data processing and accuracy. For example, when analyzing breeding material data, there is a lack of scientific and reasonable methods for effective data screening and weight allocation, resulting in low credibility of traceability results. When calculating the correlation between different breeding materials, the methods are relatively simplistic and cannot comprehensively and accurately reflect the actual connections between them, failing to meet the increasingly complex traceability accuracy requirements of maize breeding work. Therefore, there is an urgent need for a more efficient, accurate, secure, and reliable blockchain-based electronic tag traceability method and system for maize breeding materials to solve the many problems existing in current technologies and promote the high-quality development of the maize breeding industry. Summary of the Invention

[0006] The purpose of this invention is to provide a blockchain-based electronic tagging method and system for tracing maize breeding materials, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-based electronic tag traceability method and system for maize breeding materials, the method comprising:

[0008] The process involves acquiring basic attribute information and full-cycle breeding process data for maize breeding materials, extracting feature identifiers from the basic attribute information, and generating electronic tag codes. The full-cycle breeding process data is standardized to obtain a stage feature dataset. Historically stored reference breeding material data, including reference basic attributes, reference stage feature sets, and corresponding electronic tag storage records, is retrieved from the blockchain node database. The frequency of occurrence of the feature identifiers in the reference basic attributes is statistically analyzed, and the frequency percentage of the feature identifiers is calculated based on this frequency. The reciprocal of the frequency percentage is used as the association weight value of the feature identifiers, and an identifier matching value is generated for each reference breeding material based on this association weight value. The correlation degree between the reference stage feature set and the stage feature dataset for each reference breeding material is calculated. The final traceability credibility is calculated based on the correlation degree and identifier matching value of each reference breeding material. The electronic tag storage record of the reference breeding material with the highest credibility is used as the traceability reference for the current maize breeding material.

[0009] Preferably, generating identifier matching values ​​for each reference breeding material based on the association weight values ​​includes:

[0010] The sum of the association weight values ​​of the elements in the reference basic attributes of the reference breeding material that are the same as the feature identifier of the current maize breeding material is calculated as the identifier matching value of the reference breeding material.

[0011] Preferably, calculating the correlation between the reference stage feature set of each reference breeding material and the stage feature dataset includes:

[0012] Calculate the sub-association degree between the feature subsets of each reference stage of the reference breeding material and the feature data subsets of each stage of the current maize breeding material to obtain the sub-association degree set of the reference breeding material.

[0013] Preferably, calculating the correlation between the reference stage feature set of each reference breeding material and the stage feature dataset further includes:

[0014] A first threshold interval is determined based on the maximum value of the sub-association degree in the sub-association degree set of different reference breeding materials; the proportion of the number of sub-association degrees in the sub-association degree set of the reference breeding material located in the first threshold interval is calculated to obtain the association ratio value of the reference breeding material; the average value of the sub-association degrees in the sub-association degree set of the reference breeding material located in the first threshold interval is calculated; the association degree of the reference breeding material is calculated based on the association ratio value and the average value.

[0015] Preferably, the final traceability credibility is calculated based on the correlation degree and identifier matching value of each reference breeding material, including:

[0016] The product of the relevance and the identifier matching value is used as the final traceability credibility.

[0017] Preferably, the present invention also includes a blockchain-based electronic tag traceability system for maize breeding materials, the system comprising:

[0018] The system comprises the following modules: an information acquisition module for acquiring basic attribute information and full-cycle breeding process data of maize breeding materials, extracting feature identifiers from the basic attribute information and generating electronic tag codes; a data processing module for standardizing the full-cycle breeding process data to obtain a stage feature dataset; a data storage retrieval module for retrieving historically stored reference breeding material data from the blockchain node database, the reference breeding material data including reference basic attributes, reference stage feature sets, and corresponding electronic tag storage records; a frequency statistics module for counting the frequency of occurrence of the feature identifiers in the reference basic attributes and calculating the frequency proportion of the feature identifiers based on the frequency of occurrence; a weight generation module for using the reciprocal of the frequency proportion as the association weight value of the feature identifiers and generating an identifier matching value for each reference breeding material based on the association weight value; an association calculation module for calculating the association degree between the reference stage feature set of each reference breeding material and the stage feature dataset; a credibility calculation module for calculating the final traceability credibility based on the association degree and identifier matching value of each reference breeding material; and a traceability reference module for using the electronic tag storage record of the reference breeding material with the highest credibility as the traceability reference basis for the current maize breeding material.

[0019] Preferably, the weight generation module includes:

[0020] The identifier matching value calculation subunit is used to calculate the sum of the association weight values ​​of the elements in the reference basic attributes of the reference breeding material that are the same as the feature identifiers of the current maize breeding material, and use them as the identifier matching value of the reference breeding material.

[0021] Preferably, the associated calculation module includes:

[0022] The sub-association degree calculation unit is used to calculate the sub-association degree between the feature subsets of each reference stage of the reference breeding material and the feature data subsets of each stage of the current maize breeding material, so as to obtain the sub-association degree set of the reference breeding material.

[0023] Preferably, the correlation calculation module further includes:

[0024] The threshold interval determination unit is used to determine a first threshold interval based on the maximum value of the sub-associations in the sub-associations set of different reference breeding materials; the proportion calculation unit is used to calculate the proportion of the number of sub-associations in the sub-associations set of the reference breeding material that are located in the first threshold interval to the total number of sub-associations in the sub-associations set, thereby obtaining the association proportion value of the reference breeding material; the average value calculation unit is used to calculate the average value of the sub-associations in the sub-associations set of the reference breeding material that are located in the first threshold interval; and the association degree calculation unit is used to calculate the association degree of the reference breeding material based on the association proportion value and the average value.

[0025] Preferably, the credibility calculation module includes:

[0026] The credibility calculation subunit is used to take the product of the correlation degree and the identifier matching value as the final source tracing credibility.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] The blockchain-based electronic tagging method and system for tracing maize breeding materials proposed in this invention significantly improves the accuracy, reliability, and security of maize breeding material traceability through multi-dimensional data processing and scientific calculation models, bringing many positive impacts to the maize breeding industry.

