A genetically modified corn component analysis system based on big data analysis

Abstract

The invention discloses a component analysis system of genetically modified corn based on big data analysis, relates to the technical field of big data analysis, comprises a management center, wherein the management center is connected with a crop collection module, an element processing module, an intelligent inspection module and a component analysis module; material preprocessing is performed on a genetically modified comprehensive sample to obtain a material extraction sample, element criteria are set to perform fragment detection on the material extraction sample to obtain sample nutrient content information; stage classification is performed on the sample nutrient content information to obtain stage sample nutrient content information, and a position coefficient fluctuation diagram is constructed; a moving judgment axis is set to perform sliding judgment on the position coefficient fluctuation diagram to obtain a content relationship result, signal-to-graph conversion and association screening are performed on the sample nutrient content information to obtain abnormal relationship components; a large amount of data is quickly processed and analyzed, the component analysis time is greatly shortened, and the accuracy and reliability of the component analysis result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a component analysis system of genetically modified corn based on big data analysis. Background Art

[0002] Corn is one of the most important food crops in the world, and its yield and quality are crucial to ensuring food security. With the development of biotechnology, the research and application of genetically modified corn are becoming more and more extensive.

[0003] Genetically modified corn introduces foreign genes to improve its disease resistance, insect resistance and stress tolerance, but the impact of changes in its composition on food safety and nutritional quality needs to be strictly evaluated, so it is necessary to analyze the composition of genetically modified corn to ensure the safety and nutritional value of genetically modified corn. Traditional methods mainly rely on laboratory testing and chemical analysis, which are often time-consuming, costly, and difficult to process large amounts of data. For this reason, a genetically modified corn composition analysis system based on big data analysis is now provided. By using big data analysis technology to process complex data sets, the composition of genetically modified corn can be analyzed quickly and accurately. Summary of the invention

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A component analysis system for transgenic corn based on big data analysis includes a management center, wherein the management center is connected to a crop collection module, an element processing module, an intelligent inspection module, and a component analysis module;

[0006] The process of the crop collection module collecting transgenic standard data and transgenic comprehensive samples includes:

[0007] Set up a basic collection terminal to collect technical information and obtain GMO standard data;

[0008] Setting up a sampling plan, capturing and limiting the sampling area according to the sampling plan, and obtaining sampled corn plants;

[0009] A multi-source capture terminal is set up to directly collect sampled corn plants to obtain comprehensive transgenic samples.

[0010] The process of obtaining an extraction sample of a substance involves:

[0011] Segmentally summarize the transgenic comprehensive samples according to the obtained sampling plan to obtain a comprehensive sample site group, wherein the comprehensive sample site group includes samples of the same site;

[0012] Setting a growth cycle according to the obtained sampling plan, periodically sorting the comprehensive sample position group according to the growth cycle, and obtaining a sample position time series;

[0013] The material pretreatment of the comprehensive transgenic samples is carried out based on the time series of the sample positions to obtain material extraction samples.

[0014] The process of obtaining the sample nutrient information includes:

[0015] Set element criteria for material extraction samples based on the obtained GMO standard data;

[0016] The element criteria include food safety criteria and nutritional quality criteria;

[0017] Conduct safety fragment testing on the material extraction samples according to the obtained food safety guidelines to obtain food safety information of the samples;

[0018] Perform nutritional fragment testing on the material extraction samples according to the obtained nutritional quality criteria to obtain the nutritional content information of the samples;

[0019] The obtained sample food safety information and sample nutritional information are marked as sample nutritional information.

[0020] The process of obtaining the site coefficient fluctuation graph includes:

[0021] Based on the growth cycle, the obtained sample nutrient information is classified into stages according to the obtained sample part time series to obtain the stage sample nutrient information;

[0022] The nutritional information of the stage samples is separated pair by pair to obtain the paired difference factors;

[0023] Matching processing is performed according to the obtained paired difference factors to obtain the position-stage correlation coefficient;

[0024] The obtained part-stage correlation coefficients are constructed by stage combination based on the order of the growth cycle to obtain a part coefficient fluctuation diagram, and the part coefficient fluctuation diagram is marked with curves to obtain a correlation component change line.

