Corn breeding material electronic tag traceability method and system based on blockchain

Abstract

The application relates to application of a blockchain in the field of agricultural breeding technology, and discloses a corn breeding material electronic tag traceability method and system based on a blockchain, which comprises the following steps: obtaining basic attributes and whole-cycle cultivation data of corn breeding materials, and generating an electronic tag code; standardizing the cultivation data to obtain stage characteristic data sets; calling reference breeding material data from a blockchain node, counting characteristic identification frequencies, and calculating correlation weight values; generating identification matching values of the reference materials based on the weight values; calculating stage characteristic correlation degrees of the reference materials and the current materials; combining the correlation degrees and the identification matching values to calculate final traceability reliabilities, and selecting reference materials with the highest reliabilities as traceability bases. The system comprises information acquisition, data processing, record storage and calling and other modules. The application realizes non-falsifiable record storage of breeding data through the blockchain technology, improves traceability accuracy by combining dynamic weight and correlation degree analysis, and provides an efficient and reliable solution for breeding material management.

Description

TECHNICAL FIELD

[0001] The present application relates to the application of blockchain in the field of agricultural breeding technology, in particular to a corn breeding material electronic tag traceability method and system based on blockchain. BACKGROUND

[0002] In the process of modern agricultural development, corn, as an important food, feed and industrial raw material crop in the world, its breeding work is of great importance to guarantee food security and improve agricultural production efficiency. The accurate traceability of corn breeding materials can provide solid data support for variety selection, quality control and intellectual property protection, and thus has become a key research direction in the field of breeding.

[0003] The traditional traceability method of corn breeding materials mainly relies on paper records and manual management. In actual operation, paper records are easily affected by environmental factors such as humidity and insect damage, resulting in blurred or even lost data, which seriously affects the integrity and accuracy of the data. At the same time, there are problems such as arbitrary operation and non-standard recording in the process of manual management, making it difficult to effectively guarantee the authenticity of the data. In addition, the storage of paper records requires a large amount of space, and as the amount of breeding material data continues to increase, the management difficulty and cost also increase exponentially.

[0004] With the development of information technology, some breeding units have begun to use electronic data management systems for corn breeding material traceability. However, most of these systems are based on centralized databases, which have the potential risks of data tampering and poor security. Once the database is attacked maliciously or the management personnel make mistakes, the authenticity and reliability of the data will be seriously threatened. Moreover, the centralized system has great limitations in data sharing, and the data of different breeding units and research institutions cannot be efficiently interconnected, forming a "data island" phenomenon, which hinders the integration and collaborative innovation of breeding resources.

[0005] Blockchain technology, with its characteristics of decentralization, tamper resistance and traceability, brings a new solution to corn breeding material traceability. However, the existing corn breeding material traceability technology based on blockchain still has deficiencies in data processing and traceability accuracy. For example, when analyzing breeding material data, there is a lack of scientific and reasonable methods to effectively filter and weight the data, resulting in low credibility of the traceability results. When calculating the correlation between different breeding materials, the method is relatively single and cannot accurately reflect the actual relationship between breeding materials, making it difficult to meet the requirements of traceability accuracy in the increasingly complex corn breeding work. Therefore, there is an urgent need for a more efficient, accurate and secure and reliable corn breeding material electronic tag traceability method and system based on blockchain to solve the many problems existing in the prior art and promote the high-quality development of the corn breeding industry. SUMMARY

[0006] The present application aims to provide a corn breeding material electronic tag traceability method and system based on a blockchain to solve the problems raised in the background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions: a corn breeding material electronic tag traceability method and system based on a blockchain, the method comprising:

[0008] Obtaining the basic attribute information and the whole-cycle cultivation process data of the corn breeding material, extracting the characteristic identifier of the basic attribute information and generating an electronic tag code; standardizing the whole-cycle cultivation process data to obtain a stage feature data set; calling the historical reference breeding material data stored in the blockchain node database, the reference breeding material data including reference basic attributes, reference stage feature sets, and corresponding electronic tag storage records; counting the occurrence frequency of the characteristic identifier in the reference basic attributes, calculating the frequency proportion of the characteristic identifier based on the occurrence frequency; taking the reciprocal of the frequency proportion as the correlation weight value of the characteristic identifier, generating an identifier matching value of each reference breeding material based on the correlation weight value; calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set; calculating the final traceability credibility according to the correlation degree and the identifier matching value of each reference breeding material; taking the electronic tag storage record of the reference breeding material with the highest credibility as the traceability reference basis of the current corn breeding material.

[0009] Preferably, generating the identifier matching value of each reference breeding material based on the correlation weight value comprises:

[0010] Calculating the sum of the correlation weight values of the same elements in the reference basic attributes of the reference breeding material and the characteristic identifier of the current corn breeding material as the identifier matching value of the reference breeding material.

[0011] Preferably, calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set comprises:

[0012] Calculating the sub-correlation degrees of each reference stage feature subset of the reference breeding material and each stage feature data subset of the current corn breeding material to obtain a sub-correlation degree set of the reference breeding material.

[0013] Preferably, calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set further comprises:

[0014] determine a first threshold interval based on the maximum value of the sub-correlation degrees in the sub-correlation degree set of different reference breeding materials; calculate the proportion of the number of sub-correlation degrees in the first threshold interval in the total number of sub-correlation degrees in the sub-correlation degree set of the reference breeding material to obtain the correlation proportion value of the reference breeding material; calculate the average value of the sub-correlation degrees in the first threshold interval in the sub-correlation degree set of the reference breeding material; and calculate the correlation degree of the reference breeding material based on the correlation proportion value and the average value.

[0015] Preferably, the final traceability confidence is calculated according to the correlation degree and the identification matching value of each reference breeding material, including:

[0016] The product of the correlation degree and the identification matching value is taken as the final traceability confidence.

