A Cross-Platform Method for Establishing a Global Weed Gene Database

Abstract

The present invention relates to the field of information technology, and provides a method for uniformly constructing a global weed gene database across platforms, including: screening out weed populations with gene exchange characteristics with target crops, using gene alignment methods to identify gene fragments of gene exchange between crops and weeds, and determining the transmission path of resistance genes in interspecific hybridization; according to the gene alignment results, extracting the transmission path data of resistance genes, and combining with the genetic structure characteristics of the weed population, analyzing the impact of the transmission of resistance genes on the genetic structure of the weed population; calculating the frequency distribution of resistance genes in different weed populations, judging the diffusion trend of resistance genes in the weed population according to the frequency distribution, generating a resistance gene diffusion map, and based on the weed population genetic structure change trend map and the resistance gene diffusion map, predicting the future change trend of the weed population genetic structure, uniformly constructing a global weed gene database, which helps to formulate effective weed management strategies.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a method for constructing a cross-platform global weed gene database. Background Art

[0002] Relatives of weeds refer to wild relatives of modern crops and weeds related to crops, including transitional types between cultivated and wild types. In the study of the global weed gene database, it is necessary to integrate weed genomes, functional analysis, and related bioinformatics data to facilitate weed genome research, function prediction, and data analysis. During the integration of weed genomes, there is a phenomenon of gene introgression of relatives of weeds. At this time, it is necessary to focus on the characteristics of gene exchange between crops and weeds. Due to the complex evolutionary relationship between crops and weeds, gene exchange between them may occur at multiple levels, including interspecific hybridization, introgressive hybridization, and transgenic drift. In these processes, resistance genes may be transferred from crops to weed populations, resulting in weeds developing resistance to herbicides. However, the transmission path of resistance genes between different weed populations is still unclear. In addition, the transmission of resistance genes may have a significant impact on the genetic structure of weed populations, leading to a decrease in the genetic diversity of weed populations and a reduction in their evolutionary potential. Therefore, in-depth analysis of the mechanism of reshaping the genetic structure of weed populations by the transmission of resistance genes is of great significance for understanding the evolutionary dynamics of weeds and formulating effective weed management strategies. This requires the comprehensive application of research methods in multiple disciplines such as genomics, population genetics, and evolutionary biology to clarify the characteristics of gene exchange between crops and weeds, reveal the transmission and diffusion mechanisms of resistance genes in weed populations, and the impact of these processes on the genetic structure of weed populations, providing a scientific basis for the sustainable management of global weeds. Summary of the Invention

[0003] The present invention provides a method for constructing a cross-platform global weed gene database, mainly including:

[0004] Screen out the weed populations with gene flow characteristics with the target crops, extract the gene sequence data of related weed species from the global weed gene database, and construct a gene introgression analysis model; use the gene alignment method for the screened weed populations to identify the gene fragments of gene flow between crops and weeds, and determine the transmission path of resistance genes in interspecific hybridization; according to the gene alignment results, extract the transmission path data of resistance genes, combine with the genetic structure characteristics of the weed population, construct a genetic structure change model, and analyze the impact of the transmission of resistance genes on the genetic structure of the weed population; use the population genetics algorithm to calculate the frequency distribution of resistance genes in different weed populations, and judge the diffusion trend of resistance genes in the weed population according to the frequency distribution to generate a resistance gene diffusion map; according to the resistance gene diffusion map, combine with the genetic structure change model of the weed population, analyze the impact of the transmission of resistance genes on the genetic indicators of the reshaping of the genetic structure of the weed population, including the change of allele frequency and the change of linkage disequilibrium degree, and generate a genetic structure reshaping map reflecting the change of genetic indicators; through the genetic structure reshaping map, extract the key nodes of the genetic structure change of the weed population and the corresponding genetic indicators, combine with the gene introgression analysis model, and analyze the impact of gene introgression on the change of the adaptive evolution potential of the weed population and the change of the population genetic diversity level; according to the impact of gene introgression on the genetic structure of the weed population, generate the change trend of the genetic diversity index and the allele frequency of adaptive loci over time, establish a change trend map of the genetic structure of the weed population, combine with the resistance gene diffusion map, and quantitatively analyze the correlation between the transmission of resistance genes and the change of the genetic structure of the weed population; if the correlation is greater than or equal to the preset correlation threshold, then based on the change trend map of the genetic structure of the weed population and the resistance gene diffusion map, construct a prediction model of the genetic structure of the weed population, predict the change trend of the future genetic structure of the weed population, and establish a global weed gene database according to the change trend of the future genetic structure of the weed population.

[0005] The technical solution provided by the embodiment of the present invention may include the following beneficial effects:

[0006] The present invention discloses a method for constructing a cross-platform global weed gene database. The method first screens weed populations with gene flow with the target crop from the global weed gene database and extracts their gene sequence data. By constructing a gene introgression analysis model and a gene alignment algorithm, the fragments of gene flow between the crop and the weed are identified, and the transmission path of the resistance gene is determined. Combining with the population genetics algorithm, the impact of the transmission of the resistance gene on the genetic structure of the weed population is analyzed, and a resistance gene diffusion map and a genetic structure remodeling map are generated. Furthermore, the present invention analyzes the impact of gene introgression on the adaptive evolutionary potential and genetic diversity of the weed population from the perspective of evolutionary genetics, and constructs a trend map of the change in the genetic structure of the weed population. Finally, a machine learning algorithm is used to predict the change trend of the genetic structure of the weed population in the future. The present invention realizes a comprehensive analysis and prediction of the change in the genetic structure of the weed population, providing a scientific basis for formulating effective weed management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flowchart of a method for constructing a cross-platform global weed gene database of the present invention.

[0008] Figure 2 It is a schematic diagram of a method for constructing a cross-platform global weed gene database of the present invention.

[0009] Figure 3 It is another schematic diagram of a method for constructing a cross-platform global weed gene database of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0011] Such as Figures 1-3 , a method for constructing a cross-platform global weed gene database in this embodiment may specifically include:

[0012] Step S101, screening out weed populations with gene flow characteristics with the target crop, extracting gene sequence data of related weed species from the global weed gene database, and constructing a gene introgression analysis model.

[0013] Obtain the genomic data of the weed population in the area where the target crop is located, construct a multi-dimensional feature vector containing genomic similarity and genetic distance according to the genomic data; extract the genomic collinear regions according to the multi-dimensional feature vector, and use the hierarchical clustering algorithm to sort the genomic data to obtain a genomic arrangement map; calculate the genetic distance matrix according to the genomic arrangement map, and judge the gene flow direction through the genomic collinear regions of the genetic distance matrix to obtain the gene exchange frequency; construct a genomic recombination index scoring matrix according to the gene exchange frequency, and calculate the hybridization affinity coefficient through the scoring matrix to obtain the gene introgression risk level; if the gene introgression risk level exceeds the preset threshold, it is determined that there is a gene introgression risk between the target crop and the weed population.

