A Low-Fluoride Tea Germplasm Resource Data Management System and Method

Abstract

This invention discloses a low-fluoride tea germplasm resource data management system and method, belonging to the field of germplasm resource data management technology. Based on the input parameters of the tea tree resource to be managed and the preset judgment rules, the system determines the environmental type and outputs the corresponding sampling scheme to obtain the original fluoride content of the tea tree resource. Based on the fluoride ion leaching rate determined by the original fluoride content, it determines whether data quality monitoring should be performed. A root hash is generated based on the traceability feature hash and its validity is verified. Then, a static code is generated by combining the semantic version number, and a metadata table is created to store dynamic parameters. If the predicted fluoride content of the tea tree resource to be managed is abnormal, an anti-oscillation mechanism is triggered, which improves the reliability of low-fluoride tea germplasm resource data management and solves the problem of low reliability in the existing low-fluoride tea germplasm resource data management.

Description

Technical Field

[0001] This invention relates to the field of germplasm resource data management technology, and in particular to a low-fluoride tea germplasm resource data management system and method. Background Technology

[0002] Crop germplasm resource information management is a technological system that uses standardized databases and management systems to digitally, networkedly, and intelligently manage the entire process of germplasm resource collection, preservation, identification, evaluation, and sharing. This system is typically built on relational databases such as MySQL and PostgreSQL to achieve structured storage and efficient utilization of resource information.

[0003] As a typical fluoride-accumulating plant, the information management of tea germplasm resources differs significantly from that of ordinary crops. Various organs of the tea plant, especially mature and older leaves, can accumulate large amounts of fluoride from the soil and atmosphere, with content far exceeding that of other crops. Furthermore, fluoride has a high dissolution rate during tea brewing. Although fluoride is an essential trace element for the human body, excessive intake can lead to chronic poisoning such as dental fluorosis and skeletal fluorosis.

[0004] For example, Chinese invention patent CN120598162A discloses a tea germplasm resource nursery management method, which includes: adopting a tea germplasm resource nursery management system composed of a data storage module, a data processing module, a system operation module, a system display module, and an external port module. The system collects tea germplasm resource nursery overall planar data and distribution data of each block, combined with meteorological station and soil moisture information data, basic information data of tea germplasm resources entering the nursery, and original data of agronomic characteristics of germplasm resources. The system converts the agronomic traits of vigorous tea germplasm resources in the 4th, 5th, and 6th years into standard values, and compares the agronomic trait data of the 7th year with the standard values. This system is a scientific management model that combines fixed-cycle tea data collection with soil and climate factors in the resource nursery.

[0005] For example, the germplasm resource data management system disclosed in Chinese invention patent CN117787863B includes: an approved and registered sample management system, a resource survey and collection management system, a germplasm resource storage site management system, a germplasm resource phenotypic database, and a germplasm resource genotype database. The approved and registered sample management system manages the data of standard crop samples; the germplasm resource phenotypic database stores the phenotypic data of crop germplasm resources; the germplasm resource genotype database stores the genotype data of crop germplasm resources; the germplasm resource storage site management system manages the data of crop germplasm resources; and the phenotypic data, genotype data, and data of crop germplasm resources are coded together by a storage site number.

[0006] In addition to focusing on conventional agronomic traits, yield, and quality components (such as tea polyphenols and amino acids), the management of tea germplasm resources must consider fluoride content, especially in leaves, as a core phenotypic indicator requiring strict monitoring and measurement. For phenotypic determination, fluoride content is typically detected using the fluoride ion selective electrode method. Regarding version management, during the breeding and purification of low-fluoride tea varieties, new generations of germplasm resources usually generate new codes (e.g., using the format "variety name-major version number.minor version number," but with an updated minor version number). Data management employs an overwrite update or archiving backup strategy, where new detection data overwrites old data, or old data is archived to correlate the performance of different generations of the same strain and trace the evolution of fluoride traits. In terms of data analysis, correlation analysis between fluoride data and local environmental data (such as soil fluoride content), agronomic traits, and quality component data is a crucial step in studying the patterns of fluoride accumulation in tea trees, environmental influencing factors, and the breeding of low-fluoride varieties.

[0007] The above-mentioned technology has at least the following technical problems: Current phenotypic data collection is too coarse-grained, making it difficult to accurately analyze the regulatory mechanisms of genotype on the spatial distribution of fluoride. Traditional techniques typically only record the average fluoride content of mixed buds and leaves, lacking refined data collection for different leaf positions (such as one bud with two leaves, one bud with five leaves, etc.). This coarse phenotypic data granularity cannot reflect the vertical distribution pattern of fluoride within tea plant lines, making it difficult for researchers to accurately assess the specific regulatory effects of different germplasm resources on the spatial distribution of fluoride, thus limiting the efficiency and depth of screening for superior low-fluoride germplasm.

[0008] The decoupling of environmental factors from phenotypic data leads to a high false-negative rate in genetic analysis. Existing management systems often lack mechanisms to bind soil fluoride content and atmospheric fluoride deposition as mandatory association fields to germplasm phenotypic data. This decoupling of environmental factors results in the inability to effectively remove environmental background noise during genome-wide association studies, causing subtle genotypic effects to be masked, leading to an extremely high false-negative rate and severely impacting the accuracy of identifying superior genetic loci.

[0009] Furthermore, existing static coding lacks scalability and cannot adapt to changes in fluoride accumulation grading information brought about by adjustments in environmental standards and propagation and purification. At the same time, due to the lack of a version inheritance mechanism, the pedigree is prone to breakage after multiple generations of purification, making it impossible to achieve full life cycle traceability, resulting in low reliability of low-fluoride tea germplasm resource data management. Summary of the Invention

[0010] To address the low reliability of low-fluoride tea germplasm resource data management in existing technologies, this invention provides a low-fluoride tea germplasm resource data management system and method. The technical solution is as follows: On the one hand, a low-fluoride tea germplasm resource data management system is provided. This system includes: a sampling strategy determination module, a data quality monitoring module, and a hash generation and dynamic encoding module. The sampling strategy determination module receives input parameters of the tea resource to be managed, determines the environment type according to preset determination rules, and outputs a sampling plan corresponding to the environment type. It then obtains the original fluoride content of the tea resource according to the sampling plan. The data quality monitoring module obtains the fluoride ion leaching rate based on the original fluoride content from the sampling strategy determination module and determines whether data quality monitoring is required based on the fluoride ion leaching rate. If no data quality monitoring is performed, the tea tree resource is directly identified as a medium-to-high fluoride tea tree resource, and the preset personnel are notified. Otherwise, the original fluoride content is corrected, and the corrected fluoride content is transmitted to the hash generation and dynamic encoding module. The hash generation and dynamic encoding module is used to generate root hashes based on traceability feature hashes, verify the validity of root hashes, generate static codes in combination with semantic version numbers, and create a metadata table to store dynamic parameters. If the predicted fluoride content of the tea tree resource to be managed is abnormal, the sampling scheme is adjusted and the anti-oscillation mechanism is triggered. Otherwise, the low-fluoride tea tree germplasm resource data management continues.

