Specimen biological information analysis and verification system based on big data

Abstract

The invention relates to the technical field of biological information processing, in particular to a specimen biological information analysis and verification system based on big data, which comprises a big data acquisition module for acquiring specimen morphological data, gene sequence data and associated ecological environment data; the biological information analysis module mines a specimen feature association rule based on a big data algorithm to generate an analysis result; the multi-dimensional verification module performs cross verification on the analysis result according to form comparison, gene sequence verification and ecological habit matching dimensionality, and outputs a verification report; the species adaptation module autonomously optimizes and analyzes algorithm parameters and verifies weight distribution according to different class group specimen characteristics, storage states and verification reports; and the cloud co-processing module stores the specimen multi-dimensional data and the analysis and verification result to the cloud, monitors the data quality in real time, dynamically adjusts the storage resource allocation and returns the storage resource allocation to the user side. Therefore, the problems of data dispersion, low verification accuracy and the like in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics processing technology, specifically to a specimen bioinformatics analysis and verification system based on big data. Background Technology

[0002] Specimen bioinformatics analysis and validation systems are core supports for biomedical research, clinical diagnosis, and public health control. The accuracy of their analysis results directly affects the reliability of research conclusions and the scientific validity of medical decisions. In long-term application, these systems are prone to problems such as low data standardization, inaccurate feature extraction, and insufficient persuasiveness of validation results due to factors such as differences in specimen preservation conditions, diverse detection techniques and methods, and dispersed data sources (clinical sample banks, laboratory testing equipment, public bioinformatics databases, etc.). This not only reduces analysis efficiency but may also increase the risk of misjudgment, thereby affecting subsequent research progress or clinical treatment outcomes. As biomedicine develops towards precision and high throughput, higher demands are placed on specimen bioinformatics analysis in terms of data processing speed, feature recognition specificity, comprehensiveness of result validation, and adaptability to multiple scenarios. There is an urgent need for an intelligent system capable of integrating multi-source heterogeneous data, dynamically optimizing the analysis process, and achieving comprehensive validation to meet the application needs of complex biomedical scenarios.

[0003] However, traditional specimen bioinformatics analysis and validation systems have significant technical shortcomings: Their analytical logic is rigid, often employing single algorithms or fixed parameters for data processing. They fail to fully integrate multi-dimensional information related to specimen attributes, testing environment, and clinical background, making it difficult to adapt to the specific analytical needs of different specimen types (tissues, body fluids, microorganisms, etc.), resulting in insufficient data mining depth and the omission of useful information. Their data integration capabilities are weak, lacking a unified standardized processing mechanism. When dealing with structured data (detection index values, basic specimen information) and unstructured data (gene sequences, pathological images), format conflicts and data redundancy easily occur, significantly reducing the smoothness of the analysis process. Validation methods are simplistic, often relying on the same data source or repeated algorithms for result verification, lacking a closed-loop design for cross-database comparisons and cross-algorithm cross-validation, leading to insufficient reliability of validation results. Furthermore, system upgrades and iterations are difficult, unable to quickly adapt to new detection technologies and research needs. The overall technology faces multiple challenges, including data fragmentation, low validation accuracy, and a one-sided validation system, making it difficult to meet the stringent requirements of modern biomedicine for in-depth analysis and precise validation of specimen information. Summary of the Invention

[0004] This application provides a specimen bioinformatics analysis and verification system based on big data to solve the problems of data dispersion and low verification accuracy in the prior art.

[0005] The first aspect of this application provides a specimen bioinformatics analysis and verification system based on big data, comprising: a big data acquisition module, a bioinformatics analysis module, a multi-dimensional verification module, a species adaptation module, and a data storage and control module; wherein, the big data acquisition module acquires specimen morphological data, gene sequence data, and associated ecological environment data; the bioinformatics analysis module mines specimen feature association rules based on big data algorithms to generate analysis results on species classification, phylogenetic relationships, and characteristic evolution trends; the multi-dimensional verification module cross-validates the analysis results based on morphological comparison, gene sequence verification, and ecological habit matching dimensions, and outputs a verification report; the species adaptation module autonomously optimizes the analysis algorithm parameters and verification weight allocation based on the characteristics of different specimen groups, preservation status, and the verification report; and the cloud collaborative processing module stores the specimen multi-dimensional data and analysis and verification results in the cloud, monitors data quality in real time, dynamically adjusts storage resource allocation, and transmits the data back to the user terminal.

[0006] Preferably, the big data acquisition module includes a morphological data acquisition unit, a gene sequence data acquisition unit, and an ecological environment data acquisition unit. The morphological data acquisition unit is used to collect morphological characteristic parameters, organ structural details, and surface ornamentation data of the specimen. The gene sequence data acquisition unit is used to acquire nuclear gene, mitochondrial gene, or chloroplast gene sequence data of the specimen, and simultaneously record sequencing depth and quality values. The ecological environment data acquisition unit, by associating with meteorological databases, soil databases, and vegetation community databases of the specimen collection site, and combining field survey records, collects data on the climatic conditions, soil characteristics, and ecological niche of the specimen's native habitat.

[0007] Preferably, the bioinformatics analysis module includes an association rule mining unit, a species classification analysis unit, a phylogenetic relationship construction unit, and a feature evolution trend prediction unit. The association rule mining unit, based on machine learning algorithms, mines potential associations between morphological features, gene sequence variations, and ecological environmental factors. The species classification analysis unit, through feature extraction and clustering algorithms combined with a known species database, generates preliminary species classification results for the specimen. The phylogenetic relationship construction unit utilizes gene sequence homology comparison results to construct a phylogenetic tree, clarifying the phylogenetic relationship hierarchy between the specimen and closely related species. The feature evolution trend prediction unit, based on time series analysis and ancestral feature reconstruction algorithms, predicts the evolutionary direction and rate of target features.

[0008] Preferably, the multi-dimensional verification module includes a morphological comparison verification unit, a gene sequence verification unit, an ecological habit matching unit, and a cross-validation integration unit. The morphological comparison verification unit precisely compares the collected morphological data with a standard species morphology database and calculates a morphological similarity threshold. The gene sequence verification unit verifies the reliability of the gene sequence classification results through BLAST homology comparison and sequence consistency analysis. The ecological habit matching unit combines specimen ecological environment data with species niche characteristics to determine the ecological suitability of the classification results. The cross-validation integration unit integrates the verification results from the three dimensions and uses a weighted scoring method to generate a verification report containing verification conclusions, bias analysis, and confidence levels.

[0009] Preferably, the species adaptation module includes a specimen characteristic analysis unit, a preservation status assessment unit, an algorithm parameter optimization unit, and a verification weight allocation unit. The specimen characteristic analysis unit distinguishes the core characteristic differences between different groups of specimens, such as plants, animals, and microorganisms, through image recognition and data analysis. The preservation status assessment unit detects the specimen's integrity, degree of mold growth, and DNA degradation to determine the adaptation priority for data collection and analysis. The algorithm parameter optimization unit autonomously adjusts the feature extraction threshold, clustering distance parameter, and evolutionary model parameter based on the group characteristics and preservation status. The verification weight allocation unit dynamically adjusts the verification weight ratios of morphology, genes, and ecology dimensions based on the reliability of the verification dimensions for different groups of specimens.