[0014] Specifically, for the genomic data of weed populations collected within the geographical region where the target crop is located, construct a multi-dimensional feature vector including genomic similarity, genetic distance, and genomic coverage. Obtain homologous gene sequences from the global genomic database, and based on the genomic data of related species with a genomic coverage rate greater than a preset threshold and sequence integrity meeting the requirements. Extract the genomic collinear regions from the above homologous gene sequence library, count the sequence repeatability and base substitution rate, obtain the genomic data arrangement map, and use the hierarchical clustering algorithm to sort the genomic data according to the population genetic variation degree value from high to low. Calculate the genetic distance matrix using the sorted genomic data, determine the gene flow direction by the base substitution frequency in the genomic collinear region, quantitatively calculate the gene exchange frequency, and construct a numerical mapping relationship for the homology degree between genomic data. Based on the above genetic distance matrix and gene exchange frequency data, construct a genomic recombination index scoring matrix, and use machine learning methods to predict and model the genomic recombination index. Calculate the hybridization affinity coefficient according to the genomic recombination index scoring matrix, judge the gene introgression risk level by the value of the affinity coefficient, and obtain the gene introgression probability distribution map between the target crop and the weed population. For the data of the gene introgression probability distribution map, establish a gene introgression risk assessment function, and use the regression algorithm to train the risk assessment function to obtain a gene introgression risk prediction model. Gene introgression analysis is of great significance in agricultural ecosystems. By constructing a multi-dimensional feature vector to characterize the genetic characteristics of weed populations, the feature vector includes key indicators such as genomic similarity, genetic distance, and genomic coverage. For example, in the study of gene exchange between rice and wild rice, sequence data with a genomic coverage rate of more than 95% is identified as high-quality data, and these data show high sequence integrity. The homologous gene sequences screened from the global genomic database show obvious collinear conserved blocks at the chromosomal level after collinearity analysis. By analyzing the sequence repeatability and base substitution rate in these conserved blocks, the genetic variation patterns between different populations can be found. For example, in the study of maize and its wild relatives, genomic collinearity analysis shows obvious sequence conserved regions on chromosomes 2, 4, and 7, and the base substitution rate in these regions is less than 0.5%. The calculation of the genetic distance matrix is based on the sequence alignment results in the genomic collinear region, and the directionality of gene flow is revealed by analyzing the base substitution frequency. In the hybridization study of wheat and Aegilops tauschii, it is found that the gene exchange frequency reaches a peak during the flowering period, among which pollen-mediated gene flow dominates, and the gene exchange frequency can reach 12%. The genomic recombination index scoring matrix reflects the potential of gene exchange between different species, and this index shows a significant positive correlation with hybridization affinity. For example, in the study of rapeseed and its wild relatives, populations with a recombination index exceeding 0.8 show high hybridization affinity, and their hybridization seed setting rate can reach more than 25%.The introgression risk assessment function integrates multiple ecological factors, including the spatial distribution of populations, the overlap degree of flowering phenology, etc. In the symbiotic area of cultivated soybean and wild soybean, analysis shows that when the overlap degree of the flowering periods of the two species exceeds 60%, the introgression risk increases significantly. The accuracy rate of the risk prediction model can reach 85%, providing an important reference for the ecological safety assessment of crop planting areas. Among them, the introgression risk levels are classified as low risk with a risk index less than 0.3, medium risk with a risk index between 0.3 and 0.7, and high risk with a risk index greater than 0.7. By constructing an introgression probability distribution map, the introgression risk distribution pattern in different geographical regions is intuitively displayed, providing a scientific basis for formulating corresponding risk prevention and control measures.

[0015] Step S102: Use the gene alignment method to identify the gene fragments of gene exchange between crops and weeds for the selected weed populations, and determine the transmission path of resistance genes in interspecific hybridization.

[0016] Use the double-sequence local alignment method to perform sequence alignment on the genomic data of crops and weeds, calculate the sequence similarity score of gene fragments according to the alignment results, and obtain the target gene fragments with a sequence similarity score higher than the preset threshold; extract the coding region sequence for the target gene fragments, calculate the gene expression level according to the transcriptome sequencing data of the coding region sequence, and calculate the genetic stability index through the gene expression level and the gene copy number; construct a time-series expression profile based on the genetic stability index and RNA sequencing data, and use the gradient descent method to fit the time-series expression profile to obtain gene transfer path data; construct a gene interaction network for the gene transfer path data, obtain the key node sequences of gene transmission through graph structure algorithms, and determine the action sites of resistance genes according to the comparison results of the key node sequences and the phenotype database, forming a transmission path map of resistance genes in the process of interspecific hybridization.

[0017] Specifically, sequence alignment is performed based on the genomic data of crops and weeds. The double-sequence local alignment method is used to divide the gene fragment length intervals. The sequence similarity score is calculated for the base composition within the gene fragments. Target gene fragments with sequence similarity scores higher than a preset threshold are screened out from the whole genome range. The coding region sequences in the target gene fragments are extracted, and the gene expression levels are calculated using transcriptome sequencing data. The genetic stability index is calculated based on the gene copy number and gene mutation frequency values. The gene recombination breakpoint positions are determined by comparing the sequencing depth data. RNA sequencing data is used to obtain the transcriptome expression data of the target gene fragments. A time-series expression profile is constructed for the transcriptome expression data. The gradient descent method is used to fit the gene expression regulation data to obtain the preliminary direction of the gene transfer path. A gene interaction network is constructed based on the preliminary gene transfer direction data. The graph structure algorithm is used to analyze the connectivity of the gene interaction network to obtain the key node sequences of gene transmission. Phenotypic association analysis is performed on the key node sequences of gene transmission. The action sites of resistance genes are determined by comparing with the phenotype database, and a transmission pathway map of resistance genes during interspecific hybridization is constructed. The neural network model is trained using the transmission pathway map data to predict the transmission probability of resistance genes in different hybridization combinations, and the optimal path combination of resistance gene transmission is obtained. In the study of gene flow between crops and weeds, sequence alignment is the basic step for identifying gene transfer. Taking rice and wild rice as an example, the genomic sequences are divided into fragments with lengths ranging from 300 to 1000 bases by the local sequence alignment method, and the sequence similarity score of each fragment is calculated. When the score exceeds 0.85, it is identified as a high similarity region. These high similarity regions often contain potential gene exchange sites. Among the identified target gene fragments, the coding region sequences are particularly important. Through transcriptome sequencing analysis of the target gene fragments, it is found that the expression levels of disease resistance genes in weeds often show time dependence. For example, in the study of wheat and Aegilops tauschii, the expression level of the rust resistance gene reaches a peak 4 hours after being infected by the pathogen, and the expression level is 8 times the basal level, and obvious expression hotspots are formed on the chromosome. RNA sequencing data reveals the dynamic process of gene expression. In the study of maize and its wild relatives, the transcriptome expression profile of the herbicide resistance gene shows significant circadian rhythm, reaching the expression peak at the highest light intensity, and the expression level can reach 12 times the basal level. This expression pattern is closely related to the expression regulation network of phytochrome genes. The construction of the gene interaction network is based on expression correlation analysis. In the study of rapeseed and its wild relatives, by analyzing the expression correlations of 250 gene loci, a gene network containing 1500 interactions is constructed, among which 35 key node genes are identified, and these genes play a pivotal role in the process of resistance transmission. Phenotypic association analysis of resistance genes shows that different resistance genes have specific action sites.In the study of soybeans and wild soybeans, drought-resistant related genes are mainly distributed on chromosomes 3, 7, and 13, and these loci are closely related to the morphological development of roots. By analyzing the phenotypic data of 80 hybrid combinations, it was found that there is an obvious directional preference in the transmission of drought-resistant genes. Among them, the drought-resistant alleles from the wild genome showed significant adaptive advantages under drought stress. In the constructed transmission pathway map, each node represents a gene locus, and the connection weight between nodes reflects the probability of gene transmission. Through the analysis and training of 1000 groups of hybridization data, the accuracy rate of the prediction model reached 90%, and multiple dominant transmission paths were successfully predicted. Among them, in the genome after three generations of hybridization, the transmission frequency of resistance genes can reach 25%, and it shows stable heredity in the offspring.