[0029] In terms of data acquisition and processing, the system comprehensively acquires basic attribute information and full-cycle breeding process data of maize breeding materials, and standardizes the data to ensure its standardization and consistency. Simultaneously, it extracts feature identifiers from the basic attribute information and generates electronic tag codes, giving each breeding material a unique "digital ID card," enabling precise identification and management of the breeding materials. This comprehensive and standardized data acquisition and processing method provides a solid data foundation for subsequent traceability analysis.

[0030] To improve the accuracy of source tracing, this invention innovatively introduces a method for calculating the correlation weight value of feature identifiers and the correlation degree of stage feature datasets. By statistically analyzing the frequency of feature identifiers in reference basic attributes, calculating their frequency proportion, and taking the reciprocal as the correlation weight value, the uniqueness and importance of feature identifiers in different breeding materials can be accurately reflected. Based on this, the identifier matching value of each reference breeding material is calculated, which can effectively measure the similarity between the current maize breeding material and the reference breeding material in terms of basic attributes. At the same time, by calculating the correlation degree between the reference stage feature set and the stage feature dataset, the relationship between breeding materials is further analyzed from the perspective of breeding process data. This comprehensive analysis from the two dimensions of basic attributes and breeding process, compared with traditional methods, can more comprehensively and accurately determine the similarity between the current maize breeding material and the reference breeding material, thereby significantly improving the accuracy of source tracing results.

[0031] At the application level of blockchain technology, the decentralized and tamper-proof characteristics of blockchain are fully utilized to store reference breeding material data in a blockchain node database. This not only ensures the security and credibility of the data but also enables efficient data sharing and collaboration among different participants. Breeding units and research institutions can conduct traceability analysis based on the evidence-based data on the blockchain, breaking down "data silos," promoting the integration and exchange of breeding resources, and driving collaborative innovation in the maize breeding industry.

[0032] In terms of system functionality, the traceability system built based on this method achieves fully automated management of the entire process from data collection and processing to traceability analysis by setting up multiple functional modules such as information collection, data processing, and evidence storage and retrieval. The clear division of labor and collaborative work among the modules effectively improves the efficiency of breeding material traceability and reduces manual management costs. At the same time, the system's modular design gives it good scalability and compatibility, allowing for functional upgrades and optimizations based on actual needs to adapt to the ever-evolving needs of maize breeding.

[0033] The blockchain-based electronic tagging method and system for tracing maize breeding materials of this invention has significant advantages in many aspects, including data processing, traceability accuracy, technology application, and system functions. It can provide a more efficient, accurate, and secure traceability solution for the maize breeding industry and has important practical significance for promoting the modernization of the maize breeding industry. Attached Figure Description

[0034] Figure 1 This is a schematic diagram illustrating the working principle of the blockchain-based electronic tag traceability method for maize breeding materials described in this invention.

[0035] Figure 2 Detailed design diagram for correlation degree calculation;

[0036] Figure 3 A schematic diagram illustrating the working principle of an electronic tag traceability system for maize breeding materials.

[0037] Figure 4 This is a detailed design diagram of the associated calculation module. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Please see Figures 1-4 The present invention relates to a blockchain-based electronic tagging method and system for tracing maize breeding materials, the specific implementation steps of which are as follows:

[0040] The process involves acquiring basic attribute information and full-cycle breeding process data for maize breeding materials, extracting feature identifiers from the basic attribute information, and generating electronic tag codes. Basic attribute information includes, but is not limited to, the variety name, parental information, seed source, and morphological characteristics of the maize breeding materials. Full-cycle breeding process data covers environmental data (such as temperature, humidity, light, and soil nutrients), agricultural operation data (such as fertilization, irrigation, and pest and disease control), and growth and development data (such as plant height, stem diameter, leaf area, and ear traits) at each stage from sowing, seedling raising, growth, flowering, fruiting, to harvest. By analyzing the basic attribute information, unique feature identifiers that can identify the maize breeding material are extracted, such as specific gene sequence fragments or combinations of morphological characteristic parameters, and electronic tag codes are generated according to certain coding rules.

[0041] The data from the entire breeding process were standardized to obtain stage feature datasets. Since the data came from different acquisition devices and time periods, there might be issues such as inconsistent data formats and units. Therefore, standardization was necessary. Standardization included data cleaning (removing noise and outliers), data transformation (converting data from different formats to a unified format and data from different units to standard units), and data normalization (mapping the data to a specific range). Then, based on the growth cycle of maize breeding materials, the standardized data was divided into different stages, such as seedling stage, ear stage, and grain-filling stage, with each stage forming a stage feature dataset.

[0042] The system retrieves historically documented reference breeding material data from the blockchain node database. This data includes basic reference attributes, reference stage feature sets, and corresponding electronic tag records. The blockchain node database stores a large amount of historically documented maize breeding material data, which is immutable and traceable. Through the blockchain's consensus mechanism and smart contracts, the system retrieves reference breeding material data related to the current maize breeding material from the node database.

[0043] The frequency of occurrence of statistical feature identifiers in reference basic attributes is used to calculate the frequency percentage of feature identifiers. For each reference basic attribute of a reference breeding material, the number of times the feature identifier of the current maize breeding material appears in it is counted. Then, the total frequency of the feature identifier in all reference basic attributes is calculated and divided by the total number of reference basic attributes to obtain the frequency percentage of the feature identifier.

[0044] The reciprocal of the frequency percentage is used as the association weight value for the feature identifier. Identifier matching values ​​are generated for each reference breeding material based on these association weight values. The lower the frequency percentage of a feature identifier, the more unique the feature identifier is in the reference breeding material, and the larger its association weight value. The identifier matching value for each reference breeding material is obtained by summing the association weight values ​​of the elements in each reference breeding material that share the same feature identifier as the current maize breeding material.

[0045] Calculate the correlation between the reference stage feature set and the stage feature dataset of each reference breeding material. Using a specific algorithm, such as cosine similarity algorithm or Euclidean distance algorithm, calculate the sub-correlation degree between each reference stage feature subset of the reference breeding material and each stage feature data subset of the current maize breeding material, and obtain the sub-correlation degree set of the reference breeding material.

[0046] The final traceability reliability is calculated based on the correlation degree and identifier matching value of each reference breeding material. The final traceability reliability is obtained by multiplying the correlation degree by the identifier matching value.