[0025] The process of setting the moving determination axis of the component analysis module to perform sliding determination on the part coefficient fluctuation diagram includes:

[0026] A moving threshold is set for the obtained moving determination axis, and the moving determination axis is translated and slid on the part coefficient fluctuation diagram based on the moving threshold until the moving determination axis reaches the maximum value of the part coefficient fluctuation diagram, thereby obtaining a maximum determination axis coefficient;

[0027] Based on the maximum determination axis coefficient, the wave bands of the site coefficient fluctuation diagram are classified according to the moving determination axis to obtain the content relationship results;

[0028] The content relationship results include same-direction content relationships, reverse-direction content relationships, and undirected relationships.

[0029] The process of classifying the bands of the part coefficient fluctuation diagram according to the moving judgment axis based on the maximum judgment axis coefficient includes:

[0030] When the correlation component change line falls between the maximum determination axis coefficient and the moving determination axis, the sample nutritional information corresponding to the correlation component change line is marked as a correlation content result, and the correlation content result includes an inverse content relationship and a same-direction content relationship;

[0031] When the correlation component change line is not between the maximum determination axis coefficient and the moving determination axis, the sample nutritional information corresponding to the correlation component change line is marked as an undirected relationship.

[0032] The process of obtaining the abnormal relationship components includes:

[0033] Based on the sample part time series and the part coefficient fluctuation diagram, the sample nutrient information is converted into a signal-to-graph diagram to obtain a sample change curve diagram;

[0034] Check the sample change curve according to the GMO standard data to obtain abnormal component information segments;

[0035] According to the obtained content relationship results, the abnormal component information segments are associated and screened to obtain abnormal relationship components.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] Collect comprehensive data of genetically modified corn, extract the comprehensive data, obtain the component information of the collected samples, classify the component information by stages, obtain the nutritional information of the stage samples, perform pairwise deviation on the nutritional information of the stage samples and perform stage combination construction to obtain the position coefficient fluctuation diagram; perform big data analysis on the extracted component information to generate a curve chart form that is easy to observe, which is convenient for rapid processing and helps to significantly shorten the component analysis time;

[0038] A moving judgment axis is set to perform sliding judgment on the position coefficient fluctuation diagram to obtain the content relationship result, and the sample nutritional information is converted into a graph and associated screened to obtain abnormal relationship components; obtaining abnormal content values ​​of different components is helpful to evaluate the food safety and nutritional quality of genetically modified corn, and according to the changing influence relationship between different components, it is beneficial to improve the processing characteristics of genetically modified corn. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, a component analysis system of genetically modified corn based on big data analysis includes a management center, and the management center is connected to a crop collection module, an element processing module, an intelligent inspection module and a component analysis module;

[0043] The crop collection module is used to collect GMO standard data and GMO comprehensive samples, and the specific process includes:

[0044] Set up a basic collection terminal to collect technical information and obtain GMO standard data;

[0045] Furthermore, the technical data indicates the operation specifications for genetically modified crops and the food safety regulations that genetically modified products need to follow and the nutritional quality standards that they should meet. The food safety regulations and nutritional quality standards are the genetically modified standard data, wherein the food safety regulations include the standards for genetically modified ingredients, pesticide residues, mycotoxins, heavy metal content, and microbial contamination, that is, the standards for various ingredients that meet the food safety range. The nutritional quality standards include protein content, amino acid composition, fat content, vitamin content, mineral content, and other nutrients, and the nutritional quality standards are also standard contents set under the most reasonable and healthiest conditions.