[0010] Preferably, the cloud-based collaborative processing module includes a cloud storage unit, a data quality monitoring unit, a storage resource adjustment unit, and a user-end interaction unit. The cloud storage unit adopts a distributed storage architecture to classify and store multi-dimensional data on specimen morphology, genes, and ecology, as well as analysis and verification results. The data quality monitoring unit monitors data integrity, accuracy, and consistency in real time, triggering an alert when the data anomaly rate exceeds 5%. The storage resource adjustment unit dynamically allocates storage bandwidth and storage space based on data access frequency and data volume growth trends. The user-end interaction unit transmits analysis and verification results, data quality reports, and alert information back to the user end via an encrypted communication link, and supports users remotely submitting data supplementation requests.

[0011] The second aspect of this application provides a method for bioinformatics analysis and verification of specimens based on big data, comprising: acquiring specimen morphological data, gene sequence data, and associated ecological environment data; mining association rules of the specimen morphological data, gene sequence data, and associated ecological environment data based on big data algorithms to generate analysis results of species classification, phylogenetic relationships, and characteristic evolution trends; cross-validating the analysis results according to morphological comparison, gene sequence verification, and ecological habit matching dimensions, and outputting a verification report; simultaneously, autonomously optimizing the analysis algorithm parameters and verification weight allocation according to the characteristics and preservation status of different specimen groups; storing the multidimensional data of the specimens, the analysis and verification results, and the verification report in the cloud; monitoring data quality in real time based on the cloud platform, dynamically adjusting the allocation of storage resources, and transmitting the processing results back to the user terminal.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a big data-based specimen bioinformatics analysis and verification method as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a big data-based specimen bioinformatics analysis and verification method as described in the above embodiments.

[0014] The fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing a big data-based specimen bioinformatics analysis and verification method as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects: This application integrates multi-dimensional big data on morphology, gene sequences, and ecological environment. It leverages machine learning and big data algorithms to deeply mine the correlations of specimen features, combining morphological comparison, gene verification, and ecological matching for multi-dimensional cross-validation. This is supplemented by autonomous algorithm optimization and dynamic allocation of verification weights tailored to the characteristics and preservation status of different specimen groups. Furthermore, efficient collaborative interaction is achieved through cloud-based distributed storage, real-time data quality monitoring, and dynamic resource regulation. This significantly improves the accuracy and reliability of species classification, phylogenetic analysis, and characteristic evolutionary trend analysis. It also enhances the system's adaptability and flexibility to various specimen types, reduces redundant data processing and storage costs, and provides efficient, comprehensive, and reliable technical support for related species research and resource conservation. Thus, it solves the problems of data dispersion and low verification accuracy in existing technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the structure of a specimen bioinformatics analysis and verification system based on big data, according to an embodiment of this application. Figure 2 This is a schematic diagram of a specimen bioinformatics analysis and verification system based on big data, according to an embodiment of this application. Figure 3 A flowchart illustrating a big data-based specimen bioinformatics analysis and verification method according to an embodiment of this application; Figure 4 This is a schematic diagram of a specimen bioinformatics analysis and verification method based on big data according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The following description, with reference to the accompanying drawings, illustrates a specimen bioinformatics analysis and verification system based on big data, according to an embodiment of this application. Addressing the issue of low verification accuracy mentioned in the background section, this application provides a specimen bioinformatics analysis and verification system based on big data. This system integrates multi-dimensional big data on morphology, gene sequences, and ecological environment; leverages machine learning and big data algorithms to deeply mine the correlations of specimen features; combines multi-dimensional cross-validation of morphological comparison, gene verification, and ecological matching; and employs autonomous algorithm optimization and dynamic allocation of verification weights tailored to the characteristics and preservation status of different specimen groups. Furthermore, efficient collaborative interaction is achieved through cloud-based distributed storage, real-time data quality monitoring, and dynamic resource regulation. This not only significantly improves the accuracy and reliability of species classification, phylogenetic analysis, and characteristic evolutionary trend analysis but also enhances the system's adaptability and flexibility to various specimen types, reduces redundant costs in data processing and storage, and provides efficient, comprehensive, and reliable technical support for related species research and resource conservation. Thus, it solves the problems of data dispersion and low verification accuracy in existing technologies.

[0020] Figure 1This is a schematic diagram of the structure of a specimen bioinformatics analysis and verification system based on big data, provided in an embodiment of this application.

[0021] This application provides a specimen bioinformatics analysis and verification system based on big data. The system 10 includes: The system includes a big data acquisition module (100), a bioinformatics analysis module (200), a multi-dimensional verification module (300), a species adaptation module (400), and a data storage and control module (500).

[0022] The system comprises the following modules: a big data acquisition module 100, which collects morphological data, gene sequence data, and associated ecological environment data of specimens; a bioinformatics analysis module 200, which mines the association rules of specimen features based on big data algorithms, and generates analysis results on species classification, phylogenetic relationships, and characteristic evolution trends; a multi-dimensional verification module 300, which cross-validates the analysis results based on morphological comparison, gene sequence verification, and ecological habit matching, and outputs a verification report; a species adaptation module 400, which autonomously optimizes the analysis algorithm parameters and verification weight allocation based on the characteristics, preservation status, and verification reports of different groups of specimens; and a cloud-based collaborative processing module 500, which stores the multi-dimensional data of specimens and the analysis and verification results in the cloud, monitors data quality in real time, dynamically adjusts the allocation of storage resources, and transmits the data back to the user terminal.

[0023] It is understood that the embodiments of this application integrate multi-dimensional big data on morphology, gene sequences, and ecological environment, and leverage machine learning and big data algorithms to deeply mine the correlation of specimen features. This is combined with multi-dimensional cross-validation through morphological comparison, gene verification, and ecological matching, supplemented by autonomous algorithm optimization and dynamic allocation of verification weights tailored to the characteristics and preservation status of different specimen groups. Furthermore, efficient collaborative interaction is achieved through cloud-based distributed storage, real-time data quality monitoring, and dynamic resource regulation. This not only significantly improves the accuracy and reliability of species classification, phylogenetic analysis, and characteristic evolutionary trend analysis, but also enhances the system's adaptability and flexibility to various specimens, reduces redundant costs in data processing and storage, and provides efficient, comprehensive, and reliable technical support for related species research and resource conservation. Thus, it solves the problems of data dispersion and low verification accuracy in existing technologies.

[0024] In this embodiment, the big data acquisition module 100 includes: a morphological data acquisition unit, a gene sequence data acquisition unit, and an ecological environment data acquisition unit.

[0025] The morphological data acquisition unit is used to collect morphological characteristic parameters, organ structural details, and surface ornamentation data of the specimens; the gene sequence data acquisition unit is used to obtain nuclear gene, mitochondrial gene, or chloroplast gene sequence data of the specimens, and simultaneously record sequencing depth and quality values; the ecological environment data acquisition unit collects climatic conditions, soil characteristics, and niche data of the specimens' native habitat by linking meteorological databases, soil databases, and vegetation community databases of the specimen collection site, combined with field survey records.

[0026] It is understood that the embodiments of this application capture the external morphological details of the specimen through the morphological data acquisition unit, obtain the internal genetic information and sequencing quality through the gene sequence data acquisition unit, and combine the ecological environment data acquisition unit to associate the climate, soil and ecological niche information of the native environment. The core biological information of the specimen is captured from three dimensions: morphology, genetics and ecology, and a closely related multi-source dataset is constructed. This ensures the integrity and relevance of data acquisition, provides comprehensive and relevant basic data for subsequent bioinformatics analysis and multi-dimensional verification, and effectively improves the depth and accuracy of the system's analysis of the specimen's biological information.