[0047] The electronic tag records of the reference breeding material with the highest credibility will be used as the traceability reference for current maize breeding materials. By comparing the final traceability credibility of each reference breeding material, the reference breeding material with the highest credibility will be selected, and its electronic tag records will serve as the traceability reference for current maize breeding materials.

[0048] Example 1:

[0049] This embodiment mainly illustrates how to generate identifier matching values ​​for each reference breeding material based on association weight values. The specific implementation method is as follows:

[0050] The method for obtaining the reference basic attributes of reference breeding materials needs to be clearly defined. When retrieving historically stored reference breeding material data from the blockchain node database, the reference basic attributes include data of the same category as the basic attribute information of the current maize breeding material, such as variety name, parent combination, seed generation, morphological characteristic parameters, and physiological indicators. This data is stored in a structured form in the blockchain nodes and, after being retrieved through the smart contract interface, forms a set of reference data for comparison. For each reference breeding material to be processed, the system automatically extracts its corresponding reference basic attributes as the basis for comparison with the feature identifiers of the current maize breeding material.

[0051] The system compares the feature identifiers with elements of the reference baseline attributes. The feature identifiers of current maize breeding materials are key data elements with uniqueness or high discriminative power extracted from their baseline attribute information. For example, if the baseline attribute information contains specific gene marker sequences, unique combinations of plant height and leaf shape parameters, or unique parental hybridization information, the system will use a preset feature extraction algorithm to filter out feature identifiers that can represent the identity of the breeding material. During the comparison process, the system will match each element in the reference baseline attributes one by one with the feature identifiers of the current maize breeding material, identifying the identical elements.

[0052] After identifying identical elements, the association weight value corresponding to that element needs to be obtained. The association weight value is determined based on the frequency of the feature identifier in the reference basic attributes. Specifically, the system first counts the total number of times the feature identifier appears in the reference basic attributes of all reference breeding materials, and then calculates the proportion of this frequency in the total number of reference basic attribute elements, i.e., the frequency proportion. The association weight value is the reciprocal of this frequency proportion. For example, assuming that the frequency proportion of a certain feature identifier in all reference basic attributes is 0.1, then its association weight value is 1 ÷ 0.1 = 10. The logic of this calculation method is that the lower the frequency of a feature identifier in the reference basic attributes, the higher its uniqueness, and the greater its reference value in the tracing process, thus assigning it a higher association weight value.

[0053] After obtaining the association weight values ​​for each identical element, the system sums these weight values ​​to generate the identifier matching value for the reference breeding material. Specifically, for each reference breeding material, the association weight values ​​of all elements in its reference basic attributes that are identical to the feature identifier of the current maize breeding material are added together; the sum is the identifier matching value for that reference breeding material. For example, if a reference breeding material has three elements in its reference basic attributes that are identical to the feature identifier of the current maize breeding material, with corresponding association weight values ​​of 8, 10, and 6 respectively, then the identifier matching value for that reference breeding material is 8 + 10 + 6 = 24.

[0054] This method of generating identifier matching values ​​has a clear logical basis and practical significance. Logically, determining the association weight value by the reciprocal of the frequency proportion fully reflects the impact of the uniqueness of the feature identifier on the matching results. Feature identifiers with high uniqueness have a low probability of appearing in the reference basic attributes, resulting in a high association weight value and thus a larger proportion in the identifier matching value. This allows the final identifier matching value to more accurately reflect the degree of matching between the reference breeding material and the current maize breeding material in terms of basic attributes.

[0055] From a practical application perspective, this method effectively improves the accuracy and reliability of traceability. In the traceability process of maize breeding materials, different characteristic markers have varying degrees of importance for material identification. For example, certain gene markers may be unique to a specific variety, occurring at extremely low frequencies, thus possessing high reference value in traceability; while some common morphological parameters, such as the typical plant height range, may appear in multiple varieties, exhibiting higher frequencies and relatively lower association weights. Through this weighted summation method, the system can automatically distinguish between characteristic markers of different importance, avoiding traceability errors caused by treating all characteristic markers equally.

[0056] The process is both operable and efficient in its implementation. Regarding data processing, the data in the blockchain node database is immutable, ensuring the authenticity and reliability of the referenced basic attribute data. Through preset algorithms and processes, the system can automatically complete the entire process from data retrieval, feature comparison, weight calculation to summing and generating matching identifier values, without manual intervention, thus improving processing efficiency and accuracy.

[0057] In the specific technical implementation, the following aspects need to be noted: First, the feature extraction algorithm needs to be optimized according to the characteristics of maize breeding materials to ensure that the extracted feature identifiers have sufficient distinguishability and representativeness; second, when counting the frequency of feature identifiers, the integrity and accuracy of the data need to be considered to avoid deviations in frequency percentage calculation due to missing or incorrect data; third, when comparing elements, the consistency of data formats needs to be handled well. For example, the same feature in different units or different expressions needs to be standardized and converted before comparison.

[0058] Example 2:

[0059] This embodiment details the steps for calculating the correlation between the reference stage feature set and the stage feature dataset of each reference breeding material to achieve accurate traceability. This step provides crucial evidence for calculating traceability reliability through multi-stage quantitative analysis of the breeding process data. The specific implementation method is as follows:

[0060] The reference stage feature set of the reference breeding material and the stage feature dataset of the current maize breeding material need to be structurally divided. The entire breeding cycle of maize typically covers multiple growth and development stages, such as the seedling stage (from sowing to jointing), the tasseling stage (from jointing to tasseling), and the flowering and grain-filling stage (from tasseling to maturity). Based on the biological characteristics of maize growth and the stage division standards in agricultural production practice, the system will divide the reference stage feature set and the stage feature dataset into corresponding stage feature subsets. For example, the seedling stage feature subset in the reference stage feature set includes environmental data (average daily temperature, soil moisture, light duration, etc.), agricultural operation data (sowing depth, fertilizer type and amount, irrigation frequency, etc.), and growth and development data (plant height growth rate, number of leaves, stem diameter, etc.) of the reference breeding material during the seedling stage; similarly, the seedling stage feature dataset subset in the stage feature dataset includes similar data of the current maize breeding material during the seedling stage. This stage-based division method allows subsequent correlation calculations to better reflect the actual growth patterns of maize and avoids analytical biases caused by the mixing of data from different growth stages.