[0046] Setting a sampling plan, which includes sampling areas, sampling points, and sample quantities;

[0047] Further, the sampling area represents the range of the selected planting plot for planting genetically modified corn, the sampling points represent the sampling positions selected at different growth stages on the genetically modified corn plants, the different growth stages include jointing stage, tasseling stage, and maturity stage, the sampling positions include grains, leaves, stems, roots, inflorescences, stalks, leaf sheaths, and cobs; the sampling points include grains, leaves, stems, roots, inflorescences, stalks, leaf sheaths, and cobs at different growth stages, i.e., grains, leaves, stems, roots, inflorescences, stalks, leaf sheaths, and cobs at the jointing stage, grains, leaves, stems, roots, inflorescences, stalks, leaf sheaths, and cobs at the tasseling stage, and grains, leaves, stems, roots, inflorescences, stalks, leaf sheaths, and cobs at the maturity stage, the number of samples represents the number of sampling points selected in the sampling area, and the sampling plan is to uniformly and randomly select the collection objects to ensure the representativeness of the analysis of genetically modified corn components;

[0048] According to the obtained sampling plan, the sampling area is captured and limited to obtain sampled corn plants, wherein the capture and limitation means that according to the sampling plan, transgenic corn plants at different growth stages are selected in different sampling areas, and the sampling point positions are marked on the selected transgenic corn plants, namely, the sampled corn plants;

[0049] A multi-source capture terminal is provided, and samples are directly collected from sampled corn plants through the multi-source capture terminal to obtain a comprehensive transgenic sample, wherein the direct collection means collecting samples from kernels, leaves, stems, roots, inflorescences, stalks, leaf sheaths and cobs of the sampled corn plants at different growth times;

[0050] In particular, the collected transgenic comprehensive samples are collected from sampling parts of the sampled corn plants at different time points at different growth stages, and the obtained transgenic comprehensive samples have corresponding collection time points, and the collection time points can be compared with the growth stage of the transgenic corn;

[0051] According to the obtained sampling plan, the obtained transgenic integrated samples are marked to obtain the corn sampling positions;

[0052] The position mark indicates marking the sampling area corresponding to the sampled corn plant and the corresponding position information in the sampling area, associating the obtained sampled corn plant with the corresponding position information, and associating the obtained transgenic comprehensive sample with the corresponding corn sampling position, that is, each transgenic comprehensive sample has a corresponding corn sampling position.

[0053] The element processing module is used to perform material preprocessing on the genetically modified comprehensive sample, obtain material extraction samples, set element criteria to perform fragment detection on the material extraction samples, and obtain sample nutrient information. The specific process includes:

[0054] Segmentally summarize the obtained transgenic comprehensive samples according to the obtained sampling plan to obtain a comprehensive sample site group, wherein the comprehensive sample site group includes samples of the same site;

[0055] The segment summary means summarizing the transgenic comprehensive samples of the same sampling site according to the sampling site corresponding to the sampling point, recording them as samples of the same site, and combining the samples of the same site of the same category to obtain a comprehensive sample site group; for example, if the comprehensive sample site group is leaves, then all the comprehensive sample sites in the comprehensive sample site group are transgenic comprehensive samples corresponding to the leaves of transgenic corn plants;

[0056] Setting a growth cycle according to the obtained sampling plan, wherein the growth cycle includes an early stage, a middle stage, and a late stage;

[0057] The growth cycle represents the different growth cycles obtained according to the sampling points of the sampling plan, and the early stage, middle stage and late stage are obtained according to the jointing stage, tasseling stage and maturity stage corresponding to the growth cycle, that is, the early stage represents the jointing stage, the middle stage represents the tasseling stage and the late stage represents the maturity stage;

[0058] Periodically sorting the obtained comprehensive sample part groups according to the obtained growth cycles to obtain sample part time series, and associating the obtained sample part time series with the corresponding comprehensive sample part groups;

[0059] Furthermore, the cycle sorting means sorting the samples of the same part contained in the comprehensive sample part group according to the order of the growth cycle, and obtaining the order of the growth cycle of the transgenic comprehensive samples of the same part of different samples, that is, the sample part time series; that is, arranged according to the jointing stage, tasseling stage, and maturity stage, in each growth cycle, the kernels, leaves, stems, roots, inflorescences, stalks, leaf sheaths and cobs corresponding to different sampled corn plants or market corn samples are included, and the corresponding comprehensive sample part group contains all the transgenic comprehensive samples corresponding to the part;

[0060] Performing material pretreatment on the obtained transgenic comprehensive samples based on the sample site time series to obtain material extraction samples;