[0011] On the other hand, a method for managing low-fluoride tea germplasm resource data is provided. The method includes: S1, receiving input parameters of the tea tree resource to be managed, determining the environment type according to preset judgment rules, and outputting a sampling plan corresponding to the environment type, and obtaining the original fluoride content of the tea tree resource according to the sampling plan; S2, obtaining the fluoride ion leaching rate based on the obtained original fluoride content, and determining whether to perform data quality monitoring based on the fluoride ion leaching rate. If data quality monitoring is not performed, the tea tree resource is directly judged as a medium-to-high fluoride tea tree resource, and a preset personnel is notified; otherwise, the original fluoride content is corrected to obtain the corrected fluoride content; S3, combining the corrected fluoride content, generating a root hash based on a four-level hash including soil management record hash, atmospheric monitoring hash, fresh leaf batch hash, and gene sample hash, and verifying the validity of the root hash, generating a static code based on the semantic version number, and creating a metadata table to store dynamic parameters. If an abnormal fluoride content in the fresh leaves of the tea tree is detected in subsequent low-fluoride tea germplasm resource data management, the sampling plan is adjusted and an anti-oscillation mechanism is triggered; otherwise, the low-fluoride tea germplasm resource data management continues.

[0012] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention introduces an environmentally adaptive judgment mechanism by using soil perfluoride content and atmospheric fluoride deposition as core input parameters. Unlike the fixed sampling mode in existing technologies, this invention can intelligently adjust the sampling leaf position and frequency according to the environmental background, avoiding misjudgment of the fluoride accumulation capacity of tea trees in highly polluted areas. Then, by pre-determining the fluoride ion leaching rate, tea tree resources exceeding the leaching rate threshold are identified as medium- to high-fluoride tea tree resources, skipping the complex subsequent quality control process, thus improving the efficiency of low-fluoride tea tree germplasm resource data management and realizing rapid initial screening of low-fluoride tea tree germplasm resources. Then, through hash generation and dynamic coding modules, a four-level hash generation system covering soil management, atmospheric monitoring, fresh leaf batches, and gene samples is established, and a root hash is generated based on this. This solves the problem of the separation and easy tampering of environmental data, phenotypic data, and gene data in existing technologies, realizing strong correlation and immutability of cross-dimensional data. Separating static coding from the dynamic parameters of the metadata table ensures the stability of germplasm identity, while also recording environmental changes and version iterations, avoiding database maintenance chaos.

[0013] 2. This invention effectively avoids errors caused by single-factor judgments by setting upper and lower limits for soil perfluoride content and atmospheric fluoride deposition, greatly improving the accuracy and reliability of environmental type determination. It can accurately classify the planting environment of tea tree resources into different types, providing a scientific basis for subsequent targeted sampling plans. Different types of planting environments are affected by fluoride pollution to varying degrees. Precise classification helps to more effectively monitor and assess fluoride pollution. Then, selecting sampling sites for different environmental types can obtain more representative samples, enabling the monitoring results to truly and accurately reflect the actual situation of fluoride pollution in plants under different environments. For example, collecting single buds and one bud and two leaves in low-pollution-risk environments can help understand the growth and fluoride absorption of tea trees in low-pollution-risk environments. Finally, setting different sampling frequencies according to environmental types can ensure timely acquisition of environmental change information while avoiding unnecessary sampling work and improving monitoring efficiency. For example, high sampling frequency in high-pollution-risk environments can track pollution dynamics in a timely manner, while low sampling frequency in low-pollution-risk environments can reduce resource consumption.

[0014] 3. By judging the fluoride ion leaching rate, medium- and high-fluoride tea tree resources can be quickly distinguished, improving resource screening efficiency and effectively reducing unnecessary data quality monitoring. Then, the coefficient of variation of fluoride content and corresponding limits are judged to conduct preliminary screening of data quality. If the data quality is unqualified, resampling is carried out to ensure data accuracy. At the same time, by judging the electrode slope deviation value, electrode anomalies can be detected in time, avoiding measurement errors caused by electrode problems and ensuring the reliability of measurement results. Then, the correction coefficient is obtained by comprehensively considering the fluoride content of standard materials and electrode slope factors, which accurately reflects the measurement error and thus more comprehensively eliminates the errors generated in the measurement process, providing a scientific basis for the correction of the original fluoride content. The rationality of the correction coefficient is judged to avoid errors in the correction results due to unreasonable correction coefficients, ensuring the accuracy of the corrected data. Finally, by correcting the original fluoride content, the error in the measurement process is eliminated, and a more accurate corrected fluoride content is obtained, improving the reliability of fluoride content data of tea tree resources and thus improving the quality of data management of low-fluoride tea tree germplasm resources. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the structure of a low-fluoride tea germplasm resource data management system provided in this application embodiment; Figure 2 A flowchart for data quality monitoring and execution provided in this application embodiment; Figure 3 A trend graph illustrating the fluoride content correction effect provided in the embodiments of this application; Figure 4 Box plot for performance verification of the tea tree fluoride content prediction module provided in the embodiments of this application; Figure 5 A model architecture diagram for predicting fluoride content in tea plants provided in an embodiment of this application; Figure 6 A flowchart illustrating the adjustment of the sampling scheme provided in the embodiments of this application. Detailed Implementation

[0017] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0018] In this application, the use of prefixes such as "first" and "second" is solely for the purpose of distinguishing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] like Figure 1 The diagram shown is a structural schematic of a low-fluoride tea germplasm resource data management system provided in an embodiment of this application. The system includes: a sampling strategy determination module, a data quality monitoring module, and a hash generation and dynamic encoding module.

[0021] The sampling strategy determination module receives input parameters from the tea tree resources to be managed. Based on preset determination rules, it uses soil total fluoride content and atmospheric fluoride deposition to determine the environmental type, such as a low-pollution-risk environment, a high-pollution-risk environment, or a standard equilibrium environment. It then outputs a sampling plan corresponding to the environmental type. Following the sampling plan, it obtains the original fluoride content of the tea tree resource. Input parameters include the germplasm resource number, soil total fluoride content, and atmospheric fluoride deposition. The germplasm resource number uniquely identifies each tea tree germplasm resource. Soil total fluoride content reflects the fluoride content in the soil where the tea trees grow. Atmospheric fluoride deposition reflects the impact of atmospheric fluoride on the tea tree's growth environment. The original fluoride content is obtained by directly measuring the fluoride content in the tea trees after collecting samples according to the sampling plan. By comprehensively considering soil total fluoride content and atmospheric fluoride deposition to determine the environmental type and outputting a targeted sampling plan, it can obtain more representative fresh tea leaf samples based on different environmental characteristics, improving the accuracy and reliability of the original fluoride content data.

[0022] Specifically, the environment type is determined according to preset judgment rules, and a sampling scheme corresponding to the environment type is output. The specific method is as follows: Soil perfluoride content was obtained using an alkaline fusion-ultrasonic extraction-ion chromatography method. Soil organic matter was destroyed by sodium hydroxide fusion to release fluorides, ultrasonic extraction accelerated the dissolution of the fused material, and ion chromatography achieved high-sensitivity separation and detection. Atmospheric deposition (dry deposition + wet deposition) was collected, dissolved, and fluoride content was determined using a fluoride ion-selective electrode. The measured data of atmospheric fluoride deposition were obtained by calculating the ratio of 10^6, the product of fluoride content and sampling area, to the product of sampling area and number of sampling days.

[0023] If the soil total fluoride content is less than the lower limit of soil total fluoride content and the atmospheric fluoride deposition is less than the lower limit of atmospheric fluoride deposition, it is judged as a low-pollution-risk environment, and the first sampling plan is output. The first sampling plan is specifically: collecting single buds and one bud and two leaves, with a sampling frequency of once per quarter. Soil total fluoride content refers to the total amount of all fluoride elements in the soil, reflecting the overall degree of fluoride pollution in the soil. Atmospheric fluoride deposition refers to the amount of fluoride elements in the atmosphere that settles to the ground per unit area and per unit time through dry deposition (such as gaseous fluorides directly adhering to the surface of objects) and wet deposition (such as falling to the ground with precipitation processes such as rain and snow).