[0017] Preferably, the application also includes a corn breeding material electronic tag traceability system based on a blockchain, which comprises:

[0018] An information collection module is configured to obtain the basic attribute information and the whole-cycle cultivation process data of the corn breeding material, extract the characteristic identification of the basic attribute information, and generate an electronic tag code; a data processing module is configured to standardize the whole-cycle cultivation process data to obtain a stage characteristic data set; a storage retrieval module is configured to retrieve historical storage reference breeding material data in a blockchain node database, wherein the reference breeding material data includes reference basic attributes, reference stage characteristic sets, and corresponding electronic tag storage records; a frequency statistical module is configured to count the occurrence frequency of the characteristic identification in the reference basic attributes, and calculate the frequency proportion of the characteristic identification based on the occurrence frequency; a weight generation module is configured to take the reciprocal of the frequency proportion as the correlation weight value of the characteristic identification, and generate the identification matching value of each reference breeding material based on the correlation weight value; an association calculation module is configured to calculate the correlation degree of the reference stage characteristic set of each reference breeding material and the stage characteristic data set; a confidence calculation module is configured to calculate the final traceability confidence according to the correlation degree and the identification matching value of each reference breeding material; and a traceability reference module is configured to take the electronic tag storage record of the reference breeding material with the highest confidence as the traceability reference basis of the current corn breeding material.

[0019] Preferably, the weight generation module comprises:

[0020] An identification matching value calculation subunit is configured to calculate the sum of the correlation weight values of the same elements in the reference basic attributes of the reference breeding material and the characteristic identification of the current corn breeding material as the identification matching value of the reference breeding material.

[0021] Preferably, the association calculation module comprises:

[0022] The sub-correlation degree calculation unit is configured to calculate sub-correlation degrees between each reference stage characteristic subset of a reference breeding material and each stage characteristic data subset of the current corn breeding material, and obtain a sub-correlation degree set of the reference breeding material.

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

[0024] The threshold interval determination unit is configured to determine a first threshold interval based on the maximum value of the sub-correlation degrees in the sub-correlation degree set of the different reference breeding materials; the proportion value calculation unit is configured to calculate a proportion of the number of the sub-correlation degrees in the first threshold interval in the total number of the sub-correlation degrees in the sub-correlation degree set of the reference breeding material, and obtain a correlation proportion value of the reference breeding material; the average value calculation unit is configured to calculate an average value of the sub-correlation degrees in the first threshold interval in the sub-correlation degree set of the reference breeding material; and the correlation degree calculation unit is configured to calculate the correlation degree of the reference breeding material based on the correlation proportion value and the average value.

[0025] Preferably, the credibility calculation module comprises:

[0026] The credibility calculation sub-unit is configured to take the product of the correlation degree and the identification matching value as the final traceability credibility.

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] The corn breeding material electronic tag traceability method and system based on the blockchain provided by the present application significantly improve the accuracy, reliability and security of corn breeding material traceability through multi-dimensional data processing and scientific calculation models, and bring many positive influences to the corn breeding industry.

[0029] In terms of data collection and processing, the system comprehensively acquires the basic attribute information and the whole-cycle cultivation process data of the corn breeding material, and standardizes the data, thereby ensuring the standardization and consistency of the data. Meanwhile, the characteristic identification of the basic attribute information is extracted and an electronic tag code is generated, so that each piece of breeding material is given a unique "digital identity card", and the precise identification and management of the breeding material are realized. This comprehensive and standardized data collection and processing mode provides a solid data foundation for subsequent traceability analysis.

[0030] In terms of traceability accuracy, the application innovatively introduces a calculation method for the correlation weight value of feature identification and the correlation degree of stage feature data set. By counting the frequency of feature identification in the reference basic attribute, the frequency ratio is calculated and the reciprocal is taken as the correlation weight value, which can accurately reflect the uniqueness and importance of feature identification in different breeding materials. On this basis, the identification matching value of each reference breeding material is calculated, which can effectively measure the similarity of the current corn breeding material and the reference breeding material in the basic attribute. At the same time, by calculating the correlation degree of the reference stage feature set and the stage feature data set, the relationship between the breeding materials is further analyzed from the perspective of cultivation process data. Compared with the traditional method, this comprehensive analysis from the two dimensions of basic attribute and cultivation process can more comprehensively and accurately judge the similarity of the current corn breeding material and the reference breeding material, thereby greatly improving the accuracy of the traceability result.

[0031] In the application layer of blockchain technology, the characteristics of decentralization and tamper resistance of blockchain are fully utilized, and the reference breeding material data is stored in the blockchain node database. This not only guarantees the safety and credibility of the data, but also enables efficient sharing and collaboration of data among different participants. Each breeding unit and research institution can conduct traceability analysis based on the data stored on the blockchain, breaking the "data island", promoting the integration and exchange of breeding resources, and promoting the collaborative innovation and development of the corn breeding industry.

[0032] In terms of system function implementation, the traceability system based on this method realizes the full-process automatic management from data collection, processing to traceability analysis through the setting of information collection module, data processing module, storage and retrieval module and other functional modules. The modules have clear division of labor and work together, effectively improving the work efficiency of breeding material traceability and reducing the cost of manual management. At the same time, the modular design of the system has good scalability and compatibility, and can be upgraded and optimized according to actual needs to meet the needs of the development of corn breeding work.

[0033] The corn breeding material electronic tag traceability method and system based on blockchain has significant advantages in data processing, traceability accuracy, technology application and system function, and can provide more efficient, accurate and secure traceability solutions for the corn breeding industry, which has important practical significance for promoting the modernization of the corn breeding industry. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The working principle diagram of the corn breeding material electronic tag traceability method based on blockchain described in the application;

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

[0036] Figure 3 A working principle diagram of the electronic tag traceability system for corn breeding materials is shown in FIG. 1.

[0037] Figure 4 A detailed design diagram of the association calculation module is shown in FIG. 2. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0039] Please refer to Figures 1-4 The present application relates to a corn breeding material electronic tag traceability method and system based on a blockchain, and the specific implementation steps are as follows:

[0040] The basic attribute information and the whole-cycle cultivation process data of the corn breeding material are obtained, the characteristic identifier of the basic attribute information is extracted, and an electronic tag code is generated. The basic attribute information includes, but is not limited to, the variety name, parent information, seed source, and morphological characteristics of the corn breeding material. The whole-cycle cultivation process data covers environmental data (such as temperature, humidity, light, and soil nutrients) of each stage from sowing, seedling, growth, flowering, and fruiting to harvesting, farming operation data (such as fertilization, irrigation, and pest control), and growth and development data (such as plant height, stem thickness, leaf area, and ear traits). By analyzing the basic attribute information, the characteristic identifier that can uniquely identify the corn breeding material is extracted, such as a specific gene sequence fragment or a combination of morphological characteristic parameters, and an electronic tag code is generated according to certain coding rules.