[0061] Furthermore, the material pretreatment means that the collected transgenic comprehensive samples are pretreated by existing technology to become a material form that is easy to extract and analyze, and the order of material pretreatment of the transgenic comprehensive samples is carried out according to the time sequence of the sample parts, that is, according to the order of jointing stage, tasseling stage, and maturity stage, the transgenic comprehensive samples of different parts of each growth cycle are processed accordingly; for example, by drying, grinding and mixing the collected fresh transgenic comprehensive samples, the accuracy of subsequent analysis is facilitated;

[0062] Set element criteria for material extraction samples based on the obtained GMO standard data;

[0063] The element criteria include food safety criteria and nutritional quality criteria, that is, the categories of ingredients for which material extraction samples need to be collected. According to the standards for genetically modified ingredients, pesticide residues, mycotoxins, heavy metal content, and microbial contamination included in the Food Safety Regulations, the food safety criteria include genetically modified ingredients, pesticide residues, mycotoxins, heavy metal content, and microbial content, and the nutritional quality criteria include protein content, amino acid composition, fat content, vitamin content, mineral content, and other nutrient content;

[0064] Performing safety fragment detection on the substance extraction sample according to the obtained food safety criteria to obtain sample food safety information, and associating the obtained sample food safety information with the corresponding substance extraction sample;

[0065] The safety fragment detection means that the genetically modified ingredients, pesticide residues, mycotoxins, heavy metal content and microbial content in the material extraction sample are detected by detection technology to obtain the food safety information of the sample, and the food safety information of the sample includes genetically modified ingredient information, pesticide residue information, mycotoxin information, heavy metal content information and microbial content information, wherein the detection technology includes using PCR technology to detect genetically modified ingredient information in the material extraction sample, using GC-MS technology to detect pesticide residue information in the material extraction sample, using ELISA technology to detect mycotoxin information in the material extraction sample, using atomic absorption spectroscopy (AAS) technology to detect heavy metal content information in the material extraction sample, and using plate count to detect microbial content information in the material extraction sample;

[0066] Performing nutritional fragment detection on the substance extraction sample according to the obtained nutritional quality criteria to obtain the sample nutritional content information, and associating the obtained sample nutritional content information with the corresponding substance extraction sample;

[0067] Further, the nutritional fragment detection means extracting the protein content, amino acid composition, fat content, vitamin content, mineral content and other nutritional content in the material extraction sample by nutritional analysis technology to obtain the sample nutritional information, and the sample nutritional information includes protein content information, amino acid composition information, fat content information, vitamin content information, mineral content information and other nutritional content information, wherein the nutritional analysis technology includes using Kjeldahl nitrogen determination method, near infrared spectroscopy analysis or high performance liquid chromatography (HPLC) to detect the material extraction sample to obtain the corresponding sample nutritional information;

[0068] Marking the obtained sample food safety information and sample nutritional information as sample nutritional information;

[0069] Furthermore, the sample nutrient intake information is obtained based on the material extraction samples. Since the material extraction samples are obtained by material pretreatment of the transgenic comprehensive samples according to the order of the sample part time series, the obtained sample nutrient intake information is also the same as the order of the corresponding sample part time series, and the transgenic component information, pesticide residue information, mycotoxin information, heavy metal content information, microbial content information, protein content information, amino acid composition information, fat content information, vitamin content information, mineral content information and other nutrient content information included in the sample nutrient intake information all represent each component.

[0070] The intelligent inspection module is used to classify the sample nutritional information by stage, obtain the sample nutritional information by stage, and construct a part coefficient fluctuation diagram. The specific process includes:

[0071] Based on the growth cycle, the obtained sample nutrient information is classified into stages according to the obtained sample part time series to obtain the stage sample nutrient information;

[0072] The stage classification means that according to the arrangement order of the sample nutrient information corresponding to the sample part time series, the sample nutrient information corresponding to the early stage, the sample nutrient information corresponding to the middle stage, and the sample nutrient information corresponding to the late stage are divided, and the sample nutrient information of the three stages obtained is recorded as the stage sample nutrient information;

[0073] The nutritional information of the stage samples is separated pair by pair to obtain the paired difference factors;