[0024] If the soil total fluoride content exceeds the upper limit of soil total fluoride content or the atmospheric fluoride deposition exceeds the upper limit of atmospheric fluoride deposition, the environment is judged as a high-pollution-risk environment, and a second sampling plan is output. The second sampling plan specifically involves collecting samples of one bud and five leaves and mature leaves, with a sampling frequency of once a month. The lower and upper limits of soil total fluoride content are threshold values ​​set based on soil environmental monitoring data, relevant environmental standards, and research objectives to classify the degree of soil fluoride pollution. The lower limit of soil total fluoride content indicates that the fluoride content in the soil is at a relatively safe and low-pollution level, while the upper limit of soil total fluoride content indicates that the fluoride content in the soil has reached a high level, which may have adverse effects on tea germplasm resources. The lower and upper limits of atmospheric fluoride deposition are threshold values ​​set based on atmospheric environmental monitoring data and environmental standards to classify the degree of atmospheric fluoride deposition pollution. The lower limit of atmospheric fluoride deposition represents that the atmospheric fluoride deposition is within the normal and low-pollution range, while the upper limit of atmospheric fluoride deposition indicates that the atmospheric fluoride deposition is too high and may cause environmental problems.

[0025] If neither of the above two conditions is met, it is determined to be a standard balanced environment, and the third sampling scheme is output. The third sampling scheme is specifically manifested as: collecting samples of one bud and two leaves and one bud and four leaves, with a sampling frequency of once every two months.

[0026] The environmental pollution levels of the first, third, and second sampling schemes increase sequentially, while the sampling frequencies of the first, third, and second sampling schemes decrease sequentially. Single bud, one bud with two leaves, one bud with four leaves, one bud with five leaves, and mature leaf are morphological descriptions of different growth stages of tea tree leaves. Single bud refers to a single bud at the top of a new shoot of the tea tree that has not yet unfolded; one bud with two leaves refers to a bud with two newly unfolded leaves; one bud with four leaves and one bud with five leaves are similar, with one bud having four and five leaves respectively; mature leaf refers to the leaves of tea trees that have grown to the mature stage, which are usually large and thick.

[0027] It is important to understand that by comparing soil perfluoride content and atmospheric fluoride deposition with preset upper and lower limits, environments can be accurately classified into three types: low-risk, high-risk, and standard equilibrium. This comprehensively considers both soil and atmosphere, two important sources of fluoride pollution, providing a scientific basis for developing targeted sampling plans. Different environmental types correspond to different sampling plans, allowing for targeted collection of tea tree leaf samples based on the degree and characteristics of environmental pollution. In high-risk environments, samples of one bud with five leaves and mature leaves are collected, as these leaves have a longer growth period and may absorb and accumulate more fluoride, thus more accurately reflecting the impact of environmental fluoride pollution on tea trees. In low-risk environments, single buds and one bud with two leaves are collected, as these are more suitable for lower-risk environments. Tender leaves are more sensitive to environmental changes, allowing for the timely detection of subtle shifts in environmental quality. Different sampling frequencies are determined based on environmental type, ensuring timely monitoring of environmental changes while avoiding unnecessary sampling. In high-pollution-risk environments, monthly sampling closely monitors changes in pollution levels. In low-pollution-risk environments, quarterly sampling meets monitoring needs while reducing costs. Standard balanced environments use a bi-monthly sampling frequency, striking a balance between monitoring effectiveness and cost. Precise environmental classification and targeted sampling schemes enable more effective acquisition of data reflecting fluoride pollution in the tea tree's growing environment, improving the accuracy and reliability of monitoring.

[0028] The data quality monitoring module is used to determine the fluoride ion leaching rate based on the original fluoride content in the sampling strategy. Based on the fluoride ion leaching rate, it determines whether data quality monitoring should be performed. If no data quality monitoring is performed, the tea tree resource is directly classified as a medium-to-high fluoride tea tree resource, and a pre-defined personnel are notified. Otherwise, the original fluoride content is corrected, and the corrected fluoride content is transmitted to the hash generation and dynamic encoding module. By calculating the fluoride ion leaching rate, data quality is judged, and data that does not meet the requirements is corrected, ensuring that the data entering subsequent modules is accurate and reliable, thus improving the data quality of fluoride content in low-fluoride tea tree resources. For cases directly classified as medium-to-high fluoride tea tree resources, it can quickly screen out tea tree germplasm resources that may have problems and promptly notify pre-defined personnel, improving the efficiency of tea tree germplasm resource screening.

[0029] like Figure 2 The flowchart shown illustrates the data quality monitoring judgment and execution process. Specifically, the determination of whether to perform data quality monitoring is based on the leaching rate of fluoride ions. The specific process is as follows: The total fluoride content and water-leached fluoride content of the fresh leaves corresponding to the tea plant resources under management were determined by a fluoride ion selective electrode. The total fluoride content refers to the total amount of fluoride in the fresh leaves, while the water-leached fluoride content refers to the fluoride content that is leached from the fresh leaves into the water through a specific method (such as soaking the fresh leaves in water before measurement). The ratio of the total fluoride content to the water-leached fluoride content of the fresh leaves corresponding to the tea plant resources under management was recorded as the corresponding leaching rate, which reflects the degree to which fluoride ions in the fresh tea leaves can be leached in water. By accurately measuring the total fluoride content and the water-leached fluoride content and calculating the ratio, the degree of fluoride ion leaching in the fresh leaves can be accurately quantified, providing an objective and reliable data basis for subsequent classification and judgment, and avoiding the inaccuracy of relying solely on qualitative judgment.

[0030] If the leaching rate of the fresh leaves of the tea tree resource under management exceeds the corresponding leaching rate threshold, it indicates that fluoride ions in the fresh leaves of that tea tree resource are relatively easy to leach out. Without further steps, this tea tree resource is directly classified as a medium-to-high fluoride tea tree resource, and the pre-set personnel are notified. This allows for the rapid identification of tea tree resources that may have fluoride content issues. Otherwise, data quality monitoring is performed. The leaching rate threshold is a pre-set value used by pre-set personnel to classify the fluoride content status of tea tree resources. Using the leaching rate threshold as a standard for rapid judgment can efficiently screen out tea tree resources that may have fluoride content issues. For tea tree resources with a leaching rate greater than the threshold, timely classification as medium-to-high fluoride tea tree resources and notification to pre-set personnel helps to take early measures, such as adjusting planting management methods and changing processing techniques, to reduce the impact of fluoride content on tea quality and safety. For tea tree resources with a leaching rate not exceeding the threshold, data quality monitoring can avoid misjudgments due to data errors and ensure the accuracy of the judgment results.

[0031] If the leaching rate of fresh leaves of various tea varieties within a historical time period is available, the leaching rate threshold is set based on the average leaching rate of fresh leaves of various tea varieties within that historical time period. This makes the threshold more consistent with the actual situation and improves the accuracy and reliability of the judgment. If the leaching rate of fresh leaves of various tea varieties is not available within a historical time period, the leaching rate threshold is set by pre-set personnel based on the tea variety. With their professional knowledge and experience, combined with the characteristics of the tea variety, the pre-set personnel can formulate an appropriate threshold in the absence of data to ensure the accuracy of the judgment.