[0041] The whole-cycle cultivation process data is standardized to obtain a stage feature data set. Since the whole-cycle cultivation process data comes from different collection devices and time periods, there may be problems such as non-uniform data format and inconsistent units, so the data needs to be standardized. Standardization includes data cleaning (removing noise data and abnormal data), data conversion (converting different formats of data to a unified format, and converting data in different units to a standard unit), data normalization (mapping data to a certain range), and other operations. Then, according to the growth cycle of the corn breeding material, the standardized data is divided into different stages, such as the seedling stage, the ear stage, and the flowering and grain stage, and each stage forms a stage feature data set.

[0042] The reference breeding material data of the historical storage evidence is called in the blockchain node database, and the reference breeding material data includes reference basic attributes, reference stage characteristic sets and corresponding electronic tag storage evidence records. The blockchain node database stores a large amount of historical storage evidence of corn breeding material data, and these data have the characteristics of non-tamperability and traceability. Through the consensus mechanism and smart contract of the blockchain, the reference breeding material data related to the current corn breeding material is searched in the node database.

[0043] The frequency of the characteristic identifier in the reference basic attribute is counted, and the frequency proportion of the characteristic identifier is calculated based on the frequency. For the reference basic attribute of each reference breeding material, the number of times of the characteristic identifier of the current corn breeding material appearing in it is counted, and then the total frequency of the characteristic identifier appearing in all reference basic attributes is calculated, and then divided by the total number of reference basic attributes to obtain the frequency proportion of the characteristic identifier.

[0044] The reciprocal of the frequency proportion is taken as the correlation weight value of the characteristic identifier, and the identification matching value of each reference breeding material is generated based on the correlation weight value. The lower the frequency proportion of the characteristic identifier, the more unique the characteristic identifier is in the reference breeding material, and the greater the correlation weight value. By adding the correlation weight values of the same elements in each reference breeding material and the characteristic identifier of the current corn breeding material, the identification matching value of the reference breeding material is obtained.

[0045] The correlation degree of each reference breeding material and the stage characteristic data set is calculated. Through certain algorithms, such as cosine similarity algorithm, Euclidean distance algorithm, etc., the sub-correlation degrees of each reference stage characteristic subset of the reference breeding material and each stage characteristic data subset of the current corn breeding material are calculated to obtain the sub-correlation degree set of the reference breeding material.

[0046] The final traceability confidence is calculated according to the correlation degree and the identification matching value of each reference breeding material. The correlation degree and the identification matching value are multiplied to obtain the final traceability confidence.

[0047] The electronic tag storage evidence record of the reference breeding material with the highest confidence is taken as the traceability reference basis of the current corn breeding material. By comparing the final traceability confidence of each reference breeding material, the reference breeding material with the highest confidence is selected, and its electronic tag storage evidence record is taken as the traceability reference basis of the current corn breeding material.

[0048] Embodiment 1:

[0049] This embodiment mainly illustrates the generation of the identification matching value of each reference breeding material based on the correlation weight value. The specific implementation is as follows:

[0050] The reference base attribute acquisition method of the breeding material needs to be clearly referenced. When the historical reference breeding material data stored in the blockchain node database is called, the reference base attribute contains data of the same category as the current corn breeding material base attribute information, such as variety name, parent combination, seed generation number, morphological characteristic parameter, physiological index, etc. These data are stored in a structured form in the blockchain node, and after being called through the smart contract interface, a reference data set for comparison is formed. For each reference breeding material to be processed, the system will automatically extract its corresponding reference base attribute as the basic data for comparison with the current corn breeding material characteristic identifier.

[0051] The elements of the characteristic identifier and the reference base attribute are compared. The characteristic identifier of the current corn breeding material is a key data element with uniqueness or high discrimination extracted from its base attribute information. For example, if the base attribute information contains a specific genetic marker sequence, a unique combination of plant height and leaf shape parameters, or a specific parent hybridization combination information, etc., the system will select the characteristic identifier that can represent the identity of the breeding material from these information through a pre-set feature extraction algorithm. In the comparison process, the system will match each element in the reference base attribute with the characteristic identifier of the current corn breeding material one by one, and identify the same elements.

[0052] After identifying the same elements, the associated weight value of the element needs to be obtained. The determination of the associated weight value is based on the frequency of the characteristic identifier in the reference base attribute. Specifically, the system will first count the total number of times the characteristic identifier appears in the reference base attribute of all reference breeding materials, and then calculate the proportion of the number in the total number of all reference base attribute elements, i.e. the frequency proportion. And the associated weight value is the inverse of the frequency proportion. For example, assuming that the frequency proportion of a certain characteristic identifier in all reference base attributes is 0.1, then its associated weight value is 1 ÷ 0.1 = 10. The logic of this calculation method is that the lower the frequency of the characteristic identifier in the reference base attribute, the higher the uniqueness, and the greater the reference value in the traceability process, so it is given a higher associated weight value.

[0053] After obtaining the associated weight value of each same element, the system will sum the weight values to generate the identification matching value of the reference breeding material. The specific operation is that for each reference breeding material, the associated weight values of all elements in its reference base attribute that are the same as the characteristic identifier of the current corn breeding material are added, and the sum is the identification matching value of the reference breeding material. For example, if a reference breeding material has 3 elements in its reference base attribute that are the same as the characteristic identifier of the current corn breeding material, and their corresponding associated weight values are 8, 10 and 6 respectively, then the identification matching value of the reference breeding material is 8 + 10 + 6 = 24.

[0054] This way of generating the identification matching value has clear logical basis and practical significance. From a logical level, determining the correlation weight value by the reciprocal of the frequency proportion can fully reflect the influence of the uniqueness of the feature identification on the matching result. The feature identification with high uniqueness has a low probability of appearing in the reference basic attribute, and its correlation weight value is high, so it occupies a larger proportion in the identification matching value, making the final identification matching value more accurately reflect the matching degree of the reference breeding material and the current corn breeding material in the basic attribute.

[0055] From the practical application level, this method can effectively improve the accuracy and reliability of the traceability. In the traceability process of corn breeding materials, different feature identifications have different importance for material identity recognition. For example, some genetic markers may be unique to a particular variety, with very low frequency of occurrence, so they have very high reference value in traceability; while some common morphological characteristic parameters, such as ordinary plant height range, may appear in multiple varieties, with a higher frequency of occurrence and a relatively lower correlation weight value. Through this weighted summation method, the system can automatically distinguish feature identifications of different importance, avoiding traceability errors caused by treating all feature identifications equally.

[0056] This process is operable and efficient in system implementation. In terms of data processing, the data in the block chain node database has the characteristics of non-tamperability, ensuring the authenticity and reliability of the reference basic attribute data. The system can automatically complete the whole process from data retrieval, feature comparison, weight calculation to summation of identification matching value through the preset algorithm and process, without human intervention, improving the processing efficiency and accuracy.