[0061] For each corresponding subset of stage features and subset of stage feature data, sub-association degree is calculated. The calculation of sub-association degree requires an algorithm capable of quantifying data similarity; common algorithms include cosine similarity and Euclidean distance. Taking cosine similarity as an example, this algorithm treats each subset of stage features or subset of data as vectors in a high-dimensional space, and measures their similarity by calculating the cosine of the angle between two vectors. Assuming the seedling feature subset of the reference breeding material is represented as vector A(a1,a2,a3,...,an), and the seedling feature data subset of the current maize breeding material is represented as vector B(b1,b2,b3,...,bn), where each dimension corresponds to a specific data indicator (such as temperature, humidity, etc.), the calculation logic of cosine similarity is to measure the closeness of the two vectors in direction; the closer the value is to 1, the higher the similarity. Euclidean distance, on the other hand, calculates the absolute distance between two vectors in a high-dimensional space; the smaller the distance, the smaller the data difference, and the higher the similarity.

[0062] In practical applications, the system selects the appropriate algorithm based on the data characteristics. For continuous environmental data (such as temperature and humidity) and growth and development data (such as plant height and leaf area), the Euclidean distance algorithm can intuitively reflect the absolute differences in the data; for multi-dimensional agricultural operation data (such as fertilizer combinations and pest and disease control measures), the cosine similarity algorithm is more suitable for measuring the similarity of data patterns. The system automatically matches the appropriate algorithm for different types of data indicators to ensure the accuracy of sub-association calculations. For example, when processing temperature data, Euclidean distance is used to calculate the difference in daily average temperature; when processing fertilizer type data, different fertilizer combinations are converted into vector form, and then cosine similarity is used to calculate pattern similarity.

[0063] For each reference breeding material, the system sequentially calculates the sub-association degree between its feature subsets at each stage and the feature data subsets at each stage. For example, if the reference stage feature set of reference breeding material A is divided into three subsets—seedling stage, ear stage, and grain-filling stage—and the current maize breeding material's stage feature dataset is also divided into three subsets accordingly, the system will calculate the sub-association degree between the seedling stage subset and the seedling stage data subset, the ear stage subset and the ear stage data subset, and the grain-filling stage subset and the grain-filling stage data subset, thus obtaining a set containing these three sub-association degrees. This set of sub-association degrees comprehensively reflects the similarity of the reference breeding material's data at each growth stage to the current maize breeding material's cultivation process data.

[0064] This correlation calculation process has a clear logic for agricultural application. The characteristics of maize breeding materials are not only reflected in their basic attributes, but also in their full-cycle breeding process data, which is crucial for reflecting varietal characteristics, growth patterns, and environmental adaptability. Different varieties, or even the same variety under different breeding conditions, may exhibit differences in their performance at different growth stages. By calculating sub-correlation degrees in stages, it is possible to capture the specific manifestations of these differences at different stages. For example, some varieties may be more sensitive to temperature during the seedling stage, while others may have more specific water requirements during the flowering and grain-filling stage. This staged analysis method allows for a more detailed comparison of the breeding process during the tracing process, improving the reliability of the tracing results.

[0065] At the technical implementation level, attention needs to be paid to data standardization. Since data from different stages and of different types may have different units and value ranges (e.g., temperature in °C, light duration in hours, plant height in centimeters), the system will standardize the data before calculating sub-associations. Standardization includes data cleaning (removing outliers and imputing missing values), unit unification, and normalization (mapping data to the [0,1] interval or a specific range) to ensure the comparability of data across different dimensions. For example, temperature data is converted from °C to a standardized value, and light duration is converted from hours to a normalized proportional value to avoid interference from unit differences in the correlation calculation results.

[0066] The segmentation of stage feature subsets needs to be optimized by incorporating knowledge from the agricultural field. The system can collaborate with agricultural experts to establish more refined stage segmentation standards, such as further dividing the seedling stage into germination, three-leaf stage, and pre-jointing stage, making the stage feature subsets more closely aligned with the physiological stages of maize growth. Simultaneously, the time nodes and characteristic indicators for stage segmentation can be dynamically adjusted for different maize varieties (such as early-maturing and late-maturing varieties), improving the adaptability of stage segmentation.

[0067] Regarding data storage and retrieval, the reference stage feature set, as part of the blockchain-based evidence data, is guaranteed in terms of integrity and immutability. When the system retrieves the reference stage feature set through the blockchain smart contract interface, it simultaneously verifies the hash value of the data to ensure that the data has not been tampered with. The current stage feature dataset for maize breeding materials is generated from real-time collected data after standardization processing, stored in the system's local database, and associated with the electronic tag code of the blockchain-based evidence storage to ensure data traceability.

[0068] Example 3:

[0069] When calculating the correlation between the reference stage feature set and the stage feature dataset for each reference breeding material, in addition to calculating the sub-correlation set, it is necessary to further determine the correlation through threshold interval screening and statistical analysis. The specific implementation method is as follows:

[0070] The first threshold interval is determined based on the maximum sub-association degree value in the sub-association degree set of different reference breeding materials. After calculating the sub-association degree for each stage of all reference breeding materials, the system collects the maximum sub-association degree values ​​from the sub-association degree sets of all reference breeding materials. For example, assuming there are 100 reference breeding materials, and each reference breeding material may have sub-association degrees for three stages (e.g., seedling stage, heading stage, and grain-filling stage), the system will extract the maximum value for each reference breeding material from these 300 sub-association degrees, forming a set containing 100 maximum values. Next, the system needs to determine a reasonable range as the first threshold interval. This interval is usually determined based on the statistical characteristics of these maximum values. For example, the mean and standard deviation of these maximum values ​​are calculated, and an interval is formed by extending the mean upwards and downwards by a certain multiple of the standard deviation; or the minimum and maximum values ​​of the first certain proportion (e.g., the first 20%) of these maximum values ​​are taken as the upper and lower limits of the interval. The first threshold interval determined in this way can cover the relatively high sub-association degree levels exhibited in most reference breeding materials, thereby screening out the relatively high sub-association degree portions of each reference breeding material.