[0027] For example, taking the collection of ecological and environmental data for a national first-class protected rare plant specimen (such as *Magnolia grandiflora*) as an example, the ecological and environmental data collection unit first accurately locates the latitude and longitude coordinates of the specimen collection site and links it to the long-term monitoring database of the local meteorological department. This not only obtains basic climate data such as annual average temperature, annual precipitation, and seasonal precipitation rhythm, but also simultaneously extracts key indicators such as extreme high and low temperature thresholds, frost-free period duration, and monthly relative humidity curves, comprehensively reconstructing the original habitat's climate characteristics. Subsequently, the system retrieves the soil resource survey database of the collection site. In addition to soil pH and organic matter content, it further obtains soil texture (sandy, loamy, or clayey), soil porosity, nutrient content such as nitrogen, phosphorus, and potassium, and background values ​​of heavy metal elements, clarifying the soil's supporting conditions for the plant's growth. Simultaneously, combined with the regional vegetation community survey database, it meticulously analyzes the associated plant species (such as *Magnolia yunnanensis* and *Bretschneidera sinensis*), community canopy closure, dominant species ratio, and vertical stratification structure, clarifying the ecological relationships between species. Finally, through on-site verification, staff supplemented the records with information on the specimen's native habitat, including altitude, slope (e.g., gentle slope of 25°-30°), aspect (northeast slope), actual duration of sunlight, and shading rate. They also simultaneously labeled the habitat type (evergreen broad-leaved forest along a stream in a valley) and the degree of human disturbance (far from residential areas, with no obvious signs of damage). This achieved multi-dimensional and high-precision integration of ecological data on the rare plant's native habitat. This data provides comprehensive support for subsequent analysis of the plant's niche suitability, species classification verification, and assessment of environmental stress factors, fully demonstrating the advantages of the ecological environment data collection unit's "multi-database linkage + on-site verification."

[0028] In this embodiment, the bioinformatics analysis module 200 includes: an association rule mining unit, a species classification analysis unit, a kinship construction unit, and a feature evolution trend prediction unit.

[0029] The association rule mining unit uses machine learning algorithms to mine potential associations between morphological features, gene sequence variations, and ecological environment factors; the species classification analysis unit uses feature extraction and clustering algorithms, combined with a known species database, to generate preliminary species classification results for the specimens; the phylogenetic relationship construction unit uses gene sequence homology comparison results to construct a phylogenetic tree and clarify the phylogenetic relationship hierarchy between the specimens and closely related species; and the feature evolution trend prediction unit uses time series analysis and ancestral feature reconstruction algorithms to predict the evolution direction and rate of target features.

[0030] It is understood that the embodiments of this application reveal the potential connections between morphology, genes and ecological factors through the association rule mining unit, generate preliminary classification results through the species classification analysis unit, clarify the kinship level between species through the kinship construction unit, and predict the evolutionary trend of features through the feature evolution prediction unit. By deeply analyzing and mining patterns in multi-dimensional collected data, a complete analysis chain is formed from feature association to classification, kinship and evolutionary trend. This not only improves the depth and efficiency of data analysis through machine learning and algorithm models, but also ensures the scientificity of classification, kinship and other results by combining known databases. At the same time, it provides forward-looking predictions for species evolution research, lays a precise and systematic analytical foundation for subsequent multi-dimensional verification and algorithm optimization, and effectively supports the comprehensive understanding and scientific interpretation of the biological information of specimens.

[0031] For example, taking a rare butterfly specimen as an example, the phylogenetic relationship construction unit first extracted the EF-1α gene sequence from its nuclear genome and performed BLAST homology comparison with homologous gene sequences of closely related butterflies (such as other genera and species in the same subfamily). The sequence identity percentage and genetic distance were calculated, and then a phylogenetic tree was constructed using Bayesian methods. The tree showed that the specimen had 98% support among branch nodes of a known species within the same genus, with the shortest branch distance, while the branch distances with closely related species were relatively large. This clearly defined its specific position on the evolutionary tree and its phylogenetic hierarchy with closely related groups, providing intuitive and reliable genetic evidence for subsequent verification of its species classification results and analysis of its evolutionary divergence time.

[0032] In this embodiment, the multi-dimensional verification module 300 includes: a morphological comparison verification unit, a gene sequence verification unit, an ecological habit matching unit, and a cross-validation integration unit.

[0033] The morphological comparison and verification unit accurately compares the collected morphological data with the standard species morphology database and calculates the morphological similarity threshold; the gene sequence verification unit verifies the reliability of the gene sequence classification results through BLAST homology comparison and sequence consistency analysis; the ecological habit matching unit combines the specimen's ecological environment data with the species' ecological niche characteristics to determine the ecological suitability of the classification results; and the cross-validation integration unit integrates the verification results from the three dimensions and uses a weighted scoring method to generate a verification report that includes verification conclusions, bias analysis, and confidence levels.

[0034] It is understood that the embodiments of this application use a morphological comparison verification unit to accurately match standard morphological data, a gene sequence verification unit to verify the reliability of genetic classification, and an ecological habit matching unit to determine ecological suitability. Then, a cross-validation integration unit uses a weighted scoring method to comprehensively generate a report containing verification conclusions, bias analysis, and confidence levels. This comprehensively cross-validates the previous bioinformatics analysis results from three key dimensions: morphology, genes, and ecology. This approach avoids the limitations of single-dimensional verification, improves the comprehensiveness and rigor of the verification results, and clarifies the reliability boundaries of the results through quantitative confidence levels and bias analysis. This provides a precise basis for the subsequent algorithm optimization of the species adaptation module and further ensures the scientific accuracy of the final output conclusions of the entire system.

[0035] For example, taking a freshwater fish specimen as an example, the gene sequence verification unit first extracts its mitochondrial COI gene sequence and compares it with homologous sequences of closely related groups in the fish species gene database using a BLAST tool. The calculation shows that the specimen's sequence has a 99.2% sequence identity with "XX Schizothorax" in the preliminary classification results, with a comparison coverage of 100%, while the sequence identity with other species in the same genus is below 95%. Simultaneously, through sequence variation site analysis, it was found that its unique variation sites completely match the "XX Schizothorax" type specimen, ultimately verifying the reliability of the previous species classification analysis results. If the sequence identity falls below the threshold, the unit simultaneously marks the differential sites and indicates potential classification bias, providing accurate evidence for subsequent corrections and fully demonstrating its rigorous verification role at the gene level.

[0036] In this embodiment, the species adaptation module 400 includes a specimen characteristic analysis unit, a preservation status evaluation unit, an algorithm parameter optimization unit, and a verification weight allocation unit.

[0037] The specimen characteristic analysis unit distinguishes the core feature differences of different groups of specimens, such as plants, animals, and microorganisms, through image recognition and data parsing; the preservation status assessment unit detects the integrity, mold degree, and DNA degradation of the specimens to determine the appropriate priority for data collection and analysis; the algorithm parameter optimization unit autonomously adjusts the feature extraction threshold, clustering distance parameter, and evolutionary model parameter according to the group characteristics and preservation status; and the verification weight allocation unit dynamically adjusts the verification weight ratio of morphology, genes, and ecology dimensions based on the reliability of the verification dimensions of different groups of specimens.

[0038] It is understood that the embodiments of this application distinguish the differences in core characteristics of groups such as plants, animals, and microorganisms through the specimen characteristic analysis unit, the preservation status assessment unit detects the specimen integrity, degree of mold, and DNA degradation to determine the adaptation priority, the algorithm parameter optimization unit adjusts key parameters such as feature extraction thresholds in a targeted manner, and the verification weight allocation unit dynamically adjusts the verification weight ratio of morphology, genes, and ecology. This enables the analysis algorithm and verification logic to be personalized to different groups and specimens in different preservation states, improves the flexibility of adaptation to various specimens, and makes the analysis and verification process more in line with the actual situation of the specimens. This not only ensures the relevance and efficiency of data processing, but also further enhances the accuracy of subsequent results.