[0057] In specific technical implementation, the following aspects need attention: first, the feature identification extraction algorithm needs to be optimized according to the characteristics of corn breeding materials to ensure that the extracted feature identifications have sufficient distinguishability and representativeness; second, when counting the frequency of feature identification, the integrity and accuracy of the data need to be considered to avoid calculation deviation of the frequency proportion due to data loss or error; third, when comparing elements, the consistency of data format needs to be handled, such as standardizing the conversion of the same feature with different units or different expressions before comparison.

[0058] Embodiment 2:

[0059] This embodiment describes in detail the steps of calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set to realize precise traceability. This step provides key basis for traceability reliability calculation through multi-stage quantitative analysis of the cultivation process data, and the specific implementation is as follows:

[0060] The reference stage characteristic set of the reference breeding material and the stage characteristic data set of the current corn breeding material need to be structured and divided. The whole cycle breeding process of corn breeding usually covers multiple growth stages, such as seedling stage (from sowing to jointing), earing stage (from jointing to tasseling), grain filling stage (from tasseling to maturity), etc. According to the biological characteristics of corn growth and the stage division standards in agricultural production practice, the system divides the reference stage characteristic set and the stage characteristic data set into corresponding stage characteristic subsets. For example, the seedling stage characteristic subset in the reference stage characteristic set includes environmental data (daily average temperature, soil moisture, light duration, etc.), farming operation data (sowing depth, fertilizer type and amount, irrigation frequency, etc.), and growth and development data (plant height growth rate, leaf number, stem diameter, etc.) of the reference breeding material at the seedling stage. Similarly, the seedling stage characteristic data subset in the stage characteristic data set includes the same type of data of the current corn breeding material at the seedling stage. This stage-based division method can make the subsequent correlation degree calculation more consistent with the actual rules of corn growth, avoiding analysis bias caused by mixing data of different growth stages.

[0061] For each corresponding stage characteristic subset and stage characteristic data subset, sub-correlation degree calculation is implemented. The calculation of sub-correlation degree needs to use an algorithm that can quantify the similarity of data. Common algorithms include cosine similarity algorithm and Euclidean distance algorithm, etc. Taking the cosine similarity algorithm as an example, this algorithm regards each stage characteristic subset or data subset as a vector in high-dimensional space, and measures their similarity by calculating the cosine value of the angle between two vectors. Assuming that the seedling stage characteristic subset of the reference breeding material is represented as vector A (a1, a2, a3,..., an), and the seedling stage characteristic data subset of the current corn breeding material is represented as vector B (b1, b2, b3,..., bn), where each dimension corresponds to a specific data index (such as temperature, humidity, etc.), the calculation logic of cosine similarity is to measure the closeness of the two vectors in direction, and the value closer to 1 indicates higher similarity. While the Euclidean distance algorithm calculates the absolute distance of two vectors in high-dimensional space, and the smaller the distance, the smaller the data difference, and the higher the similarity.

[0062] In practical applications, the system will select appropriate algorithms according to data characteristics. For continuous environmental data (such as temperature, humidity) and growth and development data (such as plant height, leaf area), the Euclidean distance algorithm can intuitively reflect the absolute difference of data; for multi-dimensional farming operation data (such as fertilizer combination, pest control measures), the cosine similarity algorithm is more suitable for measuring the similarity of data patterns. The system will automatically match the corresponding algorithm for different types of data indicators to ensure the accuracy of sub-correlation degree calculation. For example, when processing temperature data, the Euclidean distance is used to calculate the difference of daily average temperature; when processing fertilizer type data, different fertilizer combinations are converted into vector form, and then the cosine similarity is used to calculate the pattern similarity.

[0063] For each reference breeding material, the system will calculate the sub-correlation degree between each stage feature subset and the stage feature data subset in turn. For example, the reference stage feature set of reference breeding material A is divided into three subsets of seedling stage, ear stage, and flowering and grain filling stage, and the stage feature data set of the current corn breeding material is also divided into three subsets. The system will calculate the sub-correlation degree between the seedling stage subset and the seedling stage data subset, the sub-correlation degree between the ear stage subset and the ear stage data subset, and the sub-correlation degree between the flowering and grain filling stage subset and the flowering and grain filling stage data subset, respectively, thereby obtaining a set containing three sub-correlation degrees. This sub-correlation degree set comprehensively reflects the similarity between the reference breeding material and the cultivation process data of the current corn breeding material at each growth stage.

[0064] The correlation degree calculation process has clear agricultural application logic. The characteristics of corn breeding materials are not only reflected in the basic attributes, but also in the cultivation process data throughout the whole cycle, which is the key to reflect the variety characteristics, growth rules and environmental adaptability. Different varieties or the same variety under different cultivation conditions may have different performances at different growth stages. By calculating the sub-correlation degree in stages, the specific performance of these differences at different stages can be captured, for example, some varieties may be more sensitive to temperature at the seedling stage, while other varieties may have more special water requirements at the flowering and grain filling stage. This stage-by-stage analysis method enables the tracing process to compare the details of the cultivation process in more detail and improves the reliability of the tracing results.

[0065] In terms of technical implementation, attention should be paid to the standardization of data. Since different stages and different types of data may have different dimensions and value ranges (such as temperature unit in ℃, light duration unit in hours, and plant height unit in centimeters), the system will first perform standardization processing on the data before calculating the sub-correlation degree. Standardization processing includes data cleaning (removal of outliers and interpolation of missing values), unit unification, normalization (mapping data to the [0, 1] interval or a specific range), and other operations to ensure the comparability of data in different dimensions. For example, temperature data is converted from ℃ to standardized numerical values, and light duration is converted from hours to normalized proportion values, avoiding the interference of dimensional differences on the calculation results of the correlation degree.

[0066] The division of stage feature subsets needs to be optimized in combination with the knowledge in the agricultural field. The system can cooperate with agricultural experts to establish more detailed stage division standards, such as further dividing the seedling stage into germination stage, three-leaf stage, and pre-jointing stage, so that the stage feature subsets are more suitable for the physiological stages of corn growth. At the same time, for different types of corn varieties (such as early-maturing varieties and late-maturing varieties), the time nodes and feature indicators of stage division can also be dynamically adjusted to improve the adaptability of stage division.