[0071] After determining the first threshold interval, for each reference breeding material, the proportion of sub-associations within the first threshold interval in its sub-association set is calculated to determine the association ratio. For example, if a reference breeding material has three sub-associations in its sub-association set, namely 0.7, 0.8, and 0.5, and assuming the first threshold interval is [0.6, 0.9], then 0.7 and 0.8 are within this interval, totaling 2, and the total number is 3. The association ratio is 2 ÷ 3 ≈ 0.67. This ratio reflects the proportion of higher-level sub-associations in the sub-associations of this reference breeding material at each stage. A higher ratio indicates that the reference breeding material has a higher similarity to the current maize breeding material's breeding process data at more stages.

[0072] Calculate the average sub-associations within the first threshold interval of the sub-association set of the reference breeding material. Continuing with the example above, the sub-associations within the interval are 0.7 and 0.8, and their average value is (0.7 + 0.8) ÷ 2 = 0.75. This average value reflects the average similarity of the reference breeding material at a higher similarity stage, and can further quantify its matching level with current maize breeding materials in terms of breeding process data.

[0073] The correlation degree of the reference breeding material is calculated based on the correlation ratio and the average value. The system needs to comprehensively consider these two indicators to form a correlation degree value that can fully reflect the similarity between the reference breeding material and the current maize breeding material's breeding process data. A weighted summation method can be used, where the correlation ratio and the average value are each assigned a certain weight. For example, assuming the weight of the correlation ratio is 0.4 and the weight of the average value is 0.6, the correlation degree of the reference breeding material is 0.67 × 0.4 + 0.75 × 0.6 = 0.268 + 0.45 = 0.718. The weight setting needs to be combined with the actual application scenario and data characteristics. Typically, a weight allocation scheme that makes the correlation degree calculation result more consistent with the actual similarity of breeding materials can be determined through prior analysis of a large amount of historical data.

[0074] This method of calculating correlation through threshold interval screening and statistical analysis has clear logic and practical significance. Logically, the maximum sub-correlation value reflects the highest similarity level between the reference breeding material and the current maize breeding material at a certain stage. Threshold intervals determined based on these maximum values ​​can effectively screen out relatively high sub-correlation values ​​among the reference breeding materials, avoiding interference from lower sub-correlation values, thus focusing more on stages with high similarity. The correlation ratio value quantitatively reflects the proportion of stages with high similarity, while the average value reflects the degree of similarity at these stages. Combining both allows for a comprehensive assessment of the overall correlation between the reference breeding material and the current maize breeding material in terms of breeding process data.

[0075] In practical applications, this method improves the accuracy and reliability of correlation calculations. In maize breeding, different reference breeding materials and current maize breeding materials may exhibit high similarity at certain stages, while showing lower similarity at others. Filtering out stages with higher similarity using threshold intervals avoids situations where low similarity in individual stages leads to an underestimation of the overall correlation, or vice versa. For example, a reference breeding material might show high similarity to current maize breeding materials at the seedling and tasseling stages, but lower similarity at the flowering and grain-filling stage. Directly averaging all sub-correlation scores might lower the overall correlation. However, by filtering out the higher sub-correlation scores at the seedling and tasseling stages, the calculation more accurately reflects the main similarity stages.

[0076] In the technical implementation process, the following aspects need to be considered: First, the determination of the first threshold interval needs to be scientific and reasonable, and should not be too wide or too narrow. An overly wide interval will result in insignificant screening effects, including too many sub-associations with low similarity; an overly narrow interval may miss some sub-associations with practical reference value. Therefore, it is necessary to determine an appropriate interval range based on the distribution characteristics of historical data through statistical analysis. Second, when calculating the association ratio and average value, it is necessary to ensure the accuracy of the data, especially whether the calculation of each sub-association in the sub-association set is correct, and whether it is correctly determined whether it falls within the first threshold interval. Third, the weight setting needs to be fully tested and verified. Different weight allocations will affect the final association calculation results. It is necessary to combine the actual breeding scenario, through communication with agricultural experts or analysis of historical traceability cases, to determine the optimal weight combination.

[0077] The system implementation requires good operability and scalability. It needs to be able to automatically collect the maximum sub-association values ​​of all reference breeding materials, perform statistical analysis, and determine the first threshold interval; automatically filter, count, and average the sub-association sets of each reference breeding material; and automatically calculate the correlation degree based on preset weights. Furthermore, when new reference breeding material data is added to the blockchain node database, the system needs to be able to dynamically adjust the first threshold interval to adapt to changes in data distribution, ensuring the accuracy and timeliness of the correlation degree calculation.

[0078] Example 4:

[0079] Calculating the final traceability credibility based on the correlation and identifier matching values ​​of each reference breeding material is a key step in integrating multi-dimensional information to determine the optimal traceability reference. This step provides a comprehensive evaluation of the traceability results by quantifying and combining the similarity of the breeding process with the matching degree of basic attributes. The specific implementation method is as follows:

[0080] Taking a certain maize breeding material A as an example, assume the system retrieves three reference breeding materials B, C, and D from the blockchain node database. First, it's necessary to clarify the process of obtaining the correlation degree and identifier matching value for each reference breeding material. The correlation degree of reference breeding material B is obtained by calculating the correlation between its reference stage feature set and the stage feature dataset of the current breeding material A. For example, its stage sub-correlation degree set is [0.8, 0.7, 0.6]. After filtering through threshold intervals, the correlation ratio is determined to be 0.67, the average is 0.75, and the final correlation degree is 0.718 (assuming the weight allocation is 0.4 for the correlation ratio and 0.6 for the average). Simultaneously, the identifier matching value of reference breeding material B is obtained by summing the correlation weight values ​​of elements in its reference basic attributes that have the same feature identifier as breeding material A. Assuming the correlation weight values ​​of elements with the same feature identifier are 5, 3, and 4 respectively, the sum is 12, i.e., the identifier matching value is 12.