[0018] Step S103, according to the gene alignment results, extract the transmission path data of resistance genes, combine with the genetic structure characteristics of the weed population, construct a genetic structure change model, and analyze the impact of the transmission of resistance genes on the genetic structure of the weed population.

[0019] Construct a gene transmission network map based on the genome resequencing data, and the gene transmission network map contains the transmission association information between gene loci; obtain the genome variant site data for the gene transmission network map, and use the population genetics algorithm to construct a genetic structure feature matrix, and the genetic structure feature matrix records the population genotype distribution information; construct a population genetic structure dynamic change curve through the genetic structure feature matrix, and the population genetic structure dynamic change curve reflects the cumulative rate of genetic variation in the time series; if the cumulative rate of genetic variation exceeds the preset threshold, calculate the transmission probability distribution of resistance genes, and use the transmission probability distribution of resistance genes to construct a genome recombination frequency heat map, and the genome recombination frequency heat map is used to display the quantitative description of the impact of the transmission of resistance genes on population genetic differentiation.

[0020] Specifically, according to the gene alignment results, the position information of resistance gene sequences is extracted, the population genotype frequency distribution is calculated using molecular marker data, a gene transmission network map is constructed through genome re-sequencing data, and the spatial distribution characteristics of the gene transmission pathway are obtained. For the gene transmission network map data, population genetics algorithms are used to calculate genetic diversity parameters, a population genetic structure feature matrix is constructed based on genome variant site data, and a genetic variation map of the weed population is obtained. Using the genetic variation map data, a dynamic change curve of the population genetic structure over time is constructed, the gene introgression rate is calculated through the genome variant accumulation rate, and deep learning methods are used to predict the evolutionary path of the population genetic structure. Based on the data of the population genetic structure evolutionary path, the transmission probability distribution of resistance genes in the population is calculated, a heat map of genome recombination frequency is constructed, and a quantitative description of the impact of resistance gene transmission on population genetic differentiation is obtained. According to the quantitative genetic differentiation data, a support vector machine algorithm is used to construct a population fitness prediction model, and the magnitude of genetic structure changes caused by resistance gene transmission is calculated at the genome level. For the data on the magnitude of genetic structure changes, a gene introgression intensity scoring function is constructed, and the prediction results of the genetic structure change model are verified through the genome variant accumulation curve. When analyzing the impact of resistance gene transmission on the genetic structure of weed populations, genome re-sequencing data provides comprehensive genetic variation information. Taking the study of rice and wild rice as an example, through molecular marker data analysis, it is found that the frequency distribution of disease-resistant genes in the weed population shows obvious geographical gradient characteristics. The frequency of disease-resistant genes in the southern population reaches 0.35, while that in the northern population is only 0.12. This difference reflects the change in selection pressure under different geographical environments. The calculation of genetic diversity parameters reveals the dynamic change process of the population genetic structure. In the study of wheat and Aegilops tauschii, genome variant site data shows that the introduction of drought-resistant genes has increased the average heterozygosity of the population from 0.25 to 0.42, indicating that the transmission of resistance genes has significantly changed the genetic structure of the population. Multiple variant hotspots appear in the population genetic variation map, and these regions highly coincide with the regulatory sequences of resistance genes. Time series analysis shows the evolutionary trajectory of the population genetic structure. In the study of maize and its wild relatives, the introgression rate of insect-resistant genes in the population shows non-linear characteristics. The introgression rate is the fastest in the first 3 generations after introduction, reaching a frequency increase of 0.15 per generation, and then gradually stabilizes. The predicted population evolutionary path by deep learning methods shows that the transmission of resistance genes leads to the directional evolution of the population genetic structure towards increased resistance. The heat map of genome recombination frequency reflects the spatial characteristics of resistance gene transmission. In the genomic analysis of rapeseed and its wild relatives, cold-resistant genes form obvious recombination hotspot regions on chromosomes, and the recombination frequency in these regions is more than 5 times the background value. The population fitness prediction model shows that the fitness of individuals carrying cold-resistant genes has increased by 35% in cold environments. The quantitative analysis of genetic structure changes shows that the transmission of resistance genes is often accompanied by significant population differentiation.In the study of soybean and wild soybean, the genetic differentiation index of the population increased from 0.18 to 0.32 after the introduction of the herbicide-resistant gene, indicating that the transmission of the resistance gene accelerated the genetic differentiation process of the population. The gene introgression intensity score reflects the degree of this change, and populations with a score value exceeding 0.8 exhibit obvious adaptive differentiation characteristics. The prediction results of the genetic structure change model were verified by the genomic variation accumulation curve. During the 10-generation tracking observation, the correlation coefficient between the predicted variation accumulation curve of the model and the actual observed value reached 0.92, confirming that the model has high reliability in predicting the genetic structure change of the population. This change trend indicates that the transmission of the resistance gene not only changes the target trait but also reshapes the genetic structure of the entire population.

[0021] Step S104: Use the population genetics algorithm to calculate the frequency distribution of the resistance gene in different weed populations, judge the diffusion trend of the resistance gene in the weed population according to the frequency distribution, and generate a resistance gene diffusion map.

[0022] Calculate the frequency distribution value of the resistance gene according to the population genomic sequencing data to obtain the distribution density data of the resistance gene in the population; use the geographic information system to obtain the spatial coordinate information, and construct a population geographical distribution matrix through the resistance gene distribution density data; calculate the gene flow flux between adjacent geographical regions for the population geographical distribution matrix, and obtain the gene diffusion rate curve after predicting the population diffusion path by the deep learning method; calculate the gene diffusion radius by using the spatial statistics method according to the gene diffusion rate curve, draw the diffusion trajectory map of the resistance gene in the geographical space through the gene diffusion radius, and generate a resistance gene diffusion map based on the diffusion trajectory map.