[0074] The obtained paired difference factor is labeled ,in, , m represents the sampling site number, then m is a grain, leaf, stem, root, inflorescence, stalk, leaf sheath or cob, and m represents the sampling site collected at the same time in the same growth cycle, k represents the number of the sampled corn plant, k=1, 2, 3, ..., v1, v1 is a positive integer, Indicates that one component is selected from the sample nutrient information and recorded as A. The sample nutrient information corresponding to each sampled corn plant is , and select one component from the remaining components of the sample nutritional information and record it as B, then represents the Kth sampled corn plant of component B, express The average value of express For example, if 50 transgenic samples of corn plants are collected in the middle of the stage, there will be 50 samples of nutrient information, then k=50, and the different values ​​corresponding to these 50 values ​​will be calculated. and The paired difference factor of

[0075] Furthermore, the pairwise deviation means that the pairing difference factor between any two components of the sample nutritional information is calculated in the early stage, the middle stage and the late stage, until all components in the sample nutritional information are deviated, that is, the pairing difference factor corresponding to the stage sample nutritional information;

[0076] Matching processing is performed according to the obtained paired difference factors to obtain the position-stage correlation coefficient;

[0077] The obtained position-phase correlation coefficient is marked as ,in, , g represents the growth cycle, g = early stage, middle stage or late stage, represents the mth sampling site in the gth growth cycle;

[0078] Furthermore, since the paired difference factor is obtained based on any two components of the sample nutrient information, the position-stage correlation coefficient is also composed of any two components of the sample nutrient information of different sampled corn plants at the same sampling position in the same growth period. At the same time, since the transgenic comprehensive samples have corresponding collection time points, each position-stage correlation coefficient corresponds to a collection time point, that is, the time when the sampled corn plants are directly collected. The position-stage correlation coefficient of the sample nutrient information corresponding to all sampled corn plants at the same sampling position at the same collection time point is also calculated.

[0079] Based on the order of the growth cycle, the obtained site phase correlation coefficients are constructed by stage combination to obtain a site coefficient fluctuation diagram;

[0080] It should be further explained that, in the specific implementation process, the process of the stage combination construction includes:

[0081] A two-dimensional rectangular coordinate system of the sampling site with respect to the site-stage correlation coefficient is constructed, wherein the horizontal axis represents the growth cycle, and the vertical axis represents the site-stage correlation coefficient. A two-dimensional rectangular coordinate system is based on the early stage, the middle stage, and the late stage included in the growth cycle. Then, the horizontal axis of the site coefficient fluctuation diagram is a continuous time axis according to the early stage, the middle stage, and the late stage. The points on the horizontal axis represent the sampling time points, and there is a corresponding site-stage correlation coefficient at each sampling time point.

[0082] Generate a correlation component change line according to the obtained part-stage correlation coefficient, upload the obtained correlation component change line to a two-dimensional rectangular coordinate system, and obtain a part coefficient fluctuation diagram;

[0083] Furthermore, the site-stage correlation coefficient of each sampling site corresponds to a site coefficient fluctuation diagram.

[0084] The component analysis module is used to set the moving judgment axis to slide the site coefficient fluctuation diagram to obtain the content relationship result, perform signal-to-diagram conversion and association screening on the sample nutritional information, and obtain abnormal relationship components. The specific process includes:

[0085] Setting a moving determination axis, the moving determination axis includes an upper determination axis and a lower determination axis, uploading the obtained moving determination axis to the part coefficient fluctuation diagram, the upper determination axis is located above the lower determination axis, and the moving determination axes are parallel to the two straight lines of the horizontal axis;

[0086] The sliding judgment is performed on the part coefficient fluctuation diagram by moving the judgment axis to obtain the content relationship result, wherein the content relationship result includes the same direction content relationship, the reverse direction content relationship and the undirected relationship;

[0087] Furthermore, the sliding judgment process includes:

[0088] A moving threshold is set for the obtained moving determination axis, wherein the moving threshold represents the interval distance between the upper determination axis and the lower determination axis, and the position spacing of the moving determination axis is determined according to the moving threshold;

