Biological big data storage and management system and method

Abstract

The invention relates to the technical field of biomedical data management, in particular to a biological big data storage and management system and method. Multi-source biological data is stored in a distributed mode through a redundancy mechanism, an index tag is set for each storage unit, the data level of each piece of biological sub-data is set based on the biological sub-data, then the access level of an access user is set based on identity information of the access user, and target data is determined according to the index tag input by the access user; and if the access level of the access user is greater than or equal to the data level of the target data and the environmental data of the target data accords with an environmental data threshold value, calling and displaying the target data. Therefore, an access user with a lower access level cannot view the biological data with a high data level, so that the safety of the biological data is improved, and the biological data is prevented from being leaked.

Description

Technical Field

[0001] The present invention relates to the field of biomedical data management technology, and in particular to a biological big data storage and management system and method. Background Art

[0002] The term "big data" originally originated from the Internet and IT industries. However, with the completion of the "Human Genome Project", it has led to a revolution in the biological industry. High-throughput sequencing technology has developed rapidly, enabling life science research to gain powerful data output capabilities, including genomics, transcriptomics, proteomics, metabolomics and other biological data. These data have the characteristics of large data volume, data variety, value, and high speed.

[0003] A Chinese patent with announcement number CN118656037B discloses a biomedical storage system and method based on big data. By collecting temperature data and calculating pressure and volume, combined with energy state simulation, the data storage efficiency and data processing accuracy are improved. By monitoring the change in entropy value, the access and modification anomalies of data can be tracked in real time, which significantly improves the level of data security management. The energy-saving efficiency of the system is further improved through a cooling feedback mechanism. In the prior art, only the impact of environmental data on the real-time nature of biological data is considered, but it does not explain how to improve the security of biological data and avoid the leakage of biological data. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose a biological big data storage and management system and method.

[0005] The technical solution of the present invention: On the one hand, the present application provides a biological big data storage and management method, comprising: Acquire multi-source biological data, set up multiple storage units, and distribute and store the multi-source biological data based on a redundancy mechanism; the biological data includes multiple biological sub-data; Setting an index tag for each storage unit, and setting a data level of each biological sub-data based on the biological sub-data; Obtain the identity information of the accessing user, and set the access level of the accessing user based on the identity information of the accessing user; Obtaining the index tag input by the accessing user, determining the target data according to the index tag input by the accessing user, and judging whether the access level of the accessing user is greater than or equal to the data level of the target data; If the access level of the accessing user is greater than or equal to the data level of the target data, the environmental data of the target data is collected, and it is determined whether the environmental data of the target data meets the environmental data threshold. When the environmental data of the target data meets the environmental data threshold, the target data is called and displayed.

[0006] Preferably, obtaining multi-source biological data, setting up multiple storage units, and distributively storing the multi-source biological data based on a redundancy mechanism include: Creating a biological data table; the biological data table includes a plurality of data sub-tables; Collecting a plurality of multi-source biological data respectively, and dividing the multi-source biological data into a plurality of biological sub-data according to the data source of each type of biological data; Based on the data source, each biological sub-data is placed in a data sub-table, and each data sub-table corresponds to one biological sub-data.

[0007] Preferably, an index tag is set for each storage unit, and a data level of each biological sub-data is set based on the biological sub-data, including: Select a data table; Obtaining each detailed biological data in the biological sub-data contained in the data sub-table; Based on each detailed biological data included in the data sub-table, obtain the same data segments between all the detailed biological data; record the longest same data segment as the data header, and record the first different data segment as the marking data; Set the interception length, intercept part of the data header from the starting point of the data header as the index header, and copy the index header part to the beginning of the tag data to form an index tag; Return and select a data sub-table until the index label of each biological sub-data is obtained; For all index labels, determine whether there are two identical index labels; If there are two identical index tags, one of the two identical index tags is randomly selected, the truncation length of the index tag is reset to obtain a new index tag, and all index tags are returned to determine whether there are two identical index tags, until there are no identical index tags.

[0008] Preferably, setting an index tag for each storage unit and setting a data level of each biological sub-data based on the biological sub-data further includes: Set the data level range, Randomly select a biological sub-data from the biological data table; Calculating a grade score of the biological sub-data based on the biological sub-data; Setting the data level of the biological sub-data according to the level score of the biological sub-data; Return to randomly select a biological sub-data from the biological data table until all biological sub-data in the biological data table have been selected.