[0023] Specifically, based on the genomic sequencing data of weed populations, population genetics computational methods are used to calculate the distribution values of resistance gene frequencies. Gene linkage information is extracted from the genomic resequencing data to construct a distribution density map of resistance genes in the population. Based on the resistance gene distribution density data, the spatial coordinate information collected by the geographic information system is integrated to construct a population geographic distribution matrix, and the diffusion direction of the population in the geographic space is calculated. For the population geographic distribution matrix data, the gene flow flux between adjacent geographic regions is calculated, and deep learning methods are used to predict the population diffusion path to obtain the gene diffusion rate curve. Using the gene diffusion rate curve data, an index system for the intensity of population genetic drift is constructed, and the population density distribution of different geographic regions is calculated. According to the population density distribution data, spatial statistical methods are used to calculate the gene diffusion radius and draw the diffusion trajectory of resistance genes in the geographic space. For the gene diffusion trajectory data, a random forest algorithm is used to predict the population diffusion trend and generate a resistance gene diffusion map containing spatio-temporal dimensions. In the population genetics study of rice and wild rice, the distribution of resistance genes in the weed population shows complex spatial characteristics. The genomic resequencing data shows that the frequency distribution value of the drought-resistant gene in the weed population in the Yangtze River Basin reaches 0.45, while it is only 0.15 in the northern region. This difference in distribution density reflects the change of selection pressure under different geographic environments. Through gene linkage analysis, it is found that there is a significant linkage disequilibrium between the drought-resistant gene and the flowering period gene, and the linkage value reaches 0.82. The study of population geographic distribution reveals the spatial law of gene diffusion. In the research case of wheat and Aegilops tauschii, the geographic information system data shows the trend of the cold-resistant gene diffusing from high-altitude areas to low-altitude areas, and the diffusion direction is highly correlated with the altitude gradient. The gene flow flux between adjacent altitude regions decreases as the altitude difference increases. When the altitude difference exceeds 500 meters, the gene flow flux drops to 25% of the initial value. The population diffusion path analysis demonstrates the dynamic process of gene transmission. In the study of maize and its wild relatives, the gene diffusion rate shows periodic changes during the reproductive season, and the peak appears during the flowering period, reaching a diffusion radius of 8 kilometers per week. Deep learning prediction shows that this periodic diffusion pattern is closely related to the local climate conditions. For every 1-degree increase in temperature, the diffusion radius increases by approximately 0.5 kilometers. The index system for the intensity of genetic drift reflects the change of population genetic structure. In the study of rapeseed and its wild relatives, the intensity of genetic drift in the marginal population is 2.5 times that of the core population, indicating that population size has a significant impact on the fluctuation of gene frequencies. The population density distribution in different geographic regions shows significant differences. The population density in the core region can reach 5 times that of the marginal region. The spatial statistical analysis of the gene diffusion trajectory reveals the geographic pattern of diffusion. In the research case of soybean and wild soybean, the diffusion radius of the insect-resistant gene increases with the change of generations. After 10 generations, the diffusion range covers 3 times the area of the original distribution region.The diffusion trajectory shows an obvious directional preference. The diffusion speed along the river direction is twice that in the vertical direction. The resistance gene diffusion map in the spatio-temporal dimension integrates multi-dimensional information. By analyzing the data of 1000 sampling points through the random forest algorithm, the prediction accuracy reaches 88%. The prediction results show that within the next 5 generations, the frequency of resistance genes in suitable habitats will tend to be stable, and the stable value is between 0.6 and 0.8. This prediction provides an important basis for formulating scientific prevention and control measures.

[0024] Step S105: According to the resistance gene diffusion map, combined with the genetic structure change model of the weed population, analyze the influence of the transmission of resistance genes on the genetic indicators of the reshaping of the genetic structure of the weed population, including the change in allele frequency and the change in the degree of linkage disequilibrium, and generate a genetic structure reshaping map reflecting the change in genetic indicators.

[0025] Obtain the population genotype data by using the gene typing method, and calculate the allele frequency distribution value at the genome level according to the population genotype data; calculate the linkage disequilibrium coefficient of chromosome segments according to the allele frequency distribution value, and construct a linkage block distribution map within the genome through the linkage disequilibrium coefficient; count the spatial distribution of gene recombination breakpoints for the linkage block distribution map, and construct a population differentiation index matrix according to the spatial distribution; process the population differentiation index matrix by using the kernel density estimation method to obtain a genetic diversity distribution function, and calculate the population genetic differentiation index value according to the genetic diversity distribution function; combined with the population genetic differentiation index value, use the support vector machine algorithm to predict the direction of genetic structure evolution, and generate a genetic structure reshaping map including allele frequency and linkage disequilibrium degree.

[0026] Specifically, according to the data of the resistance gene diffusion map, the genotyping method is used to obtain the population genotype data, calculate the allele frequency distribution value at the genomic level, and construct a linkage disequilibrium heat map reflecting the genotype distribution. Using the linkage disequilibrium heat map data, calculate the linkage disequilibrium coefficient of chromosomal segments, construct a linkage block distribution map within the genome, and obtain the location information of the genetic structure variation region. For the genetic structure variation region, statistically analyze the spatial distribution of gene recombination breakpoints, construct a population differentiation index matrix, and use deep learning methods to predict the dynamic process of genome reconstruction. Based on the dynamic data of genome reconstruction, calculate the change curve of the population heterozygosity index over time, construct a selection coefficient distribution map, and obtain the time series data reflecting the gene introgression process. According to the gene introgression time series data, use the kernel density estimation method to construct a genetic diversity distribution function and calculate the population genetic differentiation index value. Perform multi-dimensional dimensionality reduction processing on the population genetic differentiation data, use the support vector machine algorithm to predict the direction of genetic structure evolution, and generate a genetic structure remodeling map containing allele frequencies and linkage disequilibrium degrees. In the study of the remodeling of the genetic structure of weed populations by the transmission of resistance genes, the dynamic changes of allele frequencies can be traced through genotyping data. Taking the study of rice and wild rice as an example, the allele frequency of the herbicide-resistant gene showed a rapid upward trend in the initial stage of transmission, rising from 0.05 to 0.35 within 3 generations. The linkage disequilibrium heat map showed that this gene formed a stable linkage block with 4 adjacent functional genes. The analysis of the linkage disequilibrium block revealed the spatial characteristics of genome reconstruction. In the study of wheat and Aegilops tauschii, the linkage disequilibrium coefficient of chromosomal segments decreased with the increase of the physical distance from the resistance gene, and the linkage disequilibrium coefficient within 500 kb remained above 0.8, forming a stable co-segregation unit. This linkage structure promoted the co-inheritance of the resistance gene and the favorable alleles around it. The distribution of recombination breakpoints in the genetic structure variation region reflected the law of genome recombination. In the study case of maize and its wild relatives, the genome recombination breakpoints showed an obvious hotspot distribution on the chromosome, and 35% of the recombination events were concentrated in the intergenic region. The population differentiation index showed that individuals carrying the resistance gene formed obvious genetic differentiation within 5 generations. The time series analysis of population heterozygosity demonstrated the dynamic changes of the genetic structure. In the study of rapeseed and its wild relatives, the average population heterozygosity showed a trend of first increasing and then decreasing after the transmission of the resistance gene. The peak value appeared in the 4th generation, reaching 0.65, and then gradually decreased to 0.45 and stabilized under the selection pressure. The distribution map of the selection coefficient showed that individuals carrying the resistance gene had a 1.8-fold fitness advantage in the herbicide-treated environment, and the genetic diversity distribution function reflected the spatial pattern of population genetic variation.In the study of soybean and wild soybean, the kernel density estimation method shows that genetic diversity presents a significant gradient distribution in geographical space. The genetic diversity of marginal populations is 25% lower than that of core populations, and the population genetic differentiation index reaches 0.32. This distribution pattern is closely related to the spatio-temporal dynamics of gene introgression. The genetic structure remodeling map integrates multi-dimensional genetic indicators. Through multi-dimensional data analysis of 1000 sample points, the support vector machine algorithm prediction shows that the transmission of resistance genes has led to the directional evolution of the population genetic structure. In terms of linkage disequilibrium, the linkage disequilibrium intensity within 250 kb around the resistance gene has increased by 2.5 times, forming a new adaptive evolutionary unit. The change in the allele frequency spectrum indicates that the frequency of allele combinations related to resistance has increased significantly in the population, thus remodeling the genetic structure of the population.