[0081] The correlation calculation process for reference breeding material C is similar. Assuming its sub-correlation set is [0.7, 0.9, 0.5], after threshold interval filtering, the correlation ratio is 0.67 (two values ​​within the interval), the average is 0.8, and the correlation is 0.67 × 0.4 + 0.8 × 0.6 = 0.308 + 0.48 = 0.788. Regarding its identifier matching value, assuming there are two identical feature identifiers in the reference basic attributes, with correlation weights of 6 and 7 respectively, totaling 13, i.e., the identifier matching value is 13.

[0082] The sub-association set of reference breeding material D is [0.5, 0.6, 0.8]. After threshold interval screening, the association ratio is 0.67 (two values ​​are within the interval), the average is 0.7, and the association degree is 0.67×0.4+0.7×0.6=0.268+0.42=0.688. Regarding the identifier matching value, it is assumed that the sum of the association weight values ​​for identifiers with the same feature is 10, i.e., the identifier matching value is 10.

[0083] After obtaining the correlation and identifier matching values ​​of each reference breeding material, the system needs to perform a comprehensive calculation of these two indicators to obtain the final traceability credibility. Specifically, the credibility calculation module multiplies the correlation and identifier matching value of each reference breeding material to obtain the final traceability credibility. Taking reference breeding material B as an example, its correlation is 0.718, its identifier matching value is 12, and the final traceability credibility is 0.718×12≈8.616; the correlation of reference breeding material C is 0.788, its identifier matching value is 13, and the final traceability credibility is 0.788×13≈10.244; the correlation of reference breeding material D is 0.688, its identifier matching value is 10, and the final traceability credibility is 0.688×10≈6.88.

[0084] By comparing the final traceability credibility of the three reference breeding materials, reference breeding material C had the highest credibility of 10.244. Therefore, the system will use the electronic tag record of reference breeding material C as the traceability reference for the current maize breeding material A. This record contains the basic attribute information of reference breeding material C, the full-cycle breeding process data, and the corresponding electronic tag code, etc. This information can be used to verify the identity of breeding material A and trace its breeding history.

[0085] This method of multiplying the correlation degree and the identifier matching value to calculate the final traceability credibility has a clear logical basis and practical application value. Logically, the correlation degree reflects the similarity between the reference breeding material and the current breeding material in terms of breeding process data, while the identifier matching value reflects the degree of matching between the two in terms of basic attributes. Both reflect the reference value of the reference breeding material from different dimensions. Multiplying them can achieve complementary advantages: when a reference breeding material has a high correlation degree but a low identifier matching value, it indicates that their breeding processes are similar but their basic attributes differ significantly, and the credibility after multiplication will not be too high; conversely, if the identifier matching value is high but the correlation degree is low, it indicates that their basic attributes are similar but their breeding processes differ significantly, and the credibility will also be suppressed. Only when both are high will the final credibility be significantly improved, thus ensuring that the selected reference breeding materials have a high degree of consistency with the current breeding materials in multiple dimensions.

[0086] In practical applications, this calculation method effectively avoids the limitations of a single indicator. For example, if screening is based solely on correlation, reference breeding materials with similar cultivation processes but significant differences in basic attributes might be selected. However, these materials may belong to different varieties or batches, making accurate traceability impossible. Similarly, if screening is based solely on identifier matching values, materials with similar basic attributes but significant differences in cultivation processes might be selected. These differences in cultivation processes can lead to different phenotypic expressions, also failing to accurately reflect the history of current breeding materials. By multiplying these two methods, information from different dimensions can be balanced, improving the accuracy of traceability results.

[0087] During the technical implementation process, the following key points need to be noted: First, the numerical ranges of the correlation degree and the identifier matching value need to be reasonably controlled to avoid distortion of the product result due to excessively large or small values. For example, if the correlation degree range is [0,1] and the identifier matching value range is [0,20], then the product result range is [0,20], which facilitates subsequent comparison. Second, it is necessary to ensure the accuracy of the calculation of the correlation degree and the identifier matching value, as any deviation in any indicator will affect the final credibility. Therefore, in the early stages of correlation degree calculation and identifier matching value generation, steps such as data standardization and reasonable determination of threshold ranges must be strictly followed. In addition, the system needs to have efficient numerical calculation capabilities to quickly process the credibility calculation of a large number of reference breeding materials, especially when there is a large amount of evidence data stored in the blockchain node database, the calculation efficiency needs to be improved through algorithm optimization.

[0088] Taking another scenario as an example, suppose the current maize breeding material E contains a unique gene marker. In the reference breeding material F, the association weight of this gene marker is 8 (because the marker appears infrequently in the reference database), and the association degree of reference breeding material F is 0.9 (high similarity of breeding data at different stages). Therefore, its final credibility is 0.9 × 8 = 7.2. While the reference breeding material G has a marker matching value of 10 (more basic attribute matching elements), its association degree is only 0.5 (significant differences in the breeding process), and its final credibility is 0.5 × 10 = 5. Clearly, the reference breeding material F has higher credibility and is more suitable as a source reference. This example demonstrates how this calculation method avoids the shortcomings of a single indicator through comprehensive evaluation.

[0089] Furthermore, the implementation of this process within the system relies on the collaborative work of various modules. The information acquisition module needs to accurately obtain the basic attributes and cultivation data of the current breeding materials; the data processing module needs to complete standardization and stage division; the evidence storage and retrieval module needs to obtain reference data from the blockchain; the frequency statistics and weight generation module needs to accurately calculate the identifier matching value; the correlation calculation module needs to scientifically evaluate the correlation degree; and finally, the credibility calculation module can complete the calculation based on accurate input values. Data interaction between modules must be achieved through standardized interfaces to ensure the accuracy and integrity of data transmission.

[0090] Example 5:

[0091] The corresponding traceability system achieves fully automated processing through the collaborative work of multiple modules. Taking a corn breeding management system developed by an agricultural technology company as an example, its system architecture and the specific implementation methods of each module are as follows:

[0092] The system is deployed on a cloud server and connects to a blockchain node network via an API interface. The underlying database stores real-time data collected from current breeding materials, while the blockchain node database stores historical breeding material information. When breeding new maize breeding materials, the information acquisition module is activated. This module consists of hardware and software: the hardware includes field sensors (such as temperature, humidity, and light intensity sensors), image acquisition equipment (for capturing morphological characteristics such as plant height and leaf shape), and RFID tag readers (for reading basic attributes such as seed batches); the software is responsible for parsing the sensor data and extracting feature identifiers from the basic attribute information. For example, during the sowing stage, the information acquisition module obtains basic attributes such as seed variety name, parent number, and sowing date, extracts specific SNP markers as feature identifiers using gene sequencing equipment, and generates electronic tag codes according to coding rules, such as "MAIZE-20250623-001-ABC123", which includes material type, date, batch, and feature hash value.