[0039] For example, taking the simultaneous processing of three types of specimens—rare orchid plants, small beetles, and soil fungi—as an example, the specimen characteristic analysis unit, through high-precision image recognition technology and multi-dimensional data analysis, focuses on extracting core features such as leaf morphology parameters, floral symmetry structure, and lip ornamentation for plant specimens; for beetle specimens, it focuses on key indicators such as antennae type, wing vein branching pattern, and body surface puncture density; and for fungal specimens, it focuses on analyzing unique attributes such as colony morphology, spore shape and size, and hyphal structure. This clearly distinguishes the core feature differences between different groups of specimens—clarifying that orchid plants are mainly identified based on floral morphology, beetles are classified based on wing vein structure, and fungi are identified based on spore characteristics. This provides accurate group feature basis for subsequent algorithm parameter optimization and verification of dynamic weight allocation, fully demonstrating its fundamental support role in adapting to the analysis needs of different groups of specimens.

[0040] In this embodiment, the cloud collaborative processing module 500 includes a cloud storage unit, a data quality monitoring unit, a storage resource adjustment unit, and a user terminal interaction unit.

[0041] The cloud storage unit adopts a distributed storage architecture to classify and store multi-dimensional data on specimen morphology, genes, ecology, and analysis and verification results; the data quality monitoring unit monitors data integrity, accuracy, and consistency in real time, and triggers an alert when the data anomaly rate exceeds 5%; the storage resource adjustment unit dynamically allocates storage bandwidth and storage space according to the data access frequency and data volume growth trend; and the user terminal interaction unit transmits analysis and verification results, data quality reports, and alert information back to the user terminal through an encrypted communication link, and supports users to remotely submit data supplement requests.

[0042] It is understood that the embodiments of this application use a cloud storage unit to classify and store multidimensional data of specimens and analysis and verification results in a distributed architecture. The data quality monitoring unit detects the integrity, accuracy and consistency of data in real time and triggers an alert when the anomaly rate exceeds 5%. The storage resource adjustment unit dynamically allocates storage bandwidth and space according to access frequency and data growth trend. The user terminal interaction unit transmits relevant results, reports and alert information back through encrypted links and supports remote data supplementation requests, realizing cloud-based collaborative management and control of specimen data and system operation. This not only ensures the security, orderliness and traceability of multi-source data storage, but also avoids the risk of data deviation through real-time quality alerts, improves storage utilization efficiency through dynamic resource adjustment, and lowers the operating threshold through convenient user interaction.

[0043] For example, taking the data processing scenario of a regional animal and plant specimen survey as an example, the storage resource adjustment unit, through real-time monitoring of the system's data access dynamics and growth trends, discovered that: gene sequence data, due to its frequent use for kinship comparison and classification verification, had an access frequency of over 200 times per day, with a monthly data volume increase of 15%. The unit then automatically allocated twice the storage bandwidth and an additional 30% storage space to ensure the response speed and data storage capacity for high-frequency access; while the specimen ecological environment historical database (such as soil background data collected 10 years ago) had a low access frequency (less than 10 times per day) and a stable data volume. The unit optimized space usage through a compression storage algorithm, freeing up approximately 20% of storage space for allocation to the frequently used morphological image database. When the collection volume of rare species specimen data surged during a certain period (the daily increase in data volume exceeded 3 times the normal amount), the unit also temporarily expanded cache resources to avoid storage congestion. This not only achieved on-demand allocation and efficient utilization of storage resources but also ensured the stability of data storage and access for different types of data, fully demonstrating the core value of dynamic regulation.

[0044] This application proposes a specimen bioinformatics analysis and verification system based on big data. By integrating multi-dimensional big data on morphology, gene sequences, and ecological environment, and leveraging machine learning and big data algorithms to deeply mine the correlations of specimen characteristics, it combines multi-dimensional cross-validation of morphological comparison, gene verification, and ecological matching. Supplemented by autonomous algorithm optimization and dynamic allocation of verification weights tailored to the characteristics and preservation status of different specimen groups, and achieving efficient collaborative interaction through cloud-based distributed storage, real-time data quality monitoring, and dynamic resource regulation, this system not only significantly improves the accuracy and reliability of species classification, phylogenetic analysis, and characteristic evolutionary trend analysis, but also enhances the system's adaptability and flexibility to various specimen types, reduces redundant costs in data processing and storage, and provides efficient, comprehensive, and reliable technical support for related species research and resource conservation. Thus, it solves the problems of data dispersion and low verification accuracy in existing technologies.

[0045] The following will illustrate a big data-based specimen bioinformatics analysis and verification system through a specific embodiment, such as... Figure 2 As shown, it includes: Using a survey project of rare and endangered flora and fauna specimens in Southwest China as an application scenario, and addressing the region's rich diversity of plant, animal, and microbial specimens, the narrow distribution of some species, and significant differences in specimen preservation conditions, a big data-based specimen bioinformatics analysis and verification system was deployed. This system enables accurate information analysis, classification verification, and data management for over 300 specimens. The system follows a core logic of "data acquisition - in-depth analysis - multi-dimensional verification - personalized adaptation - cloud collaboration," with each module working closely together to solve problems such as single data dimensions, insufficient classification accuracy, and poor adaptability in traditional specimen analysis. This provides technical support for regional biodiversity conservation, species resource surveys, and evolutionary research.

[0046] The big data acquisition module, as the core of the system's data input, completes multi-dimensional information capture through three main units. For rare plant specimens (such as *Magnolia grandiflora* and *Magnolia yunnanensis*), the morphological data acquisition unit uses a high-precision 3D scanner and microscopic imaging system to acquire 12 morphological characteristic parameters, including leaf aspect ratio, floral symmetry, and lip ornamentation details, with a resolution of 0.01 mm, while simultaneously recording organ integrity and surface damage. The gene sequence data acquisition unit extracts the nuclear ITS gene sequence and the mitochondrial cox1 gene sequence using PCR amplification technology, and completes sequencing using a next-generation sequencing platform, simultaneously recording sequencing depth (average ≥100×) and quality value (Q30 ≥ 90%) to ensure the reliability of the gene data. Ecological... The environmental data acquisition unit locates the specimen collection site by latitude and longitude, links it to the local meteorological department's 50-year long-term monitoring database, and obtains climate data such as annual average temperature (15.2-18.6℃) and annual precipitation (1200-1800mm). It retrieves soil characteristic parameters such as pH value (5.5-6.8) and organic matter content (1.5%-3.2%) from the soil survey database, and combines them with the vegetation community database to identify the associated plant species (such as Bretschneidera sinensis and Emmenopterys henryi) and community canopy closure (0.7-0.9). Finally, it supplements the field information such as altitude (1200-1800m) and slope aspect (northeast slope) through field verification. For small beetle specimens, morphological collection focuses on antennae type, wing vein branching pattern, and body surface puncture density. Gene sequence collection focuses on the mitochondrial COI gene. Ecological environment data emphasizes habitat type (broadleaf forest litter layer) and temperature and humidity adaptability range. For soil fungal specimens, the focus is on collecting colony morphology, spore shape and size, and hyphal structure. The gene sequence is selected from the ITS2 fragment. Ecological data is linked to the soil microbial community database and humus content distribution information to achieve comprehensive coverage of three-dimensional data of "morphology-genes-ecology" for different groups of specimens.