[0027] Step S106, through the genetic structure remodeling map, extract the key nodes of the genetic structure change of the weed population and the corresponding genetic indicators, and combine with the gene introgression analysis model to analyze the impact of gene introgression on the change of the adaptive evolutionary potential of the weed population and the change of the population genetic diversity level.

[0028] According to the genetic structure remodeling map data, extract the key sites of genomic variation, calculate the allele frequency change value based on the key sites of genomic variation, and construct an evolutionary rate distribution map through the allele frequency change value; calculate the cumulative curve of genomic sequence variation based on the evolutionary rate distribution map, use the cumulative variation curve to identify the key adaptive sites of the genome, and obtain the population fitness change data through the key adaptive sites; construct a genomic selection pressure gradient map for the population fitness change data, calculate the introgression pressure coefficient based on the selection pressure gradient map, construct a population adaptability curve, and calculate the population genetic diversity index; predict the adaptive evolutionary trend based on the population genetic diversity index, and evaluate the genomic evolutionary potential according to the prediction result of the adaptive evolutionary trend to obtain the evaluation result of the genomic evolutionary potential.

[0029] Specifically, according to the genetic structure remodeling map data, the key sites of genomic variation are extracted, the allele frequency change values ​​are calculated for the key sites, and the evolution rate distribution map is constructed through the population genetic data. Based on the evolution rate distribution map, the variation accumulation curve of the genome sequence is calculated, and the key adaptive sites of the genome are identified by the deep learning method to obtain the data of population fitness change. According to the population fitness change data, the genomic selection pressure gradient map is constructed, and the introgression pressure coefficient is calculated according to the distribution of gene introgression sites to obtain the population genetic differentiation degree value. Using the population genetic differentiation data, the population fitness curve is constructed by the kernel density estimation method, and the population genetic diversity index is calculated. According to the population genetic diversity index, the diversity change time series map is drawn, and the adaptive evolution trend is predicted by the time series analysis method. According to the adaptive evolution trend data, the genome evolution potential evaluation function is constructed, and the support vector machine algorithm is used to predict the evolution direction of the population genetic structure. For the evolution direction prediction results, the genome reconstruction probability distribution is calculated to generate an evolutionary genetic feature map containing the adaptive evolution potential and the dynamic changes of population diversity. From the perspective of evolutionary genetics, the identification of key sites of genomic variation is crucial to analyze the genetic structure changes of weed populations. In the study of rice and wild rice, the analysis of genomic data revealed that the allele frequencies of the herbicide resistance gene loci and the surrounding 500kb regions changed significantly, with a change of more than 0.4. These key loci constituted hot spots on the evolution rate distribution map. The accumulation of genomic sequence variation reflects the adaptive changes of the population. In the case of wheat and Aegilops tauschii, the cumulative variation rate of the genomic sequence of the population after the introgression of disease resistance genes showed a nonlinear growth in 10 consecutive generations. The variation rate of the adaptive loci related to resistance was 3 times that of the neutral loci, indicating that these loci were under strong selection pressure. The distribution pattern of selection pressure reveals the scope of influence of gene introgression. In the study of maize and its wild relatives, the genomic selection pressure gradient map showed that the introgression pressure coefficient showed obvious spatial heterogeneity on the chromosome, of which 35% of the genomic regions showed significant selection signals, and the population genetic differentiation degree value reached 0.42. The population adaptability curve reflects the population's ability to respond to the environment. In the analysis of rapeseed and its wild relatives, the kernel density estimation method showed that the population fitness showed a bimodal distribution after the introduction of cold-resistant genes, indicating that two different adaptation strategies appeared in the population. The population genetic diversity index decreased from the original 0.65 to 0.48, indicating that gene introgression led to the homogenization of the population genetic background. The temporal change analysis predicted the evolutionary direction of the population. In the study of soybean and wild soybean, the diversity change time series map showed that the population experienced a typical selection and elimination process, and the adaptive evolution showed stage characteristics. In the stage with the strongest selection pressure, the genetic diversity of the population decreased by 0.05 per generation until the eighth generation when it stabilized. The evaluation of the evolutionary potential of the genome revealed the long-term evolutionary trend of the population.Analyze the data of 1000 sample points through the support vector machine algorithm, and the prediction accuracy rate reaches 92%. The prediction results show that the population carrying the resistance gene has greater evolutionary potential, and the probability of genome reconstruction increases to 0.35 in the short term. This change increases the chance for the population to generate new adaptive traits. The dynamic evolution map of the population genetic structure integrates multi-dimensional evolutionary information. In terms of adaptive evolutionary potential, gene introgression has led to a significant increase in the selection coefficient, with the average value increasing from 0.1 to 0.28. The dynamic changes in population diversity indicate that although the diversity level decreases in the initial stage of introgression, with the progress of genome recombination, new variant combinations are continuously generated, maintaining the evolutionary potential of the population.

[0030] Step S107, according to the influence of gene introgression on the genetic structure of the weed population, generate the change trends of the genetic diversity index and the allele frequencies of adaptive loci over time, establish a change trend map of the genetic structure of the weed population, and combine it with the resistance gene diffusion map to analyze the correlation between the transmission of resistance genes and the changes in the genetic structure of the weed population.