[0089] Based on the moving threshold, the moving determination axis is translated and slid in the part coefficient fluctuation diagram until the moving determination axis reaches the maximum value of the correlation component change line in the part coefficient fluctuation diagram, that is, the upper axis is determined to reach the maximum value of the correlation component change line, and the maximum determination axis coefficient is obtained;

[0090] Based on the maximum determination axis coefficient, the part coefficient fluctuation diagram is classified into bands according to the obtained moving determination axis to obtain the content relationship result;

[0091] Furthermore, the band classification process includes:

[0092] When the correlation component change line falls between the maximum determination axis coefficient and the determination lower axis, the relationship between the two components in the sample nutritional information corresponding to the correlation component change line is marked as a correlation content result, and the correlation content result includes a reverse content relationship and a same-direction content relationship;

[0093] If the correlation component change line is located in the fourth quadrant in the site coefficient fluctuation diagram, that is, the site stage correlation coefficients corresponding to the correlation component change line are all less than zero, then the correlation component change line is marked as a reverse content relationship, indicating that the relationship between the two components in the sample nutritional information corresponding to the correlation component change line satisfies that when the content of one component increases, the content of the other component decreases;

[0094] If the correlation component change line is located in the first quadrant in the site coefficient fluctuation diagram, that is, the site stage correlation coefficients corresponding to the correlation component change line are all greater than zero, then the correlation component change line is marked as a same-direction content relationship, indicating that the relationship between the two components in the sample nutritional information corresponding to the correlation component change line satisfies that when the content of one component increases, the content of the other component also increases, that is, the change directions of the two components are consistent;

[0095] When the correlation component change line is not between the maximum determination axis coefficient and the determination lower axis, the relationship between the two components in the sample nutritional information corresponding to the correlation component change line is marked as an undirected relationship, indicating that there is no influence relationship between the two components in the sample nutritional information corresponding to the correlation component change line, and the change of one component will not cause the change of another component;

[0096] In particular, if the correlation component change line falls on the moving determination axis, that is, has an intersection with the moving determination axis, the correlation component change line is also marked as a correlation content result;

[0097] Based on the sample part time series and the part coefficient fluctuation diagram, the obtained sample nutrient information is converted into a signal-to-graph diagram to obtain a sample change curve diagram;

[0098] Further, the signal-to-graph conversion represents constructing a two-dimensional rectangular coordinate system by using the sample nutrient content information in the time sequence of the sample part time series and the part coefficient fluctuation graph as the coordinate axis, with the collection time point as the horizontal axis and the vertical axis representing the content value of each component in the sample nutrient content information, generating a sample food safety curve according to the sample nutrient content information, uploading the obtained sample food safety curve to the two-dimensional rectangular coordinate system, and obtaining a sample change curve graph, and each sample change curve graph represents all sample nutrient content information of a sampled corn plant within the growth cycle, and each sampling part includes the sample food safety curve corresponding to all sample nutrient content information, and the horizontal axis is the same as the horizontal axis of the part coefficient fluctuation graph, and both meet the order of the growth cycle;

[0099] Performing component inspection on the obtained sample change curve chart according to the obtained GMO standard data to obtain abnormal component information segments;

[0100] Further, the ingredient inspection means that the safety range of the sample food safety curve is determined according to the food safety regulations and nutritional quality standards included in the collected GMO standard data. When the sample food safety curve meets the safety range of the food safety regulations or the safety range of the nutritional quality standards, it is recorded as a qualified ingredient information segment. Otherwise, the sample food safety curve is recorded as an abnormal ingredient information segment, wherein the safety range determination is a determination standard set according to the safety range of each ingredient included in the food safety regulations and nutritional quality standards.