[0009] Preferably, calculating the grade score of the biological sub-data based on the biological sub-data includes: The grade score of biological sub-data is calculated by formula 1; Formula 1; in, is the grade score of the biological sub-data, is the number of guanines contained in the DNA sequence in the biological sub-data, is the number of protein types contained in the proteome sequence in the biological sub-data, is the weight corresponding to the number of guanines contained in the DNA sequence in the biological sub-data, is the weight corresponding to the number of protein types contained in the proteome sequence in the biological sub-data, is the common logarithm.

[0010] Preferably, obtaining the identity information of the accessing user and setting the access level of the accessing user based on the identity information of the accessing user includes: Obtain the identity information of the accessing user; Based on the identity information of the access user, the access level of the access user is calculated by formula 2; Formula 2; in, Is the identity information of the access user, is the job level of the accessing user, is the number of years of experience of the visiting user, is the weight corresponding to the job level of the accessing user, It is the weight corresponding to the working experience of the visiting user.

[0011] Preferably, obtaining the index tag input by the accessing user, determining the target data according to the index tag input by the accessing user, and judging whether the access level of the accessing user is greater than or equal to the data level of the target data include: Acquire the index tag input by the access user, and based on the index tag, acquire the biological sub-data indicated by the index tag; record the biological sub-data indicated by the index tag input by the access user as target data; Determine whether the user's access level is greater than or equal to the data level of the target data; If the user's access level is lower than the data level of the target data, the user's access is stopped; If the user's access level is greater than or equal to the data level of the target data, the environmental data is detected.

[0012] Preferably, if the access level of the accessing user is greater than or equal to the data level of the target data, the environmental data of the target data is collected, and it is determined whether the environmental data of the target data meets the environmental data threshold. When the environmental data of the target data meets the environmental data threshold, the target data is called and displayed, including: Collecting multiple environmental data of the storage unit of the target data; the environmental data includes storage temperature, pressure of the storage unit and volume of the storage unit; Set environmental data thresholds; Determine whether each item of environmental data of the storage unit of the target data satisfies the environmental data threshold value; When the environmental data of all items of the storage unit of the target data meets the environmental data threshold, the target data is called and displayed.

[0013] On the other hand, the present application also provides a biological big data storage and management system, including: A storage component, wherein the storage component adopts distributed storage and stores multi-source biological data; A processing component is used to execute the aforementioned biological big data storage and management method. The processing component includes an index unit and a data unit. The biological data in the storage component is indexed by the index unit, and the biological data is processed by the data unit.

[0014] Preferably, the storage component is communicatively connected to the processing component, and data transmission and storage are encrypted via SSL / TLS transmission and hierarchical storage encryption.

[0015] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: Multi-source biological data is distributedly stored through a redundant mechanism, and an index tag is set for each storage unit. The data level of each biological sub-data is set based on the biological sub-data. Then, the access user data and the index tag input by the access user are obtained, and the access level of the access user is set based on the access user data. The target data is determined according to the index tag input by the access user, and it is determined whether the access level of the access user is greater than or equal to the data level of the target data. If the access level of the access user is greater than or equal to the data level of the target data, it is determined whether the environmental data of the target data meets the environmental data threshold. When the environmental data of the target data meets the environmental data threshold, the target data is called and displayed. The present application sets the data level of biological data and the access level of the access user, so that access users with lower access levels cannot view biological data with higher data levels, thereby improving the security of biological data and avoiding leakage of biological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a biological big data storage and management method proposed by the present invention; Figure 2 This is a structural schematic diagram of a biological big data storage and management system proposed in the present invention.