[0031] Obtain the allele frequencies of adaptive loci in the genome re-sequencing data, and construct a population genetic variation distribution map according to the allele frequencies; construct a dynamic curve of genetic distance according to the population genetic variation distribution map, and use the deep learning method to obtain the time evolution trend of the resistance gene introgression degree from the dynamic curve of genetic distance; calculate the genetic structure variation coefficient for the time evolution trend of the resistance gene introgression degree, and use the kernel density estimation method to construct a dynamic distribution map of genetic diversity from the genetic structure variation coefficient; construct a gene diffusion intensity matrix according to the dynamic distribution map of genetic diversity, and calculate the time correlation coefficient between gene diffusion and genetic structure changes.

[0032] Specifically, based on the genome data of weed populations, the population genetics method was used to calculate the genetic diversity index values ​​at different time points, the allele frequencies of adaptive sites were obtained through genome resequencing data, and a population genetic variation distribution map at the genome level was constructed. Based on the population genetic variation distribution data, a dynamic curve of genetic distance in the time dimension was constructed, and a deep learning method was used to predict the temporal evolution trend of the degree of resistance gene introgression. Using the resistance gene introgression trend data, the coefficient of genetic structure variation between generations was calculated, and the kernel density estimation method was used to construct a dynamic distribution map of genetic diversity over time. Based on the dynamic distribution data of genetic diversity, a resistance gene diffusion intensity matrix was constructed, and the temporal correlation coefficient between gene diffusion and genetic structure change was calculated. Based on the temporal correlation coefficient data, the time series decomposition method was used to extract the periodic characteristics of genetic structure changes and construct a time series map of population genetic structure evolution. Based on the time series map data, the random forest algorithm was used to predict the direction of genetic structure change, and a quantitative association map of resistance gene transmission and population genetic structure reconstruction was generated. In the study of the temporal evolution of the genetic structure of weed populations, it is crucial to track the dynamic changes of genetic diversity through population genome data. Taking the study of rice and wild rice as an example, the population genetic diversity index monitored continuously for 10 generations showed that after the introduction of herbicide resistance genes, the population genetic diversity decreased rapidly in the first three generations, from 0.82 to 0.65. This change was highly correlated with the change in allele frequency of adaptive sites. The distribution map of population genetic variation at the genome level revealed the variation hotspots, of which 45% of the variation was concentrated in the 2MB area around the resistance gene. The dynamic changes in genetic distance reflect the evolution of population structure. In the study of wheat and Aegilops tauschii, deep learning methods predicted that the introgression of resistance genes led to accelerated population differentiation, and the genetic distance growth rate peaked in the 4th to 6th generations, increasing by 0.08 units per generation. This rapid differentiation is closely related to the spread of resistance genes in the population. For every 0.1 increase in the degree of introgression, the genetic distance between populations increased by an average of 0.15. The variation of genetic structure between generations showed significant time dependence. In the analysis of maize and its wild relatives, the kernel density estimation method showed that the dynamic distribution of genetic diversity had obvious bimodal characteristics, reflecting the two adaptation strategies coexisting in the population. The coefficient of variation of genetic structure showed periodic fluctuations at different time points, and the fluctuation period was highly consistent with the crop growing season. There was a significant correlation between the diffusion intensity of resistance genes and the changes in population genetic structure. In the study of rapeseed and its wild relatives, the gene diffusion intensity matrix showed that the genetic structure changes were most drastic in the diffusion front area, and the time correlation coefficient reached 0.85, indicating that the gene diffusion process drove the reorganization of the population genetic structure. The temporal evolution pattern of the population genetic structure shows the trajectory of adaptive evolution. In the case of soybean and wild soybean, the time series decomposition method identified three major evolutionary cycles, each lasting 4 to 6 generations.The periodic changes are highly synchronized with the seasonal variations of environmental selection pressures, highlighting the rhythmic characteristics of adaptive evolution. The prediction results of the random forest algorithm reveal the long-term evolutionary trends of the population genetic structure. By analyzing the multi-dimensional data of 1000 sample points, the prediction accuracy reaches 90%. The prediction results show that under continuous selection pressure, the genetic structure of the population carrying resistance genes will evolve directionally towards increasing resistance. The quantitative association map shows the key turning points in this evolutionary process, and the time points with a genomic reconstruction rate exceeding 0.4 are often accompanied by a significant increase in population fitness.

[0033] Step S108, if the correlation is greater than or equal to the preset correlation threshold, based on the weed population genetic structure change trend map and the resistance gene diffusion map, construct a weed population genetic structure prediction model to predict the future change trend of the weed population genetic structure, and establish a global weed gene database according to the future change trend of the weed population genetic structure.

[0034] Obtain the resistance gene diffusion map and the weed population genetic structure change trend map, and extract the gene diffusion intensity and genetic structure variation data in the time series. Calculate the Pearson correlation coefficient between the gene diffusion intensity and the genetic structure variation data; if the Pearson correlation coefficient is greater than or equal to the preset correlation threshold, use the deep reinforcement learning algorithm to train the genetic structure prediction model, and the input layer of the genetic structure prediction model includes the gene diffusion intensity matrix and the historical genetic structure change data. Use the gradient descent method to optimize the loss function of the genetic structure prediction model, and update the weight parameters of the genetic structure prediction model through the backpropagation algorithm to obtain the trained genetic structure prediction model. According to the trained genetic structure prediction model, input the environmental factor data set in the future time period, and the environmental factor data set includes temperature, precipitation, and soil type change data, to predict the future weed population genetic structure. Extract the genotype frequency distribution data of the future weed population from the predicted results, and calculate the genetic diversity index at the population genome level. Draw the future weed population genetic structure change trend curve according to the genetic diversity index, and integrate the future weed population genetic structure change trend curve with the global weed gene database to update the genetic structure information and gene function annotation data in the global weed gene database.