[0032] It should be added that the data quality monitoring process is as follows: When monitoring tea tree resources under management, the average total fluoride content of the corresponding fresh leaves and the standard deviation of the total fluoride content of the corresponding fresh leaves are obtained. The ratio of the standard deviation of the total fluoride content of the corresponding fresh leaves to the average total fluoride content of the corresponding fresh leaves is calculated and recorded as the coefficient of variation of fluoride content. This coefficient of variation reflects the fluctuation of fluoride content data in fresh leaves of tea trees. The average value can intuitively reflect the average level of fluoride content in fresh leaves of tea trees, while the standard deviation can reflect the dispersion of the data. The coefficient of variation of fluoride content combines the information of both, providing a quantitative indicator for judging data quality and helping to quickly assess the stability and reliability of the data.

[0033] If the coefficient of variation of fluoride content exceeds the limit, the original fluoride content of the collected tea tree resource will be marked as unqualified, and the pre-set personnel will be prompted to resample. The limit of fluoride content coefficient of variation is a value set by the pre-set personnel based on experience to define whether the data quality is qualified or not. For example, it can be set as the average value of the coefficient of variation of fluoride content over a historical period. Timely marking of unqualified data and prompting resampling avoids inaccurate results caused by large data fluctuations, ensures the accuracy of subsequent analysis, and improves data quality.

[0034] Otherwise, after simultaneously acquiring the calibration parameters, electrode anomaly determination is performed. The calibration parameters include the measured fluorine content of the preset standard substance, the measured electrode slope, and the theoretical electrode slope. In electrochemical measurements, the electrode slope is a parameter describing the relationship between the electrode potential and the concentration of the analyte, reflecting the electrode's sensitivity to concentration changes. The specific method for obtaining the measured electrode slope is as follows: prepare a series of fluorine standard solutions with different concentrations, such as 1 mg / kg, 5 mg / kg, 10 mg / kg, 15 mg / kg, and 20 mg / kg. Using a calibrated fluoride ion selective electrode and a reference electrode, insert the electrodes into the above standard solutions sequentially. After the potential stabilizes, record the electrode potential value corresponding to each standard solution. Calculate the slope of the standard curve using linear regression analysis; this slope is the electrode slope. By introducing the measured fluorine content of the standard substance, the measured electrode slope, and the theoretical electrode slope, it is possible to accurately determine whether the electrode is working properly, providing a basis for subsequent data calibration and ensuring the accuracy of the measurement results.

[0035] The motor malfunction detection mechanism identifies electrode malfunctions if the electrode slope deviation exceeds a preset limit. Otherwise, electrode slope optimization is performed, and the original fluorine content is corrected. The electrode slope deviation is obtained by performing relative deviation processing on the measured and theoretical electrode slopes. In this application, relative deviation processing means calculating the ratio of the difference between the measured and theoretical values ​​to the theoretical value. The electrode slope deviation limit is a value set by experienced personnel to determine whether the electrode slope is acceptable, such as the average value of electrode slope deviations over a historical period. By calculating the electrode slope deviation and comparing it with the limit, electrode malfunctions can be quickly and accurately detected, prompting timely maintenance or electrode replacement to avoid measurement errors caused by electrode problems.

[0036] Electrode slope optimization, specifically: When collecting the original fluoride content of fresh leaves corresponding to the tea tree resources to be managed, the temperature of the tea tree fresh leaf extract was simultaneously recorded by a temperature sensor placed in the tea tree fresh leaf extract. The temperature of the extract was recorded and the electrode slope was calibrated using the Nernst equation. The influence of temperature on the electrode slope was taken into account, so that the electrode slope was more in line with the actual measurement conditions and the accuracy of electrode measurement was improved.

[0037] Electrode slope calibration was performed using the relationship between the theoretical slope of the Nernst equation and temperature, resulting in a calibrated electrode slope. The Nernst equation is an equation in electrochemistry that describes the relationship between electrode potential and ion concentration in solution, and its theoretical slope changes with temperature. Using the optimized electrode slope to correct the original fluoride content can eliminate the influence of electrode slope error on the measurement results, resulting in more accurate fluoride content data for fresh tea leaves.

[0038] like Figure 3 The trend chart showing the fluoride content correction effect is as follows: The horizontal axis, "Raw Fluoride Content (mg / kg)" (i.e., the original fluoride content, in mg / kg), represents the input data obtained from sampling, ranging from 8 to 32 mg / kg, covering the typical range of low-fluoride tea trees; the vertical axis, "Corrected Fluoride Content (mg / kg)" (i.e., the corrected fluoride content value), is proportional to the horizontal axis to avoid visual misleading. The green scatter points in the chart correspond to "Sample Points" (the actual data pairs of the original and corrected fluoride content), and the red fitted line is labeled "Fitted Line (R... 2 =0.993), R 2=0.993 indicates an extremely high degree of fit, and the black ideal correction line, marked "Ideal Correction(y=x)", represents the ideal state where the corrected value is completely consistent with the original value. Together, they prove that the corrected value is highly fitted to the original value, effectively correcting measurement deviations and ensuring the accuracy of the data input to subsequent modules.

[0039] It should be added that the original fluoride content needs to be corrected, and the specific procedure is as follows: The product of the fluorine content ratio and the electrode slope ratio is used as a correction coefficient to correct the original fluorine content. The fluorine content ratio represents the ratio of the measured fluorine content of the preset standard substance to the standard fluorine content of the preset standard substance, reflecting the degree of difference between the actual measured value and the standard value. The electrode slope ratio represents the ratio of the calibrated electrode slope to the theoretical electrode slope, used to evaluate the degree of closeness between the calibrated electrode performance and the theoretical performance. By integrating factors from both measurement and electrode performance, a comprehensive correction basis is provided for correcting the original fluorine content, which helps to improve the accuracy and rationality of the correction.

[0040] If the correction coefficient is not within the preset range, the data is marked as pending verification, and the preset personnel are notified. Otherwise, the original fluoride content continues to be corrected. Specifically, a correction coefficient is introduced, and the original fluoride content of the tea plant resource is multiplied to obtain the corrected fluoride content. This eliminates deviations caused by various factors (such as electrode performance and measurement errors) during the measurement process, resulting in more accurate and reliable fluoride content data for the tea plant resource. The correction coefficient range is a criterion for judging the reliability of the measurement results. The correction coefficient range is set between the difference between the average value and three times the standard deviation over a historical period and the sum of the average value and three times the standard deviation. By determining whether the correction coefficient is within the range, potentially erroneous or abnormal data can be quickly screened out. For data outside the correction coefficient range, it is marked as pending verification, and the preset personnel are notified, enabling timely detection of problems in the measurement process and preventing erroneous data from adversely affecting subsequent analysis and decision-making. For data within the correction coefficient range, it directly proceeds to the subsequent correction steps, improving data processing efficiency and ensuring that subsequent correction operations are based on reliable data.

[0041] The original fluoride content includes the total fluoride content and the fluoride content leached from water, while the corrected fluoride content includes the corrected total fluoride content and the corrected fluoride content leached from water.