[0017] Figure numerals: 100, storage component; 200, processing component; 201, index unit; 202, data unit. DETAILED DESCRIPTION

[0018] Embodiment 1, as Figure 1 As shown, the present invention proposes a biological big data storage and management method, comprising: S100, acquiring multi-source biological data, setting a plurality of storage units, and distributively storing the multi-source biological data based on a redundancy mechanism; the biological data includes a plurality of biological sub-data; S200, setting an index tag for each storage unit, and setting a data level of each biological sub-data based on the biological sub-data; S300, obtaining identity information of a visiting user, and setting an access level of the visiting user based on the identity information of the visiting user; S400, obtaining an index tag input by an accessing user, determining target data according to the index tag input by the accessing user, and determining whether the access level of the accessing user is greater than or equal to the data level of the target data; S500, if the access level of the accessing user is greater than or equal to the data level of the target data, the environmental data of the target data is collected, and it is determined whether the environmental data of the target data meets the environmental data threshold. When the environmental data of the target data meets the environmental data threshold, the target data is called and displayed.

[0019] In the present invention, multi-source biological data are distributedly stored through a redundant mechanism, and an index tag is set for each storage unit. The data level of each biological sub-data is set based on the biological sub-data. Then, the access user data and the index tag input by the access user are obtained, and the access level of the access user is set based on the access user data. The target data is determined according to the index tag input by the access user, and it is judged whether the access level of the access user is greater than or equal to the data level of the target data. If the access level of the access user is greater than or equal to the data level of the target data, it is judged whether the environmental data of the target data meets the environmental data threshold. When the environmental data of the target data meets the environmental data threshold, the target data is called and displayed. The present application sets the data level of the biological data and the access level of the access user, so that the access users with lower access levels cannot view the biological data with higher data levels, thereby improving the security of the biological data and avoiding leakage of the biological data.

[0020] In an optional embodiment, the S100 includes: S110, creating a biological data table; the biological data table includes a plurality of data sub-tables; S120, respectively collecting a plurality of multi-source biological data, and dividing the multi-source biological data into a plurality of biological sub-data according to the data source of each type of biological data; Specifically, the storage of biological data supports biological data formats such as BAM and VCF; For biological sub-data, all detailed biological data of each biological individual can be regarded as a biological sub-data. The biological sub-data of an individual includes genome sequence, transcriptome expression, proteome and metabolome information; S130, putting each biological sub-data into a data sub-table based on the data source, and each data sub-table corresponds to a biological sub-data table.

[0021] It should be noted that for a biological sub-data, the data volume of the biological sub-data is very large. A single genome sequencing may generate hundreds of GB of data. Therefore, the biological sub-data is divided into multiple segments to achieve distributed storage.

[0022] Distributed storage adopts a scalable system structure, uses multiple storage servers to share the storage load, and uses location servers to locate storage information. It not only improves the system's reliability, availability, and access efficiency, but is also easy to expand.

[0023] In an optional embodiment, the S200 includes: S210, selecting a data sub-table; S220, obtaining each detailed biological data in the biological sub-data contained in the data sub-table; S230, based on each detailed biological data included in the data sub-table, obtaining the same data segments between all the detailed biological data; recording the longest same data segment as a data header, and recording the first different data segment as a marker data; S240, setting the interception length, taking the starting point of the data header as the starting point to intercept part of the data header as the index header, and copying the index header part to the beginning of the tag data to form an index tag; S250, returning to select a data sub-table until an index label of each biological sub-data is obtained; S260, for all index tags, determining whether there are two identical index tags; S270, if there are two identical index tags, randomly select one index tag from the two identical index tags, reset the cut length of the index tag, obtain a new index tag, and return to all index tags to determine whether there are two identical index tags, until no identical index tags exist; Specifically, when resetting the interception length, the interception length is generally increased so as to obtain more information from the data header.

[0024] It should be noted that, since biological data contains a large amount of repeated data, when creating index tags, it is only necessary to intercept part of the biological sub-data to implement indexing of the biological sub-data.

[0025] For human biological sub-data, 99% of the data in its DNA sequence is the same, while the DNA sequences of humans and cabbages are different. Therefore, it is only necessary to intercept some fragments in the same DNA sequence of humans to indicate that this type of biological sub-data is human biological sub-data. Similarly, some fragments in the same DNA sequence of cabbage can also indicate that this type of biological sub-data is the biological sub-data of cabbage. Therefore, some fragments in the same DNA sequence can be used as data headers. For different individuals in the human organism, in order to highlight the differences between the biological sub-data of different individuals, some fragments in different DNA sequences are intercepted as marking data to indicate the individual corresponding to the DNA sequence.