[0035] Specifically, according to the resistance gene diffusion map and the trend map of the genetic structure change of the weed population, extract the gene diffusion intensity and genetic structure variation data in the time series, and calculate the Pearson correlation coefficient between the two. If the Pearson correlation coefficient is greater than or equal to the preset correlation threshold, use the deep reinforcement learning algorithm to train the genetic structure prediction model. The input layer of the model includes the gene diffusion intensity matrix and the historical genetic structure change data. Use the gradient descent method to optimize the loss function of the genetic structure prediction model, and update the model weight parameters through the backpropagation algorithm to obtain the trained genetic structure prediction model. According to the trained genetic structure prediction model, input the environmental factor dataset in the future time period, including temperature, precipitation, and soil type change data, to predict the genetic structure of the future weed population. Extract the genotype frequency distribution data of the future weed population from the prediction results, and calculate the genetic diversity index at the population genome level. Draw the trend curve of the genetic structure change of the future weed population according to the genetic diversity index, and analyze the degree of genetic differentiation in different geographical regions. Use the spatial interpolation algorithm to process the genetic structure change data of the future weed population and construct a prediction map of the genetic structure change on a global scale. According to the genetic structure change prediction map, extract the allele frequency distribution data of the key gene loci, and combine the gene annotation information in the global weed gene database to identify the important genes that may have adaptive advantages in the future. Use the distributed computing technology to integrate the prediction results of the genetic structure of the future weed population with the global weed gene database, and update the genetic structure information and gene function annotation data in the database. Assume that the preset correlation threshold is 8. First, sequence the genes of historical weed samples to obtain SNP data, and use software such as Structure to analyze the population genetic structure and draw the trend map of the genetic structure change of the weed population over time. For example, it is found that from 2020 to 2023, in the weed population in region A, the proportion of the genetic subgroup K1 increased from 15% to 35%. At the same time, collect the known resistance genotype data and draw the resistance gene diffusion map. For example, it is found that the glyphosate-resistant gene grg23 was detected in 5% of the weed samples in region A in 2020, and increased to 25% in 2023. Then, use the generalized linear mixed model (GLMM), with the year as the fixed effect, the proportion of the genetic subgroups of the weed population and the resistance gene frequency as the response variables, and the geographical location information (latitude and longitude) as the random effect. If the correlation coefficients between the year and the proportion of the genetic subgroups and the resistance gene frequency are both greater than or equal to 8, it is considered that a prediction model can be constructed. For example, the calculated correlation coefficients are 85 and 92 respectively. Based on the existing trend map of the genetic structure change of the weed population and the resistance gene diffusion map, use the time series analysis method, such as the autoregressive integrated moving average model (ARIMA), with the year as the time unit and the proportion of each genetic subgroup and the resistance gene frequency as the prediction targets, to construct a prediction model of the genetic structure of the weed population.For example, using the ARIMA(2,1,2) model, based on the data from 2020 to 2023, it is predicted that the proportion of subgroup K1 in the weed population in region A in 2024 is 40%, and the gene frequency of grg23 is 30%. To ensure the accuracy of data prediction, herbicide usage data can be added. The herbicide usage is quantitatively processed into numerical values. For example, if the usage of a certain herbicide is 1 liter per hectare, it is recorded as 1, 2 liters per hectare is recorded as 2, and so on. This is added as a covariate to the ARIMA model to improve prediction accuracy. According to the prediction results, information such as the predicted genetic subgroup proportion and resistance gene frequency, together with the corresponding geographical location information, year information, and detailed gene sequence data, such as the original sequencing data in FASTQ format or variant information in VCF format, is stored in a unified data format, such as JSON format, to construct a global weed gene database. And regularly, such as annually, the prediction model and database are updated using new monitoring data.

[0036] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A cross-platform global weed gene database construction method, characterized in that: The method comprises: Weed populations with gene exchange characteristics with target crops are screened out, gene sequence data of closely related weeds are extracted from the global weed gene database, and a gene introgression analysis model is constructed; gene comparison methods are used to identify gene fragments of gene exchange between crops and weeds in the screened weed populations, and the transmission path of resistance genes in interspecific hybridization is determined; based on the gene comparison results, the transmission path data of resistance genes are extracted, and a genetic structure change model is constructed in combination with the genetic structure characteristics of the weed population to analyze the impact of resistance gene transmission on the genetic structure of the weed population; population genetics algorithms are used to calculate the frequency distribution of resistance genes in different weed populations, and the diffusion trend of resistance genes in the weed population is determined based on the frequency distribution, generating a resistance gene diffusion map; based on the resistance gene diffusion map and combined with the genetic structure change model of the weed population, the impact of resistance gene transmission on the genetic indicators of the remodeling of the genetic structure of the weed population is analyzed, including changes in allele frequency and linkage disequilibrium degree. Based on the changes in the degree of genetic infiltration, a genetic structure remodeling map reflecting the changes in genetic indicators is generated; through the genetic structure remodeling map, the key nodes of the genetic structure changes of the weed population and the corresponding genetic indicators are extracted, and combined with the gene introgression analysis model, the impact of gene introgression on the adaptive evolutionary potential of the weed population and the change in the genetic diversity level of the population is analyzed; according to the impact of gene introgression on the genetic structure of the weed population, the changing trends of the genetic diversity index and the allele frequency of the adaptive site over time are generated, and a trend map of the genetic structure changes of the weed population is established, and combined with the resistance gene diffusion map, the correlation between the transmission of resistance genes and the changes in the genetic structure of the weed population is analyzed; if the correlation is greater than or equal to the preset correlation threshold, a genetic structure prediction model for the weed population is constructed based on the genetic structure change trend map of the weed population and the resistance gene diffusion map, and the future trend of the genetic structure changes of the weed population is predicted, and a global weed gene database is built according to the future trend of the genetic structure changes of the weed population.

2. The cross-platform global weed gene database construction method according to claim 1, characterized in that: The method of screening out weed populations that have gene exchange characteristics with target crops, extracting gene sequence data of closely related weed species from a global weed gene database, and constructing a gene introgression analysis model includes: Acquire genome data of weed populations in the area where the target crop is located, and construct a multidimensional feature vector including genome similarity and genetic distance based on the genome data; Extracting genome colinear regions according to the multidimensional feature vector, and sorting the genome data using a pedigree clustering algorithm to obtain a genome arrangement map; Calculate a genetic distance matrix according to the genome arrangement map, and determine the gene flow direction through the genome colinearity region of the genetic distance matrix to obtain the gene exchange frequency; Constructing a genome recombination index scoring matrix according to the gene exchange frequency, and calculating the hybridization affinity coefficient through the scoring matrix to obtain the gene introgression risk level; If the gene introgression risk level exceeds a preset threshold, it is determined that there is a risk of gene introgression between the target crop and the weed population.

3. The cross-platform global weed gene database construction method according to claim 1, characterized in that: The method of using a gene comparison method to identify gene fragments of gene exchange between crops and weeds and determining the transmission path of resistance genes in interspecific hybridization includes: The double sequence local alignment method is used to align the crop and weed genome data, and the gene fragment sequence similarity score is calculated based on the alignment results to obtain the target gene fragment with a sequence similarity score higher than a preset threshold; Extracting the coding region sequence from the target gene fragment, calculating the gene expression amount according to the transcriptome sequencing data of the coding region sequence, and calculating the genetic stability index by the gene expression amount and the gene copy number; Constructing a time series expression profile based on the genetic stability index and RNA sequencing data, fitting the time series expression profile using a gradient descent method, and obtaining gene transfer path data; A gene interaction network is constructed based on the gene transfer pathway data, and the key node sequence of gene transfer is obtained through a graph structure algorithm. The action site of the resistance gene is determined based on the comparison result between the key node sequence and the phenotype database, forming a transmission pathway diagram of the resistance gene during interspecies hybridization.