[0067] In terms of data storage and calling, the reference stage characteristic set is taken as part of the blockchain storage data, and its integrity and tamper resistance are guaranteed. When the system calls the reference stage characteristic set through the blockchain smart contract interface, it will synchronously verify the hash value of the data to ensure that the data has not been tampered with. The current stage characteristic data set of the corn breeding material is generated by real-time data collection and standardized processing, stored in the local database of the system, and associated with the electronic tag code stored in the blockchain, ensuring the traceability of the data.

[0068] Embodiment 3:

[0069] When calculating the correlation degree of the reference stage characteristic set of each reference breeding material and the stage characteristic data set, in addition to calculating the sub-correlation degree set, a threshold interval needs to be screened and statistically analyzed to further determine the correlation degree, and the specific implementation is as follows:

[0070] The maximum value of the sub-correlation degree set of different reference breeding materials is used to determine the first threshold interval. After calculating the sub-correlation degree of each stage of all reference breeding materials, the system will collect the maximum value of the sub-correlation degree set of all reference breeding materials. For example, assuming that there are 100 reference breeding materials, each reference breeding material may have 3 stages of sub-correlation degrees (such as seedling stage, ear stage, and flowering and grain filling stage), then the system will extract the maximum value of each reference breeding material from the 300 sub-correlation degrees to form a set containing 100 maximum values. Next, the system needs to determine a reasonable interval range as the first threshold interval, which is usually based on the statistical characteristics of these maximum values. For example, calculate the average value and standard deviation of these maximum values, take the average value as the center, and extend a certain multiple of the standard deviation upwards and downwards to form an interval; or take the minimum and maximum values of a certain proportion (such as the top 20%) of these maximum values as the upper and lower limits of the interval. The first threshold interval thus determined can cover the relatively high sub-correlation degree level exhibited by most reference breeding materials, thereby screening the relatively high sub-correlation degree part of each reference breeding material.

[0071] After determining the first threshold interval, for each reference breeding material, calculate the proportion of the number of sub-correlation degrees in the first threshold interval in the total number of sub-correlation degrees in the sub-correlation degree set to obtain the correlation proportion value. For example, the sub-correlation degree set of a certain reference breeding material contains 3 sub-correlation degrees, which are 0.7, 0.8, and 0.5, and the first threshold interval is assumed to be [0.6, 0.9], then 0.7 and 0.8 are located in the interval, the number is 2, the total number is 3, and the correlation proportion value is 2 ÷ 3 ≈ 0.67. This proportion value reflects the proportion of the relatively high level sub-correlation degree in the sub-correlation degree of the reference breeding material at each stage, and the higher the proportion, the more stages the reference breeding material has a high similarity with the current corn breeding material.

[0072] The average of the sub-correlations of the reference breeding material that are within the first threshold interval is calculated. Continuing with the above example, the sub-correlations within the interval are 0.7 and 0.8, and their average is (0.7 + 0.8) ÷ 2 = 0.75. This average reflects the average similarity of the reference breeding material at the higher similarity stages, and can further quantify the matching level of the reference breeding material with the current corn breeding material in the breeding process data.

[0073] The correlation degree of the reference breeding material is calculated based on the correlation proportion value and the average. The system needs to comprehensively consider these two indicators to form a correlation degree value that can fully reflect the similarity of the reference breeding material with the current corn breeding material in the breeding process data. The specific comprehensive method can use weighted summation, in which the correlation proportion value and the average are respectively given certain weights. For example, assuming that the weight of the correlation proportion value is 0.4 and the weight of the average is 0.6, then the correlation degree of the reference breeding material is 0.67 x 0.4 + 0.75 x 0.6 = 0.268 + 0.45 = 0.718. The setting of the weights needs to be combined with the actual application scenario and the characteristics of the data, and usually can be determined through the analysis of a large amount of historical data in the early stage to determine the weight allocation scheme that can make the correlation degree calculation result more consistent with the similarity of the actual breeding material.

[0074] This way of calculating the correlation degree through threshold interval screening and statistical analysis has clear logic and practical significance. From the logical point of view, the maximum value of the sub-correlation reflects the highest similarity level of the reference breeding material with the current corn breeding material at a certain stage. The threshold interval determined based on these maximum values can effectively screen out relatively high sub-correlations in each reference breeding material, avoiding interference from lower sub-correlations, and thus focusing more on higher similarity stages. The correlation proportion value reflects the proportion of higher similarity stages in quantity, and the average reflects the similarity level of these stages in degree. The combination of the two can comprehensively evaluate the overall correlation degree of the reference breeding material with the current corn breeding material in the breeding process data.

[0075] At the practical application level, this method can improve the accuracy and reliability of the correlation calculation. In the process of corn breeding, different reference breeding materials and current corn breeding materials may have high similarity at some stages, but low similarity at other stages. By screening the stages with high similarity through the threshold interval, the overall correlation can be avoided to be underestimated due to low similarity at individual stages, or overestimated due to high similarity at individual stages. For example, a certain reference breeding material may have high similarity with the current corn breeding material at the seedling stage and the ear stage, but low similarity at the flowering and grain stage. If the average value of all sub-correlations is directly taken, the overall correlation may be reduced, but by screening the higher sub-correlations at the seedling and ear stages for calculation, the main similarity stage can be more accurately reflected.

[0076] During the technical implementation, the following aspects need to be paid attention to: first, the determination of the first threshold interval needs to be scientific and reasonable, and cannot be too wide or too narrow. A too wide interval will result in an unobvious screening effect and include too many sub-correlations with low similarity; a too narrow interval may miss some sub-correlations with actual reference value. Therefore, according to the distribution characteristics of historical data, a suitable interval range needs to be determined through statistical analysis. Second, when calculating the correlation proportion value and the average value, the accuracy of the data needs to be ensured, especially whether each sub-correlation in the sub-correlation set is correctly calculated and whether it is correctly judged to be within the first threshold interval. Third, the setting of the weight needs to be fully tested and verified. Different weight allocation will affect the final correlation calculation result, and the optimal weight combination needs to be determined by combining the actual breeding scene, communicating with agricultural experts or analyzing historical traceability cases.

[0077] This process needs to have good operability and scalability in system implementation. The system needs to be able to automatically collect the maximum value of the sub-correlation of all reference breeding materials, perform statistical analysis and determine the first threshold interval; be able to automatically screen, count and average the sub-correlation set of each reference breeding material; be able to automatically calculate the correlation degree according to the preset weight. At the same time, 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 the changes in data distribution, and ensure the accuracy and timeliness of the correlation calculation.