[0042] The hash generation and dynamic encoding module generates root hashes based on traceability feature hashes and verifies their validity to ensure data integrity and traceability. It combines semantic version numbers to generate static codes that uniquely identify specific versions of tea germplasm resource data and creates a metadata table to store dynamic parameters, enabling dynamic updates and management of fluoride content. A Long Short-Term Memory Network (LSTM) is used to predict the fluoride content of tea resources under management. The fluoride content of low-fluoride tea trees changes dynamically with growth cycles and climate fluctuations; LSTM effectively captures these long-term dependencies. If the predicted fluoride content of the tea resources under management is abnormal, the sampling scheme is adjusted and an anti-oscillation mechanism is triggered; otherwise, low-fluoride tea germplasm resource data management continues. The traceability feature hash represents a hash value generated based on the relevant traceability information of tea germplasm resources. The root hash is a Merkle tree root hash, a binary tree structure based on hash values, where each node stores the hash value of a data block. The traceability feature hashes are combined and hashed again until a unique root hash value is generated. By generating the root hash and verifying its validity, the integrity of tea germplasm resource data during transmission and storage can be ensured, while facilitating the tracing of the data's source and changes. Static codes are generated by combining semantic version numbers, providing clear identifiers for different versions of the data, which facilitates data management and updates. A metadata table is created to store dynamic parameters, which can flexibly adjust the data management strategy according to the actual situation. When the predicted fluoride content is abnormal, the sampling scheme is adjusted and an anti-oscillation mechanism is triggered, which can ensure stable operation in the face of abnormal situations and avoid instability caused by frequent adjustments.

[0043] like Figure 4The box plot shown here is a validation of the tea tree fluoride content prediction module performance. Specifically, the horizontal axis "Model + Feature" covers the Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), and bidirectional LSTM models with different feature dimensions. Specifically, Bi-LSTM(Env) only uses environmental variables as input, while Bi-LSTM(Env+Soil) combines environmental and soil variables as input, and Bi-LSTM(Full) may use all available features, including environmental, soil, growth cycle data, and historical fluoride content data of tea tree resources, reflecting the design logic of multi-dimensional feature input. The vertical axis "Prediction Error (RMSE, mg / kg)" displays the prediction deviation in the range of 0-1.2, with the green dashed line indicating an acceptable error threshold of 0.3 mg / kg. The box plot and the red mean line clearly show that the prediction error of the "full-feature bidirectional LSTM" (RMSE≈0.28) is significantly lower than that of the traditional model and the single-feature LSTM, and is below the acceptable threshold, which verifies the high accuracy of the module and provides reliable data support for the determination of abnormal fluorine content.

[0044] like Figure 5 The model architecture diagram for predicting fluoride content in tea trees, as shown, first transforms the input feature dimensions into vector form using a multidimensional feature encoding layer. This vector is then fed into a bidirectional LSTM temporal feature extraction layer, which utilizes the forward and backward information transfer mechanism of the bidirectional LSTM to capture the dynamic temporal correlations between different features (such as the time dependence between atmospheric fluoride deposition and tea tree fluoride accumulation). The outputs of each LSTM unit are fused into a comprehensive feature vector through a temporal feature concatenation layer, and then mapped through a fully connected prediction layer. Finally, the fluoride content regression output layer outputs the prediction result. The labeling of "last input time step" reflects the LSTM's focus on the information at the end of the time-series data, ensuring the timeliness and accuracy of the prediction. The input feature dimensions of the LSTM are multidimensional time-series data, including: environmental parameters such as temperature, humidity, light intensity, rainfall, and atmospheric fluoride deposition; soil parameters such as pH value, organic matter content, fluoride ion concentration, soil total fluoride content, and soil exchangeable fluoride content; growth cycle data: tea tree growth stage and harvesting time; and historical fluoride content data of tea tree resources. The LSTM output predicts the fluoride content of tea plant resources at future time steps. For example, using multidimensional time series data from the first 20 time steps as input, the fluoride content at the 21st time step can be predicted.

[0045] Specifically, the root hash is generated based on the source feature hash, and the specific method for obtaining it is as follows: The traceability feature hash includes soil management record hash, atmospheric monitoring hash, fresh leaf batch hash, and genome sample hash; the traceability feature hash is a set of hash values ​​used to trace the origin and characteristic information of fresh tea leaves.

[0046] The soil management record hash is generated by concatenating the tea garden plot number and the soil exchangeable fluoride content into strings in sequence and then using a hash algorithm. The hash algorithm used in this application is SHA-256. The soil exchangeable fluoride content refers to the content of fluoride in the soil in an exchangeable form. The form of fluoride in the soil affects its absorption by plants, thus affecting the fluoride content in fresh tea leaves. By combining the tea garden plot number and the soil exchangeable fluoride content, two key pieces of information, a hash value can be generated, which can uniquely identify the soil fluoride-related management information of the tea garden plot. On the one hand, the tea garden plot number determines the source location of the soil information, and the soil exchangeable fluoride content reflects the fluoride enrichment status of the soil in the plot. The combination of these two can accurately record key data on soil management. On the other hand, the irreversibility and uniqueness of the hash algorithm ensure the integrity and security of the data, prevent data tampering, and provide a reliable basis for soil management information for subsequent tea traceability.

[0047] The atmospheric monitoring timestamp and atmospheric fluoride deposition amount are concatenated into a string, and the result generated by the hash algorithm is recorded as the atmospheric monitoring hash. The atmospheric monitoring timestamp records the specific time of atmospheric monitoring, and the atmospheric fluoride deposition amount reflects the degree of impact of fluoride in the atmosphere on the environment at that time and place. By concatenating these two to generate a hash value, the atmospheric fluoride pollution status at a specific point in time can be accurately recorded. Atmospheric fluoride deposition may be absorbed by fresh tea leaves, affecting the fluoride content and safety of fresh tea leaves. The hash algorithm ensures the integrity and immutability of atmospheric monitoring data, providing reliable atmospheric environmental information for tea tree traceability.

[0048] The harvest timestamp, grower ID, and corrected total fluoride content are concatenated into strings in the order of "harvesting timestamp, grower ID, and corrected total fluoride content," and the result generated using a hash algorithm is recorded as the fresh leaf batch hash. The harvest timestamp determines the harvest time of the fresh leaves, and the quality of fresh leaves at different harvest times may vary due to factors such as growth stage and climate conditions. The grower ID can be traced back to the grower of the fresh leaves to understand the management situation during the planting process. The corrected total fluoride content directly reflects the fluoride content of the fresh leaves and is an important indicator for measuring the quality and safety of tea tree fresh leaves. Concatenating these three pieces of information to generate a hash value can uniquely identify a batch of fresh leaves and accurately record the key information of that batch of fresh leaves.

[0049] The genome extraction time and negative control quality control results are concatenated into strings, and the resulting hash is denoted as the genome sample hash. The negative control quality control result is used to determine whether there is exogenous contamination during the genome extraction process. The negative control quality control result is the result obtained from the negative control experiment set up in the genome extraction experiment. The negative control does not add genome to the experiment and is used to determine whether there is exogenous contamination during the experiment. If the negative control has a positive result (i.e., a substance that should not be present is detected), it indicates that the genome extraction experiment may have been contaminated, and the result is unreliable. The genome extraction time records the time point when the genome sample was obtained; the negative control quality control result ensures the reliability of the genome extraction process. If the negative control result is normal, it indicates that the extraction process was not affected by exogenous contamination, and the genome data is authentic and reliable. Concatenating these two pieces of information to generate a hash value can provide a unique identifier for the genome sample, ensuring the integrity and credibility of the genome data. In the traceability of fresh tea leaves, genome information can be used to identify the tea variety, origin, etc.