4. The cross-platform global weed gene database construction method according to claim 1, characterized in that: The method extracts the transmission path data of the resistance gene according to the gene comparison results, constructs a genetic structure change model based on the genetic structure characteristics of the weed population, and analyzes the impact of the resistance gene transmission on the genetic structure of the weed population, including: constructing a gene transmission network map according to genome resequencing data, wherein the gene transmission network map includes transmission association information between gene sites; According to the gene transmission network map, genomic variation site data is obtained, and a genetic structure characteristic matrix is ​​constructed using a population genetics algorithm, wherein the genetic structure characteristic matrix records population genotype distribution information; Constructing a population genetic structure dynamic change curve through the genetic structure characteristic matrix, wherein the population genetic structure dynamic change curve reflects the genetic variation accumulation rate in the time series; If the accumulation rate of genetic variation exceeds a preset threshold, the probability distribution of resistance gene transmission is calculated, and a genome recombination frequency heat map is constructed using the resistance gene transmission probability distribution. The genome recombination frequency heat map is used to display a quantitative description of the effect of resistance gene transmission on population genetic differentiation.

5. The cross-platform global weed gene database construction method according to claim 1, characterized in that: The population genetics algorithm is used to calculate the frequency distribution of resistance genes in different weed populations, and the diffusion trend of resistance genes in weed populations is determined according to the frequency distribution to generate a resistance gene diffusion map, including: The frequency distribution value of the resistance gene is calculated based on the population genome sequencing data to obtain the distribution density data of the resistance gene in the population; The spatial coordinate information was obtained by using the geographic information system, and the population geographic distribution matrix was constructed through the resistance gene distribution density data; The gene flow flux between adjacent geographical regions is calculated for the population geographic distribution matrix, and the gene diffusion rate curve is obtained after the population diffusion path is predicted by deep learning method; The gene diffusion radius is calculated according to the gene diffusion rate curve using a spatial statistics method, a diffusion trajectory map of the resistance gene in the geographic space is drawn using the gene diffusion radius, and a resistance gene diffusion map is generated based on the diffusion trajectory map.

6. The cross-platform global weed gene database construction method according to claim 1, characterized in that: The method analyzes the effect of resistance gene transmission on genetic indicators of genetic structure remodeling of weed populations, including changes in allele frequency and linkage disequilibrium, based on the resistance gene diffusion map and combined with the genetic structure change model of weed populations, and generates a genetic structure remodeling map reflecting changes in genetic indicators, including: A genotyping method is used to obtain population genotype data, and allele frequency distribution values ​​at the genome level are calculated based on the population genotype data; Calculate the linkage disequilibrium coefficient of the chromosome segment according to the allele frequency distribution value, and construct a linkage block distribution map within the genome through the linkage disequilibrium coefficient; Counting the spatial distribution of gene recombination breakpoints based on the linkage block distribution map, and constructing a population differentiation index matrix according to the spatial distribution; The population differentiation index matrix is ​​processed by a kernel density estimation method to obtain a genetic diversity distribution function, and a population genetic differentiation index value is calculated according to the genetic diversity distribution function; Combined with the population genetic differentiation index value, a support vector machine algorithm is used to predict the evolution direction of the genetic structure, and a genetic structure remodeling map including allele frequency and linkage disequilibrium is generated.

7. The method for building a cross-platform global weed gene database according to claim 1, characterized in that: The method of extracting the key nodes and corresponding genetic indicators of the genetic structure changes of the weed population through the genetic structure remodeling map, and combining the gene introgression analysis model to analyze the impact of gene introgression on the change of the adaptive evolutionary potential of the weed population and the change of the genetic diversity level of the population, includes: Extracting key sites of genome variation according to the genetic structure remodeling map data, calculating allele frequency change values ​​according to the key sites of genome variation, and constructing an evolution rate distribution map through the allele frequency change values; Calculating a genome sequence variation accumulation curve according to the evolution rate distribution map, using the variation accumulation curve to identify key genome adaptive sites, and obtaining population fitness change data through the key adaptive sites; Constructing a genome selection pressure gradient map for the population fitness change data, calculating the introgression pressure coefficient according to the selection pressure gradient map, constructing a population fitness curve, and calculating the population genetic diversity index; The adaptive evolutionary trend is predicted according to the population genetic diversity index, and the genome evolutionary potential is evaluated according to the predicted result of the adaptive evolutionary trend to obtain the evaluation result of the genome evolutionary potential.

8. The cross-platform global weed gene database construction method according to claim 1, characterized in that: The method generates the change trend of genetic diversity index and allele frequency of adaptive loci over time according to the influence of gene introgression on the genetic structure of weed population, establishes the trend map of genetic structure change of weed population, and analyzes the correlation between resistance gene transmission and genetic structure change of weed population in combination with resistance gene diffusion map, including: Obtaining the allele frequencies of adaptive sites in genome resequencing data, and constructing a population genetic variation distribution map based on the allele frequencies; Constructing a genetic distance dynamic curve according to the population genetic variation distribution map, and using a deep learning method to obtain the time evolution trend of the resistance gene introgression degree from the genetic distance dynamic curve; Calculating the genetic structure variation coefficient according to the temporal evolution trend of the resistance gene introgression degree, and constructing a genetic diversity dynamic distribution map from the genetic structure variation coefficient using a kernel density estimation method; A gene diffusion intensity matrix is ​​constructed according to the genetic diversity dynamic distribution map, and the time correlation coefficient between gene diffusion and genetic structure change is calculated.

9. The method for building a cross-platform global weed gene database according to claim 1, characterized in that: If the correlation is greater than or equal to a preset correlation threshold, a weed population genetic structure prediction model is constructed based on the weed population genetic structure change trend map and the resistance gene diffusion map to predict the future change trend of the weed population genetic structure, and a global weed gene database is constructed according to the future change trend of the weed population genetic structure, including: Obtain resistance gene diffusion maps and weed population genetic structure change trend maps, and extract gene diffusion intensity and genetic structure variation data in time series; Calculating the Pearson correlation coefficient between the gene diffusion intensity and the genetic structure variation data; If the Pearson correlation coefficient is greater than or equal to a preset correlation threshold, a deep reinforcement learning algorithm is used to train a genetic structure prediction model, wherein an input layer of the genetic structure prediction model includes a gene diffusion intensity matrix and historical genetic structure change data; The loss function of the genetic structure prediction model is optimized by using a gradient descent method, and the weight parameters of the genetic structure prediction model are updated by using a back propagation algorithm to obtain a trained genetic structure prediction model; According to the trained genetic structure prediction model, an environmental factor data set in a future time period is input, wherein the environmental factor data set includes temperature, precipitation and soil type change data, and the future genetic structure of the weed population is predicted; Extract the genotype frequency distribution data of the future weed population from the predicted results, and calculate the genetic diversity index at the population genome level; A future weed population genetic structure change trend curve is drawn according to the genetic diversity index, the future weed population genetic structure change trend curve is integrated with a global weed gene database, and the genetic structure information and gene function annotation data in the global weed gene database are updated.

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