[0047] The bioinformatics analysis module, based on collected multi-source data, performs in-depth analysis and pattern mining through four main units. The association rule mining unit uses a random forest machine learning algorithm, trained on over 100,000 data samples, to discover characteristic association rules such as "elongated elliptical leaves + specific ITS sequence variation sites + annual average temperature 16-17℃" and "wing vein branch number ≥8 + COI sequence homology 98% + humus content ≥2.0%", clarifying the intrinsic relationship between morphological characteristics, genetic variation, and ecological factors. The species classification analysis unit extracts core features using principal component analysis, combines it with K-means clustering, and compares it with a database containing over 50,000 known species to generate preliminary specimen classification results. For example, an unknown plant specimen is preliminarily identified as *Magnolia denudata* (89% confidence level), and a beetle specimen is preliminarily classified as a closely related species of the Golden Birdwing Butterfly (91% confidence level). The phylogenetic relationship construction unit used MEGA software to perform homology comparisons on gene sequence data and employed Bayesian methods to construct phylogenetic trees, clarifying the phylogenetic hierarchy of target specimens and closely related species. For example, the branch node support rate between the unknown *Magnolia denudata* specimen and the type species *Magnolia denudata* reached 98%, and the branch distance with *Magnolia yunnanensis* was 0.03, clearly defining its evolutionary position. The characteristic evolution trend prediction unit, based on time series analysis and ancestral feature reconstruction algorithms, combined with regional geological history and climate change data, predicted that the leaf area of ​​*Magnolia denudata* showed a gradual increasing trend, with an evolutionary rate of 3.2% per million years. The evolutionary direction of the number of wing spots in *Papilio yunnanensis* was positively correlated with habitat vegetation cover, providing a forward-looking reference for species evolution research.

[0048] The multi-dimensional verification module and the species adaptation module form a collaborative mechanism to improve the accuracy of the analysis results and the system adaptability. In the multi-dimensional verification module, the morphological comparison verification unit compares the collected morphological data with the standard species morphological database point by point and calculates the morphological similarity threshold. For example, the morphological similarity between the unknown *Magnolia denudata* specimen and the *Magnolia denudata* in the standard database reaches 96.5%, meeting the set threshold requirement of 90%. The gene sequence verification unit performs homology comparison using the BLAST tool. The nuclear gene ITS sequence of this specimen has 99.3% identity with the sequence of the *Magnolia denudata* type species, with 100% coverage and no differences in key variation sites, verifying the genetic reliability of the classification results. The ecological habit matching unit combines its ecological environment data to determine that the climate and soil conditions of the specimen's native habitat are completely compatible with the ecological niche characteristics of *Magnolia denudata*, and there is no ecological exclusion. The cross-validation integration unit uses a weighted scoring method, assigning weights of 30%, 50%, and 20% to the morphological, genetic, and ecological dimensions, respectively. The final confidence level of the specimen classification result is calculated to be 97.2%, generating a validation report that concludes "the classification result is reliable and has no significant bias." The species adaptation module, based on the characteristics of different taxa, distinguishes the core differences in features between plants, animals, and microorganisms through the specimen characteristic analysis unit—clarifying that plants are primarily identified by floral morphology, animals by wing veins and antennae, and microorganisms by spore and hyphal structures. The preservation status assessment unit detected mild mold (≤15%) and slight DNA degradation (≤8%) in some early-collected specimens, setting them as secondary adaptation priority to ensure efficient processing of specimens with higher data integrity. The algorithm parameter optimization unit adjusts the feature extraction threshold for moldy specimens, increasing the sensitivity of morphological feature recognition by 20%, and optimizes the evolutionary model parameters for DNA-degraded specimens to reduce the sequence variation error rate. The verification weight allocation unit increases the gene dimension weight to 60% for microbial specimens and appropriately increases the morphological dimension weight to 40% for morphologically intact plant specimens, achieving personalized adaptation of the algorithm and verification logic.

[0049] The cloud-based collaborative processing module ensures stable system operation and efficient data management, enabling centralized control and convenient interaction of multi-source data. The cloud storage unit employs a distributed storage architecture, categorizing and storing specimen morphological images, gene sequence files, ecological environment data tables, and analysis and verification reports. Morphological data is saved using a lossless compression format, gene sequence data is associated with index tags, and ecological data is stored in partitions according to collection location and species type, ensuring efficient data retrieval. The data quality monitoring unit monitors data integrity (≥95%), accuracy (error ≤3%), and consistency in real time. When the abnormality rate of gene sequence data in a batch of fungal specimens reaches 6.2% (exceeding the 5% threshold), the system immediately triggers an alert, indicating "Sequencing quality is substandard; re-collection is required." The storage resource adjustment unit dynamically allocates resources based on data access. Gene sequence data, frequently used for kinship comparisons with an average daily access volume of 230 times and a monthly data volume increase of 16%, is allocated twice the storage bandwidth and an additional 35% storage space. Meanwhile, soil background data collected 10 years ago has a lower access frequency (averaging 8 times per day), so 25% of storage space is freed up for the frequently used morphological image database through compression. The user-end interaction unit transmits analysis and verification results, data quality reports, and early warning information in real time to the survey team's mobile and PC terminals via encrypted communication links. It supports users submitting data supplement requests remotely—for example, for fungal specimens with early warnings, staff can upload re-sequencing data online, and the system automatically triggers a secondary analysis process. Simultaneously, users can query the complete information chain of a specimen through the client, including data collection, analysis process, verification reports, and storage status, achieving full lifecycle traceability management of specimen information.

[0050] In summary, the embodiments of this application achieve multiple core beneficial effects through comprehensive acquisition of three-dimensional data (morphology-genetics-ecology), machine learning-driven deep analysis, collaborative operation of multi-dimensional cross-validation and personalized algorithm adaptation, and cloud-based distributed storage and dynamic management mechanisms. These effects effectively address the pain points of traditional specimen analysis, such as single data dimensions, insufficient classification accuracy, and poor adaptability to specimens of different taxa and preservation states, significantly improving the accuracy of species classification (up to 97.8%) and the reliability of phylogenetic relationship determination (average node support rate ≥95%). Furthermore, through feature association rule mining and evolutionary trend prediction, the system provides forward-looking scientific evidence for species evolution research. Simultaneously, the system dynamically allocates resources... With its matching, real-time data quality early warning, and remote interaction functions, the system significantly improves the efficiency of the census, reduces manpower and time costs, and achieves standardized management and traceability of specimen information throughout its entire lifecycle. Furthermore, its personalized adaptation mechanism accommodates the differences in characteristics of different groups of specimens, such as plants, animals, and microorganisms, and is compatible with the processing needs of specimens in different preservation states. The generated precise bioinformatics database provides solid data support for regional biodiversity conservation, habitat management of rare species, and population dynamic monitoring. Its core logic and technical solutions also provide replicable and efficient solutions for specimen resource censuses in other regions and for different groups, fully demonstrating the application value of big data technology in the field of biological specimen information analysis.

[0051] Next, referring to the accompanying drawings, a specimen bioinformatics analysis and verification method based on big data is described according to an embodiment of this application.

[0052] like Figure 3 As shown, this big data-based specimen bioinformatics analysis and verification method includes the following steps: In step S101, specimen morphological data, gene sequence data, and associated ecological environment data are obtained.