[0101] In particular, the abnormal component information segment indicates that the component of the genetically modified corn is abnormal and exceeds the safety range corresponding to the genetically modified standard data;

[0102] According to the obtained content relationship results, the abnormal component information segments are associated and screened to obtain abnormal relationship components;

[0103] Furthermore, the association screening process includes:

[0104] Obtain the sample nutritional information corresponding to the abnormal ingredient information segment, recorded as abnormal food safety information, obtain the site coefficient fluctuation diagram corresponding to the abnormal food safety information, recorded as abnormal correlation fluctuation diagram, that is, the site coefficient fluctuation diagram contains abnormal food safety information, and the collection time point and growth cycle are the same;

[0105] Obtaining a content relationship result corresponding to the abnormal correlation fluctuation diagram, performing an abnormal analysis on the abnormal correlation fluctuation diagram according to the obtained content relationship result, and obtaining an abnormal relationship component, wherein the abnormal relationship component includes an associated action component relationship and an irrelevant action component relationship;

[0106] Furthermore, the abnormal analysis means analyzing the influence and action relationship between abnormal components in the part coefficient fluctuation diagram;

[0107] When the content relationship result is a correlated content result, it means that the change of the abnormal food safety information will cause the change of the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram, and the abnormal food safety information and the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram are marked as a correlation component relationship, and the direction of the influence of the change of the abnormal food safety information on the change of the nutritional information of another sample is determined according to the correlated content result;

[0108] When the direction of influence is a unidirectional content relationship, it means that the increase of abnormal food safety information will cause the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram to continue to increase, or the decrease of abnormal food safety information will cause the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram to continue to decrease, that is, the abnormal component of the sampling site of the genetically modified corn corresponding to the abnormal food safety information will affect the change of another component. If the nutritional information of the other sample is also abnormal food safety information, it means that the nutritional information of these two samples is not the nutritional information of qualified samples within the safety range. These two abnormal food safety information need to be marked and uploaded to the management center, and the management center records the sampling area, sampling site, collection time point and corn sampling position corresponding to the abnormal food safety information;

[0109] When the direction of influence is an inverse content relationship, it means that the increase of abnormal food safety information will cause the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram to continue to decrease, or the decrease of abnormal food safety information will cause the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram to continue to increase, that is, the abnormal component of the sampling part of the genetically modified corn corresponding to the abnormal food safety information will also affect the change of another component, but the influence relationship is not the same, so it is necessary to screen the abnormal food safety information and the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram to obtain the optimal content value of the nutritional information of another sample corresponding to the abnormal food safety information, wherein screening means obtaining the optimal content value corresponding to the nutritional information of another sample when the abnormal food safety information changes according to the variation range of the inverse content relationship and the genetically modified standard data;

[0110] When the content relationship result is an undirected relationship, it means that the change of abnormal food safety information will not affect the nutritional information of another sample corresponding to the abnormal correlation fluctuation diagram, that is, the abnormal food safety information and the nutritional information of another sample are unrelated component relationships. In this case, the abnormal food safety information will be uploaded to the management center, and the management center will record the sampling area, sampling site, collection time point and corn sampling location corresponding to the abnormal food safety information.

[0111] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A genetically modified corn component analysis system based on big data analysis, including a management center, characterized in that: The management center is connected to a crop collection module, an element processing module, an intelligent inspection module and a component analysis module; The crop collection module is used to collect transgenic standard data and transgenic comprehensive samples; The element processing module is used to perform segment induction and period sorting on the transgenic comprehensive sample according to the sampling plan, obtain the sample part time series, perform material pretreatment on the transgenic comprehensive sample based on the sample part time series, obtain the material extraction sample, set the element criteria to perform fragment detection on the material extraction sample, and obtain the sample nutrient content information; The intelligent inspection module is used to classify the sample nutritional information by stage, obtain the stage sample nutritional information, perform pairwise deviation on the stage sample nutritional information and perform stage combination construction to obtain the position coefficient fluctuation diagram; The process of obtaining the site coefficient fluctuation graph includes: Based on the growth cycle, the obtained sample nutrient information is classified into stages according to the obtained sample part time series to obtain the stage sample nutrient information; The nutritional information of the stage samples is separated pair by pair to obtain the paired difference factors; The obtained paired difference factor is labeled as PD m ,in, , m represents the sampling site number, k represents the number of the sampled corn plant, A k Indicates that any one component in the sample nutrient information is recorded as A, and the sample nutrient information corresponding to each sampled corn plant is A k , and select one component from the remaining components of the sample nutritional information and record it as B, then B k represents the Kth sampled corn plant of component B, Indicates A k The average value of Indicates B k The average value of Matching processing is performed according to the obtained paired difference factors to obtain the position-phase correlation coefficient; The obtained position-phase correlation coefficient is marked as ,in, , g represents the growth period, represents the mth sampling site in the gth growth cycle; Based on the order of the growth cycle, the obtained part-stage correlation coefficients are constructed in stages to obtain a part coefficient fluctuation diagram, and the part coefficient fluctuation diagram is marked with a curve to obtain a correlation component change line; The component analysis module is used to set a moving judgment axis to perform sliding judgment on the site coefficient fluctuation diagram, obtain content relationship results, perform signal-to-graph conversion on the sample nutritional information, obtain a sample change curve diagram, perform component inspection and correlation screening on the sample change curve diagram through transgenic standard data, and obtain abnormal relationship components.