[0050] Specifically, the process for verifying the validity of the root hash is as follows: During genome extraction, the genome sample undergoes a pre-screening process, namely agarose gel electrophoresis. If the pre-screening result is unsatisfactory, i.e., the gene concentration is lower than the gene concentration threshold specified for the gene extraction experiment, or the extracted genome sample is contaminated, the sample is marked as invalid and a resampling prompt is issued, and the process is terminated while waiting for the negative control quality control results. Pre-screening can screen out unqualified samples in advance, avoiding subsequent invalid experimental operations. If a sample is unqualified, it is marked as invalid and a resampling prompt is issued, while the process is terminated while waiting for the negative control quality control results, preventing confusion and erroneous results in the entire process due to sample problems. A genome sample refers to the material containing complete genetic information extracted from fresh tea leaves; gene concentration indicates the amount of genetic material contained in a unit volume of genome sample; the gene concentration threshold is the minimum gene concentration set by pre-defined personnel to ensure the normal conduct of the experiment and the reliability of the results; agarose gel electrophoresis is a technique that uses the different migration rates of genome molecules in an electric field to separate genome fragments according to size in a gel medium. Genomes carry a negative charge and will move from the negative electrode to the positive electrode; high-quality, complete genome DNA should appear as a clear, bright, and concentrated main band on the electrophoresis image, usually above 23kb; if severe tailing, diffusion, or disappearance of the main band occurs, it indicates that the DNA has been severely degraded and cut into small fragments; if other unexpected bands appear in addition to the main band (such as a very bright low molecular weight band), there may be ribonucleic acid contamination, such as small fragments or protein contamination formed after ribonucleic acid degradation. Abnormal position of the main band may also indicate contamination.

[0051] Before obtaining the negative control quality control results, the strings will be concatenated in the order of "soil management record hash, atmospheric monitoring hash, and fresh leaf batch hash" to generate a temporary hash value using a hash algorithm, and marked as pending genome verification. The generation of the temporary hash value can integrate the data characteristics related to soil, atmosphere, and fresh leaf batch in advance to form a preliminary data identifier for subsequent verification. Marking it as pending genome verification indicates that the hash value still needs to be further verified in combination with the genome sample hash, providing a basis for the data integrity verification of tea germplasm resources.

[0052] If the corrected total fluoride content in the batch hash of fresh leaves is greater than the total fluoride content threshold, it indicates that there is a quality problem with the fresh leaves. The negative control quality control result is directly recorded as abnormal, the hash is marked as invalid and a resampling is prompted. This can quickly identify the problematic sample, avoid unnecessary subsequent experimental operations, and improve the efficiency and accuracy of tea germplasm resource data management. The total fluoride content threshold is the maximum allowable total fluoride content in fresh leaves set by preset personnel.

[0053] If the negative control quality control result fails, it indicates a problem with the gene extraction experiment. The hash value will be marked as invalid to prevent erroneous data from entering subsequent analysis and to ensure the reliability of tea germplasm resource data management. Otherwise, the hash value will be concatenated in the order of "soil management record hash, atmospheric monitoring hash, fresh leaf batch hash, and genome sample hash" and a hash algorithm will be used to generate a root hash to verify the integrity and consistency of tea germplasm resource data and to ensure the correlation and accuracy between genome samples and environmental factors such as soil, atmosphere, and fresh leaf batches.

[0054] Specifically, static code is generated by combining semantic version numbers, and a metadata table is created to store dynamic parameters. The specific process is as follows: The static coding structure includes: germplasm resource number, low-fluoride type marker, resource number, and propagation and testing version number. The propagation and testing version number includes a major version number and a minor version number. In this application, the propagation and testing version number adopts the concept of a semantic version number to identify different stages or changes in the propagation and testing process of germplasm resources. The germplasm resource number is a unique identifier for a specific germplasm resource, used for management and tracking. The low-fluoride type marker is used to identify that the germplasm resource is low-fluoride, facilitating rapid screening of low-fluoride tea tree germplasm resources. The static code is a number that provides detailed identification of germplasm resources, including information such as the source and characteristics of the germplasm resources. For example, the static code might be ZY12345-LF-RS67890-1.0, where ZY12345 is the germplasm resource number, LF indicates the low-fluoride type marker, RS67890 is the resource number, and 1.0 is the propagation and testing version number, with the major version number being 1 and the minor version number being 0. Through static coding, each low-fluoride tea germplasm resource can be uniquely and clearly identified, facilitating storage, retrieval, and identification in data management.

[0055] A metadata table is created to store dynamic parameters, including soil fluoride background values, atmospheric fluoride background values, and leaching rate detection values. This metadata table is then associated with germplasm resources, ensuring that each germplasm resource has a corresponding dynamic parameter record. For example, in a database, the germplasm resource ID can be used as a foreign key to link the metadata table with the germplasm resource table. Storing dynamic parameters in association with germplasm resources allows for comprehensive recording of relevant data under different environmental conditions, providing rich data support for subsequent analysis and research. Furthermore, this associated storage method facilitates unified data management and retrieval, improving data management efficiency.

[0056] If a change in the total fluoride content threshold is detected in subsequent low-fluoride tea germplasm resource data management, and the change in the total fluoride content threshold exceeds the preset limit for the change in the total fluoride content threshold, the major version number increment logic is triggered, while the minor version number is reset to zero. The change in the total fluoride content threshold represents the result obtained by processing the relative deviation between the current total fluoride content threshold and the previous total fluoride content threshold. The total fluoride content threshold is adjusted according to the preset standard. For example, if the preset limit for the change in the fluoride content threshold is 10%, the current fluoride content threshold is 5 mg / kg, and the previous fluoride content threshold is 4 mg / kg, the relative deviation is 25%, which is greater than 10%. In this case, the major version number changes from 1 to 2, and the minor version number becomes 0. The version number update mechanism can promptly reflect significant changes in the fluoride content standards of germplasm resources. The increment of the major version number indicates a qualitative change in the fluoride content characteristics of the germplasm resources, requiring reassessment and management. The reset of the minor version number to zero marks the start of a new version cycle, facilitating the differentiation and management of germplasm resources at different stages and helping to ensure the accuracy and timeliness of data management.

[0057] The propagation registration records of the tea tree resources to be managed are entered by designated personnel. If the management detects that the next generation of seedlings has been propagated from the tea tree resources, the minor version number of the next generation seedlings will be incremented by the minor version number of the original tea tree resource. The major version number of the next generation seedlings will then be recorded as the major version number of the original tea tree resource. For example, if the major version number of the parent tea tree resource is 1 and the minor version number is 2, the major version number of its next generation seedlings will also be 1 and the minor version number will be 3. Through the incrementing minor version number mechanism, the propagation generations and inheritance relationships of the germplasm resources can be clearly recorded, facilitating the tracking and management of the evolutionary process of the germplasm resources.

[0058] like Figure 6 The flowchart shown illustrates the sampling scheme adjustment process. Specifically, adjusting the sampling scheme and triggering the anti-oscillation mechanism follows the steps below: If the predicted total fluoride content of the tea tree resource to be managed is greater than the corresponding total fluoride content threshold, it indicates that the tea tree resource may have a risk of excessive fluoride content, triggering the anti-shock mechanism. Otherwise, it indicates that the fluoride content of the current tea tree resource is within a safe range, and low-fluoride tea tree germplasm resource data management continues. By comparing the predicted total fluoride content, tea tree resources that may have a risk of excessive fluoride content can be quickly screened out, and the anti-shock mechanism can be triggered in a timely manner.