[0053] It is understood that this application embodiment constructs a complete three-dimensional specimen bioinformatics dataset integrating "morphology-phenotype-habitat" by acquiring specimen morphological data, gene sequence data, and associated ecological environment data, thus laying a solid data foundation for subsequent full-process analysis and verification. This not only breaks through the limitations of traditional single-dimensional data collection, ensuring the comprehensiveness and relevance of information coverage, but also provides diverse and reliable data support for subsequent feature association rule mining, species classification, phylogenetic relationship construction, and multi-dimensional verification by accurately capturing the external morphological characteristics, internal genetic information, and native habitat adaptation conditions of specimens. This ensures the scientific, accurate, and comprehensive nature of subsequent analysis results from the source, and lays a core data foundation for revealing the intrinsic connection between specimen characteristics and the ecological environment and predicting species evolution trends.

[0054] In step S102, association rules are mined from specimen morphological data, gene sequence data, and related ecological environment data based on big data algorithms to generate analysis results on species classification, phylogenetic relationships, and characteristic evolution trends.

[0055] Big data algorithms refer to a class of computational methods that use statistical analysis, machine learning, and other technologies to uncover potential patterns, extract core value, and support decision-making for massive, multi-source data.

[0056] It is understood that the embodiments of this application deeply integrate multi-source data on specimen morphology, gene sequence, and ecological environment by using big data algorithms to mine potential correlation rules among the three and transform them into concrete analysis results of species classification, kinship, and characteristic evolution trends. This not only breaks through the limitations of data fragmentation and difficulty in finding patterns in traditional analysis, but also improves the efficiency and depth of data analysis through statistical analysis and machine learning, ensuring the accuracy of classification results, the reliability of kinship, and the foresight of evolution trend prediction. It also provides a clear analytical basis for subsequent multi-dimensional verification and algorithm parameter optimization, and helps to reveal the intrinsic connection between species characteristics and genetic and ecological factors, providing scientific support for related research and decision-making.

[0057] For example, taking the analysis of an unknown orchid specimen as an example, the random forest algorithm was used to perform multi-source fusion training on its morphological data (such as petal aspect ratio and lip ornamentation density), gene sequence data (nuclear gene ITS mutation sites), and ecological environment data (native habitat annual average temperature and soil pH). This process uncovered a feature association rule: "lip ornamentation density ≥ 5 lines / mm² + specific base mutations in the ITS sequence + annual average temperature 16-18℃". Simultaneously, the K-means clustering algorithm was used to compare its core features with a database of known orchid species, initially classifying it as a closely related species to *Cymbidium goeringii*. Then, a Bayesian algorithm was used to analyze gene sequence homology, constructing a phylogenetic tree to clarify its phylogenetic distance (0.02) from *Cymbidium goeringii*. Finally, an analysis result was generated, including species classification suggestions, phylogenetic hierarchy, and the "petal widening" evolutionary trend. This process achieved deep correlation and pattern extraction of multi-dimensional data through big data algorithms, overcoming the limitations of single-data type analysis and improving the scientific rigor and forward-looking nature of the results through precise calculations of the algorithm model, providing a clear analytical basis for subsequent verification.

[0058] In step S103, the analysis results are cross-validated based on morphological alignment, gene sequence verification, and ecological habit matching, and a validation report is output. At the same time, the analysis algorithm parameters and validation weight allocation are automatically optimized according to the characteristics and preservation status of different groups of specimens.

[0059] It is understood that the embodiments of this application perform multi-dimensional cross-validation analysis of the results through morphological comparison, gene sequence verification, and ecological habit matching, and output a verification report containing core conclusions. At the same time, based on the characteristics and preservation status of different groups of specimens, the analysis algorithm parameters and verification weight allocation are autonomously optimized to construct a "verification-adaptation" collaborative mechanism. This not only breaks the limitations of single-dimensional verification through multi-dimensional cross-validation, greatly improving the rigor and reliability of the analysis results, but also enhances the adaptability to different groups of specimens and different preservation statuses through personalized parameter adjustment and weight allocation, making the analysis and verification process more in line with the actual situation of the specimens. Meanwhile, the output verification report provides a clear basis for the application of results and subsequent optimization, ensuring the scientificity, accuracy and practical value of the overall method from the process perspective.

[0060] In step S104, the multidimensional data of the specimen, the analysis and verification results, and the verification report are stored in the cloud. The data quality is monitored in real time based on the cloud platform, the storage resource allocation is dynamically adjusted, and the processing results are sent back to the user terminal.

[0061] Among them, multidimensional specimen data refers to multi-source associated data collected around the specimen, covering morphological characteristics, gene sequence information and related ecological environment conditions, which can comprehensively reflect the biological attributes and survival background of the specimen.

[0062] It is understood that the embodiments of this application utilize multidimensional data of specimens and combine analysis and verification results and verification reports for cloud storage and management, thereby achieving centralized integration, full lifecycle management and efficient interactive sharing of specimen-related data. This not only provides a complete basis for users' subsequent data queries, traceability and secondary analysis by leveraging multidimensional data to comprehensively reflect the biological attributes and survival background of specimens, but also avoids data deviation risks through real-time quality monitoring in the cloud, improves storage utilization efficiency through dynamic resource allocation, and quickly transmits processing results back to the user end to lower the operational threshold, providing convenient and accurate data support for relevant research and decision-making.

[0063] The present application proposes a big data-based specimen bioinformatics analysis and verification method. This method integrates multi-dimensional big data on morphology, gene sequences, and ecological environment, leverages machine learning and big data algorithms to deeply mine the correlations of specimen features, and combines multi-dimensional cross-validation of morphological comparison, gene verification, and ecological matching. It further employs autonomous algorithm optimization and dynamic allocation of verification weights tailored to the characteristics and preservation status of different specimen groups. Furthermore, efficient collaborative interaction is achieved through cloud-based distributed storage, real-time data quality monitoring, and dynamic resource regulation. This significantly improves the accuracy and reliability of species classification, phylogenetic analysis, and characteristic evolutionary trend analysis, while also enhancing the system's adaptability and flexibility to various specimen types. It reduces redundant costs in data processing and storage, providing efficient, comprehensive, and reliable technical support for related species research and resource conservation. This solves the problems of data dispersion and low verification accuracy in existing technologies.

[0064] The following will illustrate a method for validating specimen bioinformatics analysis based on big data through a specific embodiment, such as... Figure 4 As shown, it includes: Taking the special survey of rare and endangered flora and fauna specimens in the Hengduan Mountains of Southwest China as the application scenario, this region, with its complex terrain and diverse climate, is home to over 300 species of rare and endangered flora and fauna, including *Magnolia denudata*, *Papilio yunnanensis*, and *Civet yunnanensis*, as well as a large number of endemic microorganisms. Furthermore, some specimens were collected in earlier years, resulting in uneven preservation and incomplete data records. To address this, a big data-based specimen bioinformatics analysis and verification scheme was adopted, following a complete workflow logic of "data collection - algorithm analysis - multi-dimensional verification - adaptation and optimization - cloud management." This scheme streamlines the entire data acquisition and application process, resolving pain points in traditional surveys such as ambiguous classification, scattered data, and limited verification, providing standardized technical support for regional biodiversity conservation, species resource documentation, and evolutionary research.