[0093] The data processing module standardizes the collected data throughout the entire lifecycle. Taking a maize breeding material from sowing to harvest as an example, the collected data includes the average daily temperature of 25℃ and soil moisture of 60% during the seedling stage, the nitrogen application rate of 15 kg / mu and the irrigation volume of 30 m³ during the ear stage. 3 / mu, including the use of a certain type of insecticide for pest and disease control during the flowering and grain-filling stage. The data processing module first performs outlier detection on the temperature data, removing extreme values ​​exceeding 35℃ (possibly due to sensor malfunction), standardizes the humidity unit to a percentage, and converts nitrogen application and irrigation amounts to standard per-mu units. Then, based on the corn growth cycle, it divides the data into three stages: seedling stage (0-30 days), ear stage (31-60 days), and flowering and grain-filling stage (61-100 days), forming a corresponding feature dataset for each stage. For example, the seedling stage feature dataset includes data such as average daily temperature, humidity, and plant height growth, while the ear stage feature dataset includes data such as fertilizer type, irrigation frequency, and stem diameter growth rate.

[0094] The evidence retrieval module accesses the node database via a blockchain smart contract. Assuming the current breeding material's feature identifier contains a specific gene sequence "ZeamaysL.var.mays", the evidence retrieval module will search the blockchain for all historical evidence data containing that gene sequence. For example, the blockchain node returns three reference breeding materials: Reference Material A (bred in 2023), Reference Material B (bred in 2024), and Reference Material C (bred in 2022). Each reference material includes basic reference attributes (such as variety name and parental combination), a reference stage feature set (breeding data for each stage), and electronic tag evidence records (such as evidence time and block height).

[0095] The frequency statistics module counts the frequency of feature identifiers in the reference basic attributes. Taking the feature identifier "gene sequence X" as an example, it appears once in the basic attributes of reference material A, 0 times in reference material B, and once in reference material C. There are a total of 3 reference materials, and the total number of reference basic attribute elements is 10 (assuming each reference material contains 3-4 basic attribute elements). Then the frequency ratio is 2 ÷ 10 = 0.2, and the association weight is 1 ÷ 0.2 = 5. If the current breeding material has 3 feature identifiers, namely gene sequence X (weight 5), plant height parameter Y (weight 3), and leaf shape parameter Z (weight 4), then the identifier matching value of each reference material is the sum of the weights of the same feature identifier.

[0096] The weight generation module calculates the identifier matching value for each reference material. Reference material A's basic reference attributes include gene sequence X and leaf shape parameter Z, with an identifier matching value of 5 + 4 = 9; reference material B includes plant height parameter Y, with an identifier matching value of 3; and reference material C includes gene sequence X, with an identifier matching value of 5. This module iterates through the elements in the basic reference attributes, compares them with the current feature identifier, and automatically accumulates the weight values ​​for elements with the same identifier.

[0097] The correlation calculation module calculates the correlation degree between the reference stage feature set and the stage feature dataset. Taking reference material A as an example, its sub-correlation degree sets are 0.8 for seedling stage, 0.7 for heading stage, and 0.6 for flowering and grain-filling stage. The system determines the first threshold interval [0.65, 0.9] based on the maximum sub-correlation degree values ​​of all reference materials (e.g., 0.8, 0.9, 0.7). Reference material A has two sub-correlation degrees (0.8, 0.7) within this interval, with a correlation ratio of 2 ÷ 3 ≈ 0.67. The average sub-correlation degree within the interval is (0.8 + 0.7) ÷ 2 = 0.75. Assuming the weights are a correlation ratio of 0.4 and an average of 0.6, the correlation degree is 0.67 × 0.4 + 0.75 × 0.6 = 0.718. The sub-associations of reference material B are 0.7, 0.9, and 0.5, with a total correlation of 0.788; the sub-associations of reference material C are 0.6, 0.8, and 0.5, with a total correlation of 0.688.

[0098] The credibility calculation module multiplies the correlation score by the identifier matching value to obtain the final credibility score. Reference material A has a credibility score of 0.718 × 9 ≈ 6.462, reference material B has 0.788 × 3 ≈ 2.364, and reference material C has 0.688 × 5 ≈ 3.44. Therefore, reference material A has the highest credibility score. The traceability module retrieves the electronic tag storage records of reference material A, including every agricultural operation data point during its cultivation process (such as sowing depth of 5cm, fertilization date, etc.), environmental monitoring data (such as rainfall on a certain date), and the corresponding blockchain hash value, for breeders to view and trace.

[0099] The various modules of the system interact through a data bus: the information acquisition module transmits raw data to the data processing module, and the processed stage feature dataset is sent to the correlation calculation module; the evidence retrieval module transmits the reference data obtained from the blockchain to the frequency statistics module and the correlation calculation module simultaneously; the identifier matching value calculated by the frequency statistics and weight generation module and the correlation degree generated by the correlation calculation module are both transmitted to the credibility calculation module, and the final result is displayed by the traceability reference module.

[0100] In terms of technical implementation, the information acquisition module uses edge computing nodes to perform preliminary cleaning of sensor data in the field, reducing the pressure on cloud transmission; the data processing module uses a distributed computing framework to process large-scale cultivation data in parallel; the evidence storage and retrieval module uses blockchain smart contracts to achieve multi-node data consensus verification, ensuring the immutability of reference data; and the correlation calculation module uses a vector database to store stage feature sets, accelerating the calculation efficiency of sub-correlation degree.

[0101] For example, when breeders need to trace the parental origin of a batch of corn seeds, the system reads the seed electronic tag code through the information collection module, parses the feature identifier, retrieves reference material data from the blockchain, and after calculation by various modules, shows that the most credible reference material is a variety bred in 2023, whose parental combination is "father A × mother B", which highly matches the parental information of the current seeds. At the same time, the temperature adaptation range of its cultivation process is consistent with the field performance of the current seeds, thus providing a basis for breeding decisions.