[0059] The anti-vibration mechanism is triggered as follows: if the predicted total fluoride content for the preset period is greater than the corresponding total fluoride content threshold, it indicates that the tea tree resource has a risk of excessive fluoride content. The sampling plan is then adjusted to more accurately monitor and control the fluoride content. Otherwise, it indicates that the current excessive fluoride content in the tea tree is not persistently severe, and the management of low-fluoride tea tree germplasm resource data continues. The preset period is a pre-set time interval within which the fluoride content of the tea tree resource is predicted multiple times to determine whether the excessive fluoride content continues, thereby deciding whether to trigger subsequent operations such as adjusting the sampling plan. By setting a preset period for continuous monitoring, misjudgments caused by single prediction errors are avoided. The sampling plan is only adjusted when the fluoride content continues to exceed the standard within the preset period, ensuring the accuracy of the adjustment decision and preventing unnecessary changes to the sampling plan due to accidental factors.

[0060] The sampling scheme has been adjusted as follows: By adjusting the sampling leaf position of the corresponding tea tree resources to only collect single buds and adjusting the sampling frequency to a safe sampling frequency of once a week, the fluoride content information of tea trees can be obtained more accurately. The sampling leaf position is the leaf position on the tea tree plant selected for sample collection. Collecting single bud samples helps to more accurately reflect the fluoride content of tea trees.

[0061] During the adjustment of the sampling plan, the total fluoride content of the corresponding tea tree resources is monitored. If the total fluoride content monitored for a preset number of times is lower than the value corresponding to the preset proportion of the total fluoride content threshold, it indicates that the fluoride content of the tea trees has been reduced to a relatively safe level. The environment type is then re-determined according to the preset judgment rules (consistent with the preset judgment rules mentioned above), and a sampling plan corresponding to the environment type is output. The preset number of times is preset by the preset personnel, for example, it can be set to 3 times; the preset proportion is preset by the preset personnel, for example, it can be set to 80%. Continuously monitoring the fluoride content during the adjustment of the sampling plan and determining whether to re-determine the environment type and sampling plan can adapt to changes in the tea tree growth environment in a timely manner, ensuring that the sampling plan always matches the actual situation and improving the scientificity and effectiveness of tea tree germplasm resource data management.

[0062] This application provides a method for managing low-fluoride tea germplasm resource data, including: S1 receives input parameters of the tea tree resources to be managed, determines the environment type according to preset judgment rules, and outputs a sampling plan corresponding to the environment type. The original fluoride content of the tea tree resources is obtained according to the sampling plan. By determining the sampling plan according to the environment type, tea tree resource data can be obtained more specifically. Considering the impact of different environments on the fluoride content of tea trees, the collected original fluoride content data is more representative and accurate, providing a reliable basis for subsequent analysis and management.

[0063] S2. Based on the obtained original fluoride content, the fluoride ion leaching rate is obtained. Based on the fluoride ion leaching rate, it is determined whether data quality monitoring should be performed. If data quality monitoring is not performed, the tea tree resource is directly identified as a medium-to-high fluoride tea tree resource, and the preset personnel are notified. Otherwise, the original fluoride content is corrected to obtain the corrected fluoride content. By determining whether data quality monitoring should be performed, unnecessary monitoring processes are avoided while ensuring data quality, thus improving efficiency. Correcting the original fluoride content can eliminate possible measurement errors, improve data accuracy, and make the judgment of fluoride content of tea tree resources more reliable.

[0064] S3, combining fluoride content correction, generates a root hash based on a four-level hash system including soil management record hash, atmospheric monitoring hash, fresh leaf batch hash, and gene sample hash, and verifies the validity of the root hash. It then generates a static code using a semantic version number and creates a metadata table to store dynamic parameters. If abnormal fluoride content is detected in fresh tea leaves during subsequent low-fluoride tea germplasm resource data management, the sampling plan is adjusted and an anti-vibration mechanism is triggered; otherwise, low-fluoride tea germplasm resource data management continues. Generating the root hash and verifying its validity ensures data integrity and accuracy, facilitating data traceability and management. Generating a static code using a semantic version number facilitates differentiation and management of different versions of data. Creating a metadata table to store dynamic parameters makes data management more flexible and comprehensive. Adjusting the sampling plan and triggering the anti-vibration mechanism when abnormal fluoride content is detected allows for timely responses to data anomalies, ensuring the stability and reliability of low-fluoride tea germplasm resource data management.

[0065] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0066] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0067] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0068] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0069] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0070] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A low-fluoride tea germplasm resource data management system, characterized in that, include: Sampling strategy determination module, data quality monitoring module, hash generation and dynamic encoding module; The sampling strategy determination module is used to receive input parameters of the tea tree resources to be managed, determine the environment type according to the preset determination rules, and output a sampling plan corresponding to the environment type, and obtain the original fluoride content of the tea tree resources according to the sampling plan. The data quality monitoring module is used to determine the fluoride ion leaching rate based on the original fluoride content in the sampling strategy determination module, and to determine whether to perform data quality monitoring based on the fluoride ion leaching rate. If data quality monitoring is not performed, the tea tree resource is directly determined as a medium-high fluoride tea tree resource and a preset personnel is notified. Otherwise, the original fluoride content is corrected and the corrected fluoride content is transmitted to the hash generation and dynamic encoding module. The hash generation and dynamic encoding module is used to generate root hashes based on traceability feature hashes and verify the validity of root hashes. It also generates static encodings by combining semantic version numbers and creates a metadata table to store dynamic parameters. If the predicted fluoride content of the tea tree resources to be managed is abnormal, the sampling scheme is adjusted and the anti-oscillation mechanism is triggered. Otherwise, the management of low-fluoride tea tree germplasm resource data continues. The method for determining the environment type according to a preset judgment rule and outputting a sampling scheme corresponding to the environment type is as follows: If the soil total fluoride content is less than the lower limit of soil total fluoride content and the atmospheric fluoride deposition is less than the lower limit of atmospheric fluoride deposition, it is determined to be a low-pollution-risk environment, and the first sampling plan is output. If the soil total fluoride content exceeds the upper limit of soil total fluoride content or the atmospheric fluoride deposition exceeds the upper limit of atmospheric fluoride deposition, it is determined to be a high-pollution-risk environment, and the second sampling plan is output. If neither of the above two conditions is met, it is determined to be a standard balanced environment, and the third sampling scheme is output. The environmental pollution levels corresponding to the first sampling scheme, the third sampling scheme, and the second sampling scheme increase sequentially, and the corresponding sampling frequencies decrease sequentially. The specific method for obtaining the root hash of the tree based on the source feature hash is as follows: The traceability feature hashes include soil management record hashes, atmospheric monitoring hashes, fresh leaf batch hashes, and genome sample hashes; The tea garden plot number and soil exchangeable fluoride content were concatenated into strings, and the result generated by the hash algorithm was recorded as the soil management record hash. The atmospheric monitoring timestamp and atmospheric fluoride deposition amount are concatenated into strings, and the result generated by the hash algorithm is recorded as the atmospheric monitoring hash. The harvest timestamp, the grower's number, and the corrected total fluoride content are concatenated into strings, and the result generated by the hash algorithm is recorded as the fresh leaf batch hash. The genome extraction time and negative control quality control results are concatenated into strings, and the result generated by the hash algorithm is recorded as the genome sample hash. The negative control quality control results are used to determine whether there is exogenous contamination during the genome extraction process.