[0065] Multidimensional data from specimens were acquired through multi-source collection methods, laying a solid foundation for subsequent analysis. For rare plant specimens (such as *Magnolia grandiflora*), the morphological data acquisition unit employed 4K microscopic imaging and 3D laser scanning technology to accurately capture 15 core parameters, including leaf aspect ratio, floral symmetry, and lip ornamentation density, with a resolution of 0.005 mm. Simultaneously, the integrity of the specimen organs and the degree of surface damage were recorded. The gene sequence data acquisition unit extracted the nuclear ITS gene sequence and the mitochondrial cox1 gene fragment using the CTAB method. After PCR amplification, sequencing was performed using the Illumina sequencing platform, simultaneously recording sequencing depth (average ≥120×) and quality value (Q30 ≥ 92%) to ensure the integrity of genetic information. Ecological and environmental data... The collection unit relies on the latitude and longitude coordinates of the specimen collection site and links it to the local meteorological department's 60-year monitoring database to obtain climate data such as annual average temperature (14.8-19.2℃), annual precipitation (1100-1900mm), and seasonal distribution of precipitation. It also retrieves parameters such as pH value (5.2-7.0), organic matter content (1.8%-3.5%), and soil texture from the soil survey database. Combined with the regional vegetation community database, it identifies associated plants (such as Bretschneidera sinensis and Cathaya argyrophylla) and community canopy closure (0.65-0.95). Finally, it supplements the field information such as altitude (1100-2000m), slope aspect (mainly northwest slope), and sunshine duration through field verification. For animal specimens such as the Golden Birdwing Butterfly, morphological collection focuses on key characteristics such as wing vein branching patterns, antennae type, and density of punctures on the body surface. Gene sequences are based on the mitochondrial COI gene, and ecological data emphasizes habitat type (forest edge of evergreen broad-leaved forest) and temperature and humidity tolerance range. For soil fungal specimens, the collection focuses on unique attributes such as colony morphology, spore size and shape, and hyphal structure. The gene sequence is selected from the ITS2 fragment, and ecological data is associated with soil microbial community composition and humus content, achieving comprehensive coverage of three-dimensional data of "morphology-genes-ecology" for different groups of specimens.

[0066] Based on the collected multi-source data, big data algorithms were used to deeply mine data association rules and generate core analysis results. The random forest algorithm was used to train on over 120,000 collected multi-source data samples, integrating morphological features, gene mutation sites, and ecological factors to uncover strongly correlated feature rules such as "no spots on the lip of the flower + specific base mutation in the ITS sequence + average annual temperature of 16-17.5℃" and "number of wing vein branches = 10 + COI sequence consistency 97% + humus content ≥ 2.2%", clearly revealing the intrinsic connections between the three. The K-means clustering algorithm was used to extract the core feature vectors of the specimens, and similarity comparisons were performed with an authoritative database containing over 80,000 known species to generate preliminary species classification results—for example, a certain unknown plant specimen was preliminarily identified as *Magnolia denudata* (90% confidence), and a certain butterfly specimen was classified as the nominate subspecies of *Papilio macrantha* (93% confidence). In the phylogenetic relationship construction phase, MEGA software was used to perform homology comparison of gene sequences, and a phylogenetic tree was constructed using Bayesian method. The results showed that the branch node support rate between the unknown *Magnolia yunnanensis* specimen and the type species *Magnolia yunnanensis* reached 99%, and the branch distance with the closely related species *Magnolia yunnanensis* was 0.025, clarifying its evolutionary position. In the characteristic evolution trend prediction phase, combined with the geological history and climate change data of the region, time series analysis and ancestral feature reconstruction algorithms were used to predict that the leaf width of *Magnolia yunnanensis* showed a gradual increasing trend, with an evolutionary rate of 4.1% per million years. The wing spot area of ​​the Golden Birdwing Butterfly showed a positive correlation with the vegetation cover of its habitat, providing a forward-looking scientific basis for species evolution research.

[0067] Based on the generated core analysis results, the accuracy and flexibility of the results were further improved through multi-dimensional cross-validation and personalized adaptation optimization. In the multi-dimensional validation phase, the morphological comparison validation unit compared 15 morphological parameters of the unknown *Magnolia denudata* specimen with the standard species morphological database point by point, calculating a morphological similarity of 97.3%, higher than the set 90% threshold. The gene sequence verification unit performed homology analysis using the BLAST tool, showing that the nuclear gene ITS sequence of this specimen was 99.5% identical to the *Magnolia denudata* type species sequence, with 100% coverage and no differences in key variation sites, verifying the genetic reliability of the classification results. The ecological habit matching unit, combined with its native habitat data, confirmed that the climate, soil conditions, and ecological niche requirements of *Magnolia denudata* were completely compatible, with no ecological adaptation conflicts. The cross-validation integration unit used a weighted scoring method, assigning weights of 30%, 50%, and 20% to the morphological, gene, and ecological dimensions, respectively, and calculated the final confidence level of the specimen's classification results to be 98.1%, generating a formal report including validation conclusions, bias analysis, and confidence level rating. The adaptation and optimization phase, tailored to the characteristics of different specimen groups, uses image recognition and data analysis to clarify that plants are identified primarily based on floral morphology, animals on wing veins and antennae features, and microorganisms on spore and hyphal structures. The preservation status assessment unit detected 32 specimens collected in earlier years with mild mold (mold level ≤18%) and slight DNA degradation (degradation rate ≤10%), assigning them a secondary processing priority to ensure efficient analysis of specimens with complete data. The algorithm parameter optimization unit increased the sensitivity of morphological feature recognition by 25% for moldy specimens and optimized evolutionary model parameters for DNA-degraded specimens, reducing the tolerance for sequence variation. The validation weight allocation unit increased the gene dimension weight to 65% for microbial specimens and adjusted the morphological dimension weight to 40% for morphologically intact plant specimens, achieving personalized adaptation of the analysis algorithm and validation logic.

[0068] After analysis, verification, and optimization, centralized data management and efficient interaction are achieved through cloud-based collaborative processing, providing stable assurance for overall operation. The cloud storage unit adopts a distributed architecture, classifying and storing specimen morphological images, gene sequence files, ecological environment data tables, and analysis and verification reports by type. Morphological data uses a lossless compression format to save space, gene sequence data is associated with unique index tags, and ecological data is managed by collection location and species type, significantly improving data retrieval efficiency. The data quality monitoring unit monitors data integrity (≥96%), accuracy (error ≤2.5%), and consistency in real time. When the abnormality rate of gene sequence data in a batch of fungal specimens reaches 5.8% (exceeding the 5% threshold), a red alert is immediately triggered, and a notification stating "Sequencing quality is substandard; re-collection is recommended" is simultaneously pushed to the staff's terminal. The storage resource adjustment unit dynamically and intelligently allocates resources based on data access. Gene sequence data, frequently used for kinship comparisons with an average daily access volume of 250 times and a monthly data volume increase of 18%, is automatically allocated 2.2 times the storage bandwidth and an additional 40% storage space. Meanwhile, soil background data collected 15 years ago has a low access frequency (an average of 6 times per day), so 30% of its storage space is freed up through compression and allocated to the frequently used morphological image database. The user-end interaction unit transmits analysis and verification results, data quality reports, and early warning information back to the survey team's mobile and PC terminals in real time via encrypted communication links, supporting remote submission of data supplementation requests—staff can upload re-sequencing fungal gene data online, automatically triggering a secondary analysis process. Simultaneously, users can query the complete information chain of specimens through the client, ensuring full traceability from data collection and analysis to verification reports and storage status, achieving standardized management of specimen information throughout its entire lifecycle.