[0026] In an optional embodiment, the S200 further includes: S280, set the data level range, S290, randomly selecting a biological sub-data from the biological data table; S291, calculating a grade score of the biological sub-data based on the biological sub-data; S292, setting the data level of the biological sub-data according to the level score of the biological sub-data; Specifically, the higher the grade score of biological sub-data, the higher the grade of its corresponding data and the higher its importance; S293, returning to randomly selecting a biological sub-data from the biological data table until all biological sub-data in the biological data table are selected; Optionally, for individuals of the same category, the importance of their biological sub-data is similar, that is, for individual A, individual B, cabbage C, and cabbage D, the importance of the biological sub-data of individual A and individual B is the same, the importance of the biological sub-data of cabbage C and cabbage D is the same, but the importance of the biological sub-data of individual A is higher than the importance of the biological sub-data of cabbage C; Therefore, when calculating the grade score of biological sub-data, in order to reduce the computational workload, for individuals of the same species, the biological importance of the biological sub-data of an individual under the species can be used as the biological importance of the biological sub-data of the species.

[0027] It should be noted that for each individual's biological sub-data, the importance of biological sub-data varies between different species and individuals. The importance of the biological sub-data of human individuals is much higher than that of cabbage. Therefore, in order to avoid the leakage of important biological sub-data, the importance of the biological sub-data of different individuals is defined by calculating the data scores of different biological sub-data, thereby setting different data levels for different individuals.

[0028] In an optional embodiment, the S291 includes: S291-1, calculating the grade score of biological sub-data by formula 1; Formula 1; in, is the grade score of the biological sub-data, is the number of guanines contained in the DNA sequence in the biological sub-data, is the number of protein types contained in the proteome sequence in the biological sub-data, is the weight corresponding to the number of guanines contained in the DNA sequence in the biological sub-data, is the weight corresponding to the number of protein types contained in the proteome sequence in the biological sub-data, is the common logarithm; Optionally, since Formula 1 is a calculation method for the grade score of biological sub-data based on proteome and DNA sequence, it is mainly a calculation method for the grade score of biological sub-data between different species. For different individuals of the same species, their proteomes and DNA sequences are very similar. Therefore, the calculation result of Formula 1 is difficult to reflect the difference in grade scores between different individuals of the same species. Therefore, Formula 3 can also be used to calculate the grade scores between different individuals of the same species; Formula 3; in, is the grade score of the biological sub-data, is the number of guanines contained in the DNA sequence in the biological sub-data, is the number of protein types contained in the proteome sequence in the biological sub-data, is the weight corresponding to the number of guanines contained in the DNA sequence in the biological sub-data, is the weight corresponding to the number of protein types contained in the proteome sequence in the biological sub-data, is the common logarithm, is the importance of the individual, is the weight corresponding to the importance of the individual; It should be noted that individual importance is a custom indicator, so that users can adjust the importance of biological sub-data according to their own needs when saving biological sub-data.

[0029] It should be noted that the DNA sequence contains four bases: guanine, adenine, cytosine and thymine. Guanine and cytosine always appear in pairs, and adenine and thymine always appear in pairs. Therefore, when calculating the grade score of biological sub-data, it is only necessary to calculate the proportion of the number of any one base to reflect the proportion of all bases in the DNA sequence. Since the number of guanine in the DNA sequence is large, in order to simplify the calculation, the common logarithm of the number of guanine is first calculated to reduce the order of magnitude. For proteome sequences, since the number of protein types in simple organisms is less than that in complex organisms, for example, prokaryotes such as bacteria have only a few thousand types of proteins, while eukaryotes such as humans have tens of thousands of types of proteins, the number of biological species can be simply distinguished by the number of protein types.

[0030] In an optional embodiment, the S300 includes: Obtain the identity information of the accessing user; Specifically, the identity information of the accessing user includes the ID number, job grade and working grade of the accessing user; Based on the identity information of the access user, the access level of the access user is calculated by formula 2; Formula 2; in, Is the identity information of the access user, is the job level of the accessing user, is the number of years of experience of the visiting user, is the weight corresponding to the job level of the accessing user, It is the weight corresponding to the working experience of the visiting user.

[0031] It should be noted that in order to improve the security of biometric data and prevent leakage of biometric data, different access levels are set for different access users, so that people with lower access levels cannot view biometric data with higher data levels.

[0032