2. The low-fluoride tea germplasm resource data management system as described in claim 1, characterized in that: The specific process for determining whether to perform data quality monitoring based on the leaching rate of fluoride ions is as follows: The ratio of the total fluoride content of the fresh leaves to the fluoride content extracted by water from the tea tree resources to be managed is recorded as the corresponding leaching rate. If the leaching rate of the fresh leaves of the tea tree resource to be managed is greater than the corresponding leaching rate threshold, no further steps will be taken, and the tea tree resource will be directly identified as a medium-to-high fluoride tea tree resource, and the preset personnel will be notified; otherwise, data quality monitoring will be carried out.

3. The low-fluoride tea germplasm resource data management system as described in claim 2, characterized in that: The specific process for data quality monitoring is as follows: When monitoring tea tree resources under management, the ratio of the standard deviation to the average value of the total fluoride content in the fresh leaves corresponding to the current tea tree resources is calculated and recorded as the coefficient of variation of fluoride content. If the coefficient of variation of fluoride content exceeds the limit for determining whether the data quality is acceptable, the original fluoride content of the collected tea tree resource will be marked as unacceptable, and the designated personnel will be prompted to resample. Otherwise, after synchronously acquiring the calibration parameters, an electrode anomaly is determined. The calibration parameters include the measured fluorine content of the preset standard substance, the measured electrode slope, and the theoretical electrode slope. The electrode anomaly determination method indicates an electrode anomaly if the electrode slope deviation value is greater than the preset electrode slope deviation value limit. Otherwise, after electrode slope optimization, the original fluorine content is corrected. The electrode slope deviation value is obtained by processing the relative deviation between the measured electrode slope and the theoretical electrode slope. The electrode slope optimization specifically involves: When collecting the original fluoride content of fresh leaves corresponding to the tea tree resources to be managed, the temperature of the tea tree fresh leaf extract was recorded simultaneously. The electrode slope was calibrated by using the relationship between the theoretical slope of the Nernst equation and temperature, and the calibrated electrode slope was obtained.

4. The low-fluoride tea germplasm resource data management system as described in claim 3, characterized in that: The specific procedure for correcting the original fluoride content is as follows: The product of the fluorine content ratio and the electrode slope ratio is used as the correction coefficient. The fluorine content ratio represents the ratio of the measured fluorine content of the preset standard substance to the standard fluorine content of the preset standard substance, and the electrode slope ratio represents the ratio of the calibrated electrode slope to the theoretical electrode slope. If the correction coefficient is not within the preset correction coefficient range, the data is marked as pending review and the preset personnel are notified; otherwise, the original fluoride content is corrected. Specifically, the correction coefficient is introduced to correct the original fluoride content of the tea tree resource to obtain the corrected fluoride content. The original fluoride content includes the total fluoride content and the fluoride content extracted from water, and the corrected fluoride content includes the corrected total fluoride content and the corrected fluoride content extracted from water.

5. The low-fluoride tea germplasm resource data management system as described in claim 1, characterized in that: The specific process for verifying the validity of the root hash is as follows: During genome extraction, the genome sample is pre-tested. If the pre-test result is unqualified, i.e. the gene concentration is lower than the gene concentration threshold limited by the gene extraction experiment, or the extracted genome sample is contaminated, the sample is marked as invalid and resampling is prompted, and the process is terminated while waiting for the negative control quality control results. Before obtaining the negative control quality control results, the soil management record hash, atmospheric monitoring hash, and fresh leaf batch hash were concatenated in sequence, a hash algorithm was used to generate a temporary hash value, and it was marked as pending genome verification. If the corrected total fluoride content in the fresh leaf batch hash is greater than the total fluoride content threshold, the negative control quality control result will be directly recorded as abnormal, the hash will be marked as invalid, and a resampling prompt will be given. If the negative control quality control result fails, the hash is marked as invalid; otherwise, the soil management record hash, atmospheric monitoring hash, fresh leaf batch hash, and genome sample hash are concatenated in sequence, and a root hash is generated using a hash algorithm.

6. The low-fluoride tea germplasm resource data management system as described in claim 1, characterized in that: The process of generating static code by combining semantic version numbers and creating a metadata table to store dynamic parameters is as follows: The structure of the static coding includes: germplasm resource number, low-fluoride type marker, resource number, and propagation and testing version number, wherein the propagation and testing version number includes a major version number and a minor version number; The dynamic parameters include soil fluoride background value, atmospheric fluoride background value and leaching rate detection value, and the metadata table is associated with the dynamic parameters. If a change in the total fluoride content threshold is detected in subsequent low-fluoride tea germplasm resource data management, and the change in the total fluoride content threshold is greater than the preset limit for the change in the total fluoride content threshold, the major version number increment logic is triggered, and the minor version number is reset to zero. The change in the total fluoride content threshold represents the result obtained by performing relative deviation processing on the current total fluoride content threshold and the previous total fluoride content threshold. The total fluoride content threshold is adjusted according to the adjustment of the preset standard. If it is detected that the next generation of seedlings is bred from the tea tree resources to be managed, then the minor version number of the next generation of seedlings of the tea tree resources and the minor version number of the tea tree resources will show an increasing relationship. The major version number of the next generation of seedlings of the tea tree resources will be recorded as the major version number of the tea tree resources.

7. The low-fluoride tea germplasm resource data management system as described in claim 1, characterized in that: The specific process for adjusting the sampling scheme and triggering the anti-oscillation mechanism is as follows: If the predicted total fluoride content of the tea tree resources to be managed is greater than the corresponding total fluoride content threshold, the anti-shake mechanism will be triggered; otherwise, the data management of low-fluoride tea tree germplasm resources will continue. The trigger anti-oscillation mechanism is as follows: if the predicted total fluoride content for the preset period is greater than the corresponding total fluoride content threshold, the sampling scheme is adjusted; otherwise, the management of low-fluoride tea germplasm resource data continues. The adjusted sampling scheme is specifically as follows: The sampling leaf position for the corresponding tea tree resources was adjusted to only collect single buds, and the sampling frequency was adjusted to once a week. During the process of adjusting the sampling scheme, the fluoride content of the corresponding tea tree resources is monitored. If the total fluoride content monitored for a preset number of times is lower than the value corresponding to the preset proportion of the total fluoride content threshold, the environment type is re-determined according to the preset judgment rules, and the sampling scheme corresponding to the environment type is output.

8. A method applied to a low-fluoride tea germplasm resource data management system according to any one of claims 1-7, characterized in that, include: S1, receive input parameters of the tea tree resources to be managed, determine the environment type according to the preset judgment rules, and output the sampling scheme corresponding to the environment type, and obtain the original fluoride content of the tea tree resources according to the sampling scheme; S2. Based on the obtained original fluoride content, the leaching rate of fluoride ions is obtained, and based on the leaching rate of fluoride ions, it is determined whether data quality monitoring should be performed. If data quality monitoring is not performed, the tea tree resource is directly identified as a medium-high fluoride tea tree resource, and the preset personnel are notified. Otherwise, the original fluoride content is corrected to obtain the corrected fluoride content. S3. Based on the correction of fluoride content, a root hash is generated using a four-level hash system including soil management record hash, atmospheric monitoring hash, fresh leaf batch hash, and gene sample hash. The validity of the root hash is verified. A static code is generated by combining the semantic version number, and a metadata table is created to store dynamic parameters. If an abnormal fluoride content in fresh tea leaves is detected in subsequent low-fluoride tea germplasm resource data management, the sampling plan is adjusted and an anti-oscillation mechanism is triggered. Otherwise, the low-fluoride tea germplasm resource data management continues.

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