[0069] In summary, this application's embodiments utilize comprehensive three-dimensional multi-source data acquisition across morphology, genes, and ecology, coupled with high-precision equipment for refined capture of specimen morphological parameters, gene sequences, and ecological factors (morphological resolution up to 0.005 mm, gene sequencing quality value Q30 ≥ 92%), and all-round identification of the characteristics of specimens from different groups and preservation states, providing massive and accurate data for analysis and verification. After preprocessing with big data algorithms (random forest mining for feature association, K-means clustering for core vector extraction), combined with multi-dimensional cross-validation (morphological comparison, gene verification, ecological matching) and personalized adaptation optimization (algorithm parameter adjustment, verification weight allocation), the classification confidence can be quantified in real time and biases can be accurately corrected (final confidence reaches 98.1%), laying a data and algorithmic foundation for the reliability of the results. The dynamic adaptation mechanism optimizes the verification logic through weighted scoring and feature priority ranking. It precisely adjusts the weighting of morphological, genetic, and ecological dimensions based on specimen group characteristics and preservation status, maintaining a high species classification accuracy of 98.3% and an average support rate of ≥96% for kinship construction nodes. In a practical survey in the Hengduan Mountains of Southwest China, the solution completed the analysis of 326 specimens in only 60% of the time of traditional processes, achieving accurate classification of 32 specimens with early mold and DNA degradation, fully validating its high adaptability under complex specimen conditions. Simultaneously, dynamic cloud resource allocation reduces data storage waste, and full lifecycle traceability ensures standardized data management. Ultimately, it achieves closed-loop management from data collection to result application, comprehensively improving the accuracy, efficiency, and scalability of specimen surveys, providing solid technical support for biodiversity conservation.

[0070] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0071] When the processor 502 executes the program, it implements a specimen bioinformatics analysis and verification method based on big data provided in the above embodiments.

[0072] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0073] The memory 501 is used to store computer programs that can run on the processor 502.

[0074] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0075] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0076] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0077] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0078] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for bioinformatics analysis and verification of specimens based on big data.

[0079] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described method for big data-based specimen bioinformatics analysis and verification.

[0080] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0082] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0083] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0084] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0085] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A specimen bioinformatics analysis and verification system based on big data, characterized in that, include: The system includes a big data acquisition module, a bioinformatics analysis module, a multi-dimensional verification module, a species adaptation module, and a data storage and regulation module; among which, The big data acquisition module collects specimen morphological data, gene sequence data, and associated ecological and environmental data. The bioinformatics analysis module mines the association rules of specimen features based on big data algorithms, and generates analysis results on species classification, phylogenetic relationships and feature evolution trends; The multi-dimensional verification module performs cross-validation on the analysis results based on morphological comparison, gene sequence verification, and ecological habit matching, and outputs a verification report. The species adaptation module autonomously optimizes the analysis algorithm parameters and verification weight allocation based on the characteristics of different taxa specimens, their preservation status, and the verification report. The cloud-based collaborative processing module stores multidimensional data of the specimens and analysis and verification results in the cloud, monitors data quality in real time, dynamically adjusts the allocation of storage resources, and sends them back to the user end.

2. The specimen bioinformatics analysis and verification system based on big data according to claim 1, characterized in that, The big data acquisition module includes a morphological data acquisition unit, a gene sequence data acquisition unit, and an ecological environment data acquisition unit. The morphological data acquisition unit is used to collect morphological characteristic parameters, organ structural details, and surface ornamentation data of the specimen. The gene sequence data acquisition unit is used to acquire nuclear gene, mitochondrial gene, or chloroplast gene sequence data of the specimen, and simultaneously record sequencing depth and quality values. The ecological environment data acquisition unit collects climatic conditions, soil characteristics, and niche data of the specimen's native habitat by linking meteorological databases, soil databases, and vegetation community databases of the specimen collection site, combined with field survey records.

3. The specimen bioinformatics analysis and verification system based on big data according to claim 1, characterized in that, The bioinformatics analysis module includes an association rule mining unit, a species classification analysis unit, a phylogenetic relationship construction unit, and a feature evolution trend prediction unit. The association rule mining unit, based on machine learning algorithms, mines potential associations between morphological features, gene sequence variations, and ecological environmental factors. The species classification analysis unit, through feature extraction and clustering algorithms combined with a known species database, generates preliminary species classification results for the specimen. The phylogenetic relationship construction unit utilizes gene sequence homology comparison results to construct a phylogenetic tree, clarifying the phylogenetic relationship hierarchy between the specimen and closely related species. The feature evolution trend prediction unit, based on time series analysis and ancestral feature reconstruction algorithms, predicts the evolutionary direction and rate of target features.

4. The specimen bioinformatics analysis and verification system based on big data according to claim 1, characterized in that, The multi-dimensional verification module includes a morphological comparison verification unit, a gene sequence verification unit, an ecological habit matching unit, and a cross-validation integration unit. The morphological comparison verification unit precisely compares the collected morphological data with a standard species morphology database and calculates a morphological similarity threshold. The gene sequence verification unit verifies the reliability of the gene sequence classification results through BLAST homology comparison and sequence consistency analysis. The ecological habit matching unit combines specimen ecological environment data with species niche characteristics to determine the ecological suitability of the classification results. The cross-validation integration unit integrates the verification results from the three dimensions and uses a weighted scoring method to generate a verification report that includes verification conclusions, bias analysis, and confidence levels.

5. The specimen bioinformatics analysis and verification system based on big data according to claim 1, characterized in that, The species adaptation module includes a specimen characteristic analysis unit, a preservation status assessment unit, an algorithm parameter optimization unit, and a verification weight allocation unit. The specimen characteristic analysis unit distinguishes the core characteristic differences between different groups of specimens, such as plants, animals, and microorganisms, through image recognition and data parsing. The preservation status assessment unit detects the specimen's integrity, degree of mold growth, and DNA degradation to determine the adaptation priority for data collection and analysis. The algorithm parameter optimization unit autonomously adjusts the feature extraction threshold, clustering distance parameter, and evolutionary model parameter based on the group characteristics and preservation status. The verification weight allocation unit dynamically adjusts the verification weight ratios of morphology, genes, and ecology dimensions based on the reliability of the verification dimensions for different groups of specimens.

6. The specimen bioinformatics analysis and verification system based on big data according to claim 1, characterized in that, The cloud-based collaborative processing module includes a cloud storage unit, a data quality monitoring unit, a storage resource adjustment unit, and a user-end interaction unit. The cloud storage unit employs a distributed storage architecture to categorize and store multi-dimensional data on specimen morphology, genes, and ecology, as well as analysis and verification results. The data quality monitoring unit monitors data integrity, accuracy, and consistency in real time, triggering an alert when the data anomaly rate exceeds 5%. The storage resource adjustment unit dynamically allocates storage bandwidth and storage space based on data access frequency and data volume growth trends. The user-end interaction unit transmits analysis and verification results, data quality reports, and alert information back to the user end via an encrypted communication link and supports remote data supplementation requests from users.

7. A method for applying a specimen bioinformatics analysis and verification system based on big data as described in any one of claims 1-6, characterized in that, The method includes: Acquire specimen morphological data, gene sequence data, and associated ecological and environmental data; Based on big data algorithms, association rules are mined from the morphological data, gene sequence data, and related ecological environment data of the specimens to generate analysis results on species classification, phylogenetic relationships, and characteristic evolution trends; Based on morphological comparison, gene sequence verification, and ecological habit matching, the analysis results are cross-validated and a validation report is output. At the same time, the analysis algorithm parameters and validation weight allocation are automatically optimized according to the characteristics and preservation status of different groups of specimens. The multidimensional data of the specimens, the analysis and verification results, and the verification report are stored in the cloud. The data quality is monitored in real time based on the cloud platform, the storage resource allocation is dynamically adjusted, and the processing results are sent back to the user.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the big data-based specimen bioinformatics analysis and verification method as described in claim 7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When a computer program or instruction is executed, it implements the specimen bioinformatics analysis and verification method based on big data as described in claim 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When a computer program or instruction is executed, it implements the specimen bioinformatics analysis and verification method based on big data as described in claim 7.

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