[0078] Embodiment 4:

[0079] According to the correlation degree and the identification matching value of each reference breeding material, the final traceability credibility is calculated, which is a key step of integrating multi-dimensional information to determine the optimal traceability reference. This step quantitatively combines the breeding process similarity and the basic attribute matching degree to provide a comprehensive evaluation for the traceability result, and the specific implementation is as follows:

[0080] Taking a certain corn breeding material A as an example, it is assumed that the system retrieves 3 reference breeding materials B, C and D in the blockchain node database. First, the acquisition process of the correlation degree and the identification matching value of each reference breeding material needs to be determined. The correlation degree of reference breeding material B is obtained by calculating the correlation degree of its reference stage characteristic set and the stage characteristic data set of the current breeding material A. For example, its stage sub-correlation degree set is [0.8, 0.7, 0.6], after threshold interval screening, the correlation proportion value is determined as 0.67, the average value is 0.75, and the final correlation degree is 0.718 (assuming the weight distribution is 0.4 for the correlation proportion value and 0.6 for the average value). At the same time, the identification matching value of reference breeding material B is obtained by summing the associated weight values of the same elements in its reference basic attributes and the characteristic identification of breeding material A. Assuming that the associated weight values of the same elements are 5, 3 and 4, the total is 12, and the identification matching value is 12.

[0081] The correlation degree calculation process of reference breeding material C is similar. Assuming that its sub-correlation degree set is [0.7, 0.9, 0.5], the correlation proportion value is 0.67 (2 values are located in the interval) after threshold interval screening, the average value is 0.8, and the correlation degree is 0.67x0.4+0.8x0.6=0.308+0.48=0.788. In terms of identification matching value, assuming that there are two same characteristic identifications in the reference basic attributes, the associated weight values are 6 and 7, the total is 13, and the identification matching value is 13.

[0082] The sub-correlation degree set of reference breeding material D is [0.5, 0.6, 0.8], the correlation proportion value is 0.67 (2 values are located in the interval) after threshold interval screening, the average value is 0.7, and the correlation degree is 0.67x0.4+0.7x0.6=0.268+0.42=0.688. In terms of identification matching value, assuming that the total associated weight value of the same characteristic identification is 10, the identification matching value is 10.

[0083] After obtaining the correlation degree and the identification matching value of each reference breeding material, the system needs to comprehensively calculate these two indicators to obtain the final traceability credibility. Specifically, the credibility calculation module will multiply the correlation degree and the identification matching value of each reference breeding material to obtain the final traceability credibility. Taking reference breeding material B as an example, its correlation degree is 0.718, its identification matching value is 12, and its final traceability credibility is 0.718x12≈8.616; the correlation degree of reference breeding material C is 0.788, the identification matching value is 13, and the final traceability credibility is 0.788x13≈10.244; the correlation degree of reference breeding material D is 0.688, the identification matching value is 10, and the final traceability credibility is 0.688x10≈6.88.

[0084] By comparing the final traceability credibility of the three reference breeding materials, the credibility 10.244 of reference breeding material C is the highest, so the system will record the electronic tag of reference breeding material C as the traceability reference basis of current corn breeding material A. The record contains the basic attribute information, whole-cycle cultivation process data and corresponding electronic tag code of reference breeding material C, which can be used to verify the identity of breeding material A and trace its cultivation history.

[0085] This way of multiplying the correlation degree and the identification matching value to calculate the final traceability credibility has clear logical basis and practical application value. From the logical level, the correlation degree reflects the similarity of the reference breeding material and the current breeding material in the cultivation process data, while the identification matching value reflects the matching degree of the two in the basic attributes. Both of them reflect the reference value of the reference breeding material from different dimensions. Multiplying the two can achieve complementary advantages: when the correlation degree of a certain reference breeding material is high but the identification matching value is low, it means that the cultivation process is similar but the basic attributes are quite different, and the credibility will not be too high after multiplication. On the contrary, if the identification matching value is high but the correlation degree is low, it means that the basic attributes are similar but the cultivation process is quite different, and the credibility will also be inhibited. Only when both are high, the final credibility will be significantly improved, so as to ensure that the selected reference breeding material has high consistency with the current breeding material in multiple dimensions.

[0086] In practical application, this calculation method can effectively avoid the limitations of a single indicator. For example, if only the correlation degree is used for screening, reference breeding materials with similar cultivation processes but large differences in basic attributes may be selected, which may belong to different varieties or different batches and cannot be accurately traced. If only the identification matching value is used for screening, reference breeding materials with similar basic attributes but large differences in cultivation processes may be selected, and the differences in cultivation processes may lead to different trait performances, which also cannot accurately reflect the history of the current breeding material. By multiplying the two, the information in different dimensions can be balanced, and the accuracy of the traceability result can be improved.

[0087] In the process of technical implementation, the following points need to be noted: First, the value range of the correlation degree and the identification matching value needs to be reasonably controlled to avoid distortion of the product result due to too large or too small values. For example, if the correlation degree takes a value range of [0, 1] and the identification matching value takes a value range of [0, 20], the product result range is [0, 20], which is convenient for subsequent comparison; second, the calculation accuracy of the correlation degree and the identification matching value needs to be ensured, and any deviation of one indicator will affect the final credibility. Therefore, in the early correlation degree calculation and identification matching value generation process, the steps of data standardization, reasonable determination of threshold interval, etc. need to be strictly followed; in addition, the system needs to have efficient numerical calculation capability, which can quickly process the credibility calculation of a large number of reference breeding materials, especially when there are more data stored in the blockchain node database, algorithm optimization is needed to improve the calculation efficiency.

[0088] For another example, suppose the characteristic identification of the current corn breeding material E contains a certain gene marker, and the correlation weight value of the gene marker in the reference breeding material F is 8 (because the frequency of the marker in the reference database is low), while the correlation degree of the reference breeding material F is 0.9 (the data of each stage of cultivation is highly similar), then the final credibility is 0.9 x 8 = 7.2; while the reference breeding material G has an identification matching value of 10 (more basic attribute matching elements), but the correlation degree is only 0.5 (the difference in the cultivation process is large), the final credibility is 0.5 x 10 = 5, obviously the credibility of the reference breeding material F is higher, and it is more suitable as a traceability reference. This example shows how this calculation method can avoid the defects of a single indicator through comprehensive evaluation.