2. The component analysis system of transgenic corn based on big data analysis according to claim 1, characterized in that: The process of the crop collection module collecting transgenic standard data and transgenic comprehensive samples includes: Set up a basic collection terminal to collect technical information and obtain GMO standard data; Setting up a sampling plan, capturing and limiting the sampling area according to the sampling plan, and obtaining sampled corn plants; A multi-source capture terminal is set up to directly collect sampled corn plants to obtain comprehensive transgenic samples.

3. The component analysis system of transgenic corn based on big data analysis according to claim 2, characterized in that: The process of obtaining an extraction sample of a substance involves: Segmentally summarize the transgenic comprehensive samples according to the obtained sampling plan to obtain a comprehensive sample site group, wherein the comprehensive sample site group includes samples of the same site; Setting a growth cycle according to the obtained sampling plan, periodically sorting the comprehensive sample position group according to the growth cycle, and obtaining a sample position time series; The material pretreatment of the comprehensive transgenic samples is carried out based on the time series of the sample positions to obtain material extraction samples.

4. The component analysis system of transgenic corn based on big data analysis according to claim 3, characterized in that: The process of obtaining the sample nutrient information includes: Set element criteria for material extraction samples based on the obtained GMO standard data; The element criteria include food safety criteria and nutritional quality criteria; Conduct safety fragment testing on the material extraction samples according to the obtained food safety guidelines to obtain food safety information of the samples; Perform nutritional fragment testing on the material extraction samples according to the obtained nutritional quality criteria to obtain the nutritional content information of the samples; The obtained sample food safety information and sample nutritional information are marked as sample nutritional information.

5. The component analysis system of transgenic corn based on big data analysis according to claim 4, characterized in that: The process of setting the moving determination axis of the component analysis module to perform sliding determination on the part coefficient fluctuation diagram includes: A moving threshold is set for the obtained moving determination axis, and the moving determination axis is translated and slid on the part coefficient fluctuation diagram based on the moving threshold until the moving determination axis reaches the maximum value of the part coefficient fluctuation diagram, thereby obtaining a maximum determination axis coefficient; Based on the maximum determination axis coefficient, the wave bands of the site coefficient fluctuation diagram are classified according to the moving determination axis to obtain the content relationship result; The content relationship results include same-direction content relationships, reverse-direction content relationships, and undirected relationships.

6. The component analysis system of transgenic corn based on big data analysis according to claim 5, characterized in that: The process of classifying the bands of the part coefficient fluctuation diagram according to the moving judgment axis based on the maximum judgment axis coefficient includes: When the correlation component change line falls between the maximum determination axis coefficient and the moving determination axis, the sample nutritional information corresponding to the correlation component change line is marked as a correlation content result, and the correlation content result includes an inverse content relationship and a same-direction content relationship; When the correlation component change line is not between the maximum determination axis coefficient and the moving determination axis, the sample nutritional information corresponding to the correlation component change line is marked as an undirected relationship.

7. The component analysis system of transgenic corn based on big data analysis according to claim 6, characterized in that: The process of obtaining the abnormal relationship components includes: Based on the sample part time series and the part coefficient fluctuation diagram, the sample nutrient information is converted into a signal-to-graph diagram to obtain a sample change curve diagram; Check the sample change curve according to the GMO standard data to obtain abnormal component information segments; According to the obtained content relationship results, the abnormal component information segments are associated and screened to obtain abnormal relationship components.

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