[0102] The implementation of this system not only enables full-cycle traceability of maize breeding materials, but also ensures data reliability through blockchain technology. The collaborative work of each module automates and refines the traceability process, avoiding errors caused by manual comparison. It is suitable for material management and traceability scenarios in large-scale maize breeding bases.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A blockchain-based electronic tagging method for tracing maize breeding materials, characterized in that, include: Acquire basic attribute information and full-cycle breeding process data of maize breeding materials, extract feature identifiers from the basic attribute information and generate electronic tag codes; perform standardization processing on the full-cycle breeding process data to obtain stage feature datasets; The system retrieves historically stored reference breeding material data from the blockchain node database. This data includes reference basic attributes, reference stage feature sets, and corresponding electronic tag storage records. The frequency of occurrence of the feature identifiers in the reference basic attributes is statistically analyzed, and the frequency percentage of the feature identifiers is calculated based on this frequency. The reciprocal of the frequency percentage is used as the association weight value of the feature identifiers, and an identifier matching value is generated for each reference breeding material based on this association weight value. The correlation degree between the reference stage feature set and the stage feature set of each reference breeding material is calculated. Finally, the traceability credibility is calculated based on the correlation degree and identifier matching value of each reference breeding material. The electronic tag records of the most reliable reference breeding materials will be used as the traceability reference for current maize breeding materials.

2. The blockchain-based electronic tag traceability method for maize breeding materials as described in claim 1, characterized in that, Based on the aforementioned association weight values, identifier matching values ​​for each reference breeding material are generated, including: The sum of the association weight values ​​of the elements in the reference basic attributes of the reference breeding material that are the same as the feature identifier of the current maize breeding material is calculated as the identifier matching value of the reference breeding material.

3. The blockchain-based electronic tag traceability method for maize breeding materials as described in claim 1, characterized in that, Calculate the correlation between the reference stage feature set and the stage feature dataset for each reference breeding material, including: Calculate the sub-association degree between the feature subsets of each reference stage of the reference breeding material and the feature data subsets of each stage of the current maize breeding material to obtain the sub-association degree set of the reference breeding material.

4. The blockchain-based electronic tag traceability method for maize breeding materials as described in claim 3, characterized in that, Calculating the correlation between the reference stage feature set and the stage feature dataset for each reference breeding material further includes: A first threshold interval is determined based on the maximum value of the sub-association degree in the sub-association degree set of different reference breeding materials; the proportion of the number of sub-association degrees in the sub-association degree set of the reference breeding material located in the first threshold interval is calculated to obtain the association ratio value of the reference breeding material; the average value of the sub-association degrees in the sub-association degree set of the reference breeding material located in the first threshold interval is calculated; the association degree of the reference breeding material is calculated based on the association ratio value and the average value.

5. The blockchain-based electronic tag traceability method for maize breeding materials as described in claim 1, characterized in that, The final traceability reliability is calculated based on the correlation and identifier matching values ​​of each reference breeding material, including: The product of the relevance and the identifier matching value is used as the final traceability credibility.

6. A blockchain-based electronic tag traceability system for maize breeding materials, based on the blockchain-based electronic tag traceability method for maize breeding materials as described in any one of claims 1-5, characterized in that, include: The information acquisition module is used to acquire basic attribute information and full-cycle breeding process data of maize breeding materials, extract feature identifiers of the basic attribute information and generate electronic tag codes; The data processing module is used to standardize the data of the entire cultivation process to obtain a stage feature dataset; The evidence retrieval module is used to retrieve historically stored reference breeding material data from the blockchain node database. The reference breeding material data includes reference basic attributes, reference stage feature sets, and corresponding electronic tag evidence records. The frequency statistics module is used to count the frequency of occurrence of the feature identifier in the reference basic attributes, and calculate the frequency proportion of the feature identifier based on the frequency of occurrence; the weight generation module is used to take the reciprocal of the frequency proportion as the association weight value of the feature identifier, and generate the identifier matching value of each reference breeding material based on the association weight value. The correlation calculation module is used to calculate the correlation degree between the reference stage feature set of each reference breeding material and the stage feature dataset; The credibility calculation module is used to calculate the final traceability credibility based on the correlation and identifier matching value of each reference breeding material. The traceability reference module is used to use the electronic tag records of the most reliable reference breeding materials as the traceability reference for current maize breeding materials.

7. The blockchain-based electronic tag traceability system for maize breeding materials as described in claim 6, characterized in that, The weight generation module includes: The identifier matching value calculation subunit is used to calculate the sum of the association weight values ​​of the elements in the reference basic attributes of the reference breeding material that are the same as the feature identifiers of the current maize breeding material, and use them as the identifier matching value of the reference breeding material.

8. The blockchain-based electronic tag traceability system for maize breeding materials as described in claim 6, characterized in that, The associated calculation module includes: The sub-association degree calculation unit is used to calculate the sub-association degree between the feature subsets of each reference stage of the reference breeding material and the feature data subsets of each stage of the current maize breeding material, so as to obtain the sub-association degree set of the reference breeding material.

9. The blockchain-based electronic tag traceability system for maize breeding materials as described in claim 6, characterized in that, The associated calculation module also includes: The threshold interval determination unit is used to determine a first threshold interval based on the maximum value of the sub-associations in the sub-associations set of different reference breeding materials; the proportion calculation unit is used to calculate the proportion of the number of sub-associations in the sub-associations set of the reference breeding material that are located in the first threshold interval to the total number of sub-associations in the sub-associations set, thereby obtaining the association proportion value of the reference breeding material; the average value calculation unit is used to calculate the average value of the sub-associations in the sub-associations set of the reference breeding material that are located in the first threshold interval; and the association degree calculation unit is used to calculate the association degree of the reference breeding material based on the association proportion value and the average value.

10. The blockchain-based electronic tag traceability system for maize breeding materials as described in claim 6, characterized in that, The credibility calculation module includes: The credibility calculation subunit is used to take the product of the correlation degree and the identifier matching value as the final source tracing credibility.

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