[0089] In addition, the implementation of this process in the system needs to rely on the collaborative work of each module. The information collection module needs to accurately obtain the basic attributes and cultivation data of the current breeding material, the data processing module needs to complete standardization and stage division, the storage and retrieval module needs to obtain reference data from the blockchain, the frequency statistics and weight generation module needs to accurately calculate the identification matching value, the correlation calculation module needs to scientifically evaluate the correlation degree, and the final credibility calculation module can complete the operation based on accurate input values. The data interaction between each module needs to be realized through standardized interfaces to ensure the accuracy and integrity of data transmission.

[0090] Embodiment 5:

[0091] The corresponding traceability system realizes full-process automatic processing through the collaborative work of multiple modules. Taking the corn breeding management system developed by an agricultural technology company as an example, the system architecture and specific implementation of each module are as follows:

[0092] The system is deployed in a cloud server and connected to a blockchain node network through an API interface. The underlying database stores real-time collected data of the current breeding material, and the blockchain node database stores historical breeding material information. When a new corn breeding material is cultivated, the information collection module starts working. The module is composed of hardware devices and software programs: the hardware includes field sensors (such as temperature, humidity, and light intensity sensors), image collection devices (for shooting plant height, leaf shape, and other morphological characteristics), and RFID tag readers and writers (for reading seed batch and other basic attributes); the software program is responsible for analyzing sensor data and extracting feature identifiers from basic attribute information. For example, during the seeding stage, the information collection module obtains the seed variety name, parent number, seeding date, and other basic attributes, extracts specific SNP markers as feature identifiers through gene sequencing equipment, and generates an electronic tag code according to the coding rules, such as "MAIZE-20250623-001-ABC123", which contains material type, date, batch, and feature hash value.

[0093] The data processing module standardizes the collected full-cycle data. Taking the process of a certain corn breeding material from seeding to harvesting as an example, the collected data includes daily average temperature of 25°C, soil humidity of 60% during the seedling stage, nitrogen application amount of 15 kg / acre, irrigation amount of 30 m 3 / acre during the earing stage, and the use of a certain type of insecticide for disease and pest control during the flowering and grain filling stage. The data processing module first detects outliers for temperature data, removes extreme values (possibly caused by sensor failure) exceeding 35°C, unifies humidity units to percentage, and converts nitrogen application amount and irrigation amount to standard acreage units; then, according to the corn growth cycle, it is divided into three stages: seedling stage (0-30 days), earing stage (31-60 days), and flowering and grain filling stage (61-100 days), and each stage forms a corresponding feature data set. For example, the seedling stage feature data set includes daily average temperature, humidity, and plant height growth, and the earing stage feature data set includes fertilizer type, irrigation frequency, and stem growth rate.

[0094] The storage and retrieval module accesses the node database through a blockchain smart contract. Assuming that the feature identifier of the current breeding material contains a specific gene sequence of "Zeamays L. var. mays", the storage and retrieval module will retrieve all historical storage data containing this gene sequence in the blockchain. For example, the blockchain node returns three reference breeding materials: reference material A (cultivated in 2023), reference material B (cultivated in 2024), and reference material C (cultivated in 2022), each containing reference basic attributes (such as variety name, parent combination), reference stage feature set (cultivation data at each stage), and electronic tag storage record (such as storage time and block height).

[0095] The frequency statistics module counts the frequency of the feature identifier appearing in the reference basic attribute. Taking the feature identifier "gene sequence X" as an example, it appears once in the basic attribute of reference material A, 0 times in reference material B, and 1 time in reference material C. There are a total of 3 reference materials, and the total number of reference basic attribute elements is 10 (assuming that each reference material contains 3-4 basic attribute elements). The frequency proportion is 2 / 10 = 0.2, and the correlation weight value is 1 / 0.2 = 5. If the current breeding material has 3 feature identifiers, which are gene sequence X (weight 5), plant height parameter Y (weight 3), and leaf shape parameter Z (weight 4), 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 of each reference material. The reference basic attribute of reference material A contains gene sequence X and leaf shape parameter Z, and the identifier matching value is 5+4 = 9; reference material B contains plant height parameter Y, and the identifier matching value is 3; reference material C contains gene sequence X, and the identifier matching value is 5. This module automatically accumulates the weight values of the same elements by traversing the elements in the reference basic attribute and comparing them with the current feature identifier.

[0097] The correlation calculation module calculates the correlation degree of the reference stage feature set and the stage feature data set. Taking reference material A as an example, its sub-correlation degree set is 0.8 for seedling stage, 0.7 for ear stage, and 0.6 for flowering and grain stage. The system determines the first threshold interval as [0.65, 0.9] according to the maximum values of the sub-correlation degrees of all reference materials (such as 0.8, 0.9, 0.7). Reference material A has 2 sub-correlation degrees (0.8, 0.7) located in the interval, the correlation proportion value is 2 / 3 ≈ 0.67, and the average value of the sub-correlation degrees in the interval is (0.8+0.7) / 2 = 0.75. Assuming that the weight is the correlation proportion value 0.4 and the average value 0.6, the correlation degree is 0.67x0.4 + 0.75x0.6 = 0.718. The sub-correlation degree of reference material B is 0.7, 0.9, and 0.5, and the correlation degree is 0.788; the sub-correlation degree of reference material C is 0.6, 0.8, and 0.5, and the correlation degree is 0.688.

[0098] The credibility calculation module multiplies the correlation degree and the identifier matching value to obtain the final credibility. The credibility of reference material A is 0.718x9 ≈ 6.462, the credibility of reference material B is 0.788x3 ≈ 2.364, and the credibility of reference material C is 0.688x5 ≈ 3.44. Therefore, the credibility of reference material A is the highest. The traceability reference module retrieves the electronic tag evidence record of reference material A, including each piece of agricultural operation data (such as seeding depth 5 cm, fertilization date, etc.), environmental monitoring data (such as rainfall on a certain date), and corresponding blockchain hash value in the cultivation process, for the breeder to view the traceability.

[0099] The modules of the system interact through the data bus: the information collection module transmits raw data to the data processing module, and the processed stage characteristic data set is sent to the correlation calculation module; the reference data obtained by the storage calling module from the block chain is synchronously transmitted to the frequency statistics module and the correlation calculation module; the identification 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 collection module uses edge computing nodes to preliminarily clean the sensor data on the spot in the field, reducing the transmission pressure of the cloud; the data processing module uses a distributed computing framework to perform parallel processing on large-scale cultivation data; the storage calling module realizes multi-node data consensus verification through the smart contract of the block chain, ensuring the non-tamperability of the reference data; the correlation calculation module uses a vector database to store the stage characteristic set, accelerating the calculation efficiency of the sub-correlation degree.

[0101] For example, when the breeding personnel need to trace the parent source of a batch of corn seeds, the system reads the seed electronic tag code through the information collection module, parses the characteristic identifier, calls the reference material data in the block chain, and after calculation by each module, displays the reference material with the highest credibility as a certain variety cultivated in 2023, whose parent combination is "father A x mother B", which is highly matched with the parent information of the current seeds, and its temperature adaptation range in the cultivation process is consistent with the field performance of the current seeds, thereby providing a basis for breeding decision-making.

[0102] The implementation of the system not only realizes the whole-cycle traceability of corn breeding materials, but also guarantees the reliability of the data through the block chain technology. The collaborative work of each module makes the traceability process automated and precise, avoiding the errors of manual comparison, and is suitable for material management and traceability scenarios in large-scale corn breeding bases.

[0103] It should be noted that, in this text, relationship terms such as first and second are only used to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes" or "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0104] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for tracing electronic tags of corn breeding materials based on a blockchain, characterized in that, The method comprises the following steps: acquiring basic attribute information and whole-cycle cultivation process data of a corn breeding material, extracting a feature identifier of the basic attribute information, and generating an electronic tag code; acquiring reference breeding material data stored in a blockchain node database, the reference breeding material data comprising reference basic attributes, a reference stage feature set, and corresponding electronic tag storage records; statistically analyzing the frequency of the feature identifier in the reference basic attributes, calculating the frequency proportion of the feature identifier based on the frequency, taking the reciprocal of the frequency proportion as the correlation weight value of the feature identifier, generating an identifier matching value of each reference breeding material based on the correlation weight value, calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set, and calculating the final traceability credibility based on the correlation degree and the identifier matching value of each reference breeding material; taking the electronic tag storage record of the reference breeding material with the highest credibility as the traceability reference basis of the current corn breeding material; calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set comprises: calculating the sub-correlation degrees of each reference stage feature subset of the reference breeding material and each stage feature data subset of the current corn breeding material to obtain a sub-correlation degree set of the reference breeding material; calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set further comprises:

2. The blockchain-based electronic tagging and tracing method of corn breeding material as claimed in claim 1, wherein, determining a first threshold interval based on the maximum value of the sub-correlation degrees in the sub-correlation degree set of the different reference breeding materials, calculating the proportion of the number of sub-correlation degrees in the first threshold interval in the total number of the sub-correlation degree set to obtain a correlation proportion value of the reference breeding material, calculating the average value of the sub-correlation degrees in the first threshold interval in the sub-correlation degree set of the reference breeding material, and calculating the correlation degree of the reference breeding material based on the correlation proportion value and the average value. generating the identifier matching value of each reference breeding material based on the correlation weight value comprises:

3. The blockchain-based electronic tagging and tracing method of corn breeding material as claimed in claim 1, wherein, calculating the sum of the correlation weight values of the same elements in the reference basic attributes of the reference breeding material and the feature identifier of the current corn breeding material as the identifier matching value of the reference breeding material. calculating the final traceability credibility based on the correlation degree and the identifier matching value of each reference breeding material comprises:

4. A blockchain-based electronic tag traceability system for corn breeding materials according to any one of claims 1-3, characterized in that, taking the product of the correlation degree and the identifier matching value as the final traceability credibility. The method comprises the following steps: an information acquisition module for acquiring basic attribute information and whole-cycle cultivation process data of a corn breeding material, extracting a feature identifier of the basic attribute information, and generating an electronic tag code; a data processing module for standardizing the whole-cycle cultivation process data to obtain a stage feature data set; a storage retrieval module for acquiring reference breeding material data stored in a blockchain node database, the reference breeding material data comprising reference basic attributes, a reference stage feature set, and corresponding electronic tag storage records; a credibility calculation module for statistically analyzing the frequency of the feature identifier in the reference basic attributes, calculating the frequency proportion of the feature identifier based on the frequency, taking the reciprocal of the frequency proportion as the correlation weight value of the feature identifier, generating an identifier matching value of each reference breeding material based on the correlation weight value, calculating the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set, and calculating the final traceability credibility based on the correlation degree and the identifier matching value of each reference breeding material. The frequency statistics module is configured to count the occurrence frequency of the feature identifier in the reference basic attribute, and calculate the frequency proportion of the feature identifier based on the occurrence frequency; the weight generation module is configured to take the reciprocal of the frequency proportion as the correlation weight value of the feature identifier, and generate the identifier matching value of each reference breeding material based on the correlation weight value; The correlation calculation module is configured to calculate the correlation degree of the reference stage feature set of each reference breeding material and the stage feature data set; The credibility calculation module is configured to calculate the final traceability credibility according to the correlation degree and the identifier matching value of each reference breeding material; The traceability reference module is configured to record the electronic tag of the reference breeding material with the highest credibility as the traceability reference basis of the current corn breeding material; The correlation calculation module comprises: The sub-correlation degree calculation unit is configured to calculate the sub-correlation degree of each reference stage feature subset of the reference breeding material and each stage feature data subset of the current corn breeding material, and obtain a sub-correlation degree set of the reference breeding material; The correlation calculation module further comprises: The threshold interval determination unit is configured to determine a first threshold interval based on the maximum value of the sub-correlation degrees in the sub-correlation degree set of the different reference breeding materials; the proportion value calculation unit is configured to calculate the proportion of the number of sub-correlation degrees in the first threshold interval in the total number of the sub-correlation degree set of the reference breeding material, and obtain the correlation proportion value of the reference breeding material; the average value calculation unit is configured to calculate the average value of the sub-correlation degrees in the first threshold interval in the sub-correlation degree set of the reference breeding material; and the correlation degree calculation unit is configured to calculate the correlation degree of the reference breeding material based on the correlation proportion value and the average value.

5. The blockchain-based electronic tagging and tracing system for corn breeding material as claimed in claim 4 wherein, The weight generation module comprises: The identifier matching value calculation sub-unit is configured to calculate the sum of the correlation weight values of the same elements in the reference basic attribute of the reference breeding material and the feature identifier of the current corn breeding material as the identifier matching value of the reference breeding material.

6. The blockchain-based electronic tagging and tracing system for corn breeding material as claimed in claim 4 wherein, The credibility calculation module comprises: The credibility calculation sub-unit is configured to take the product of the correlation degree and the identifier matching value as the final traceability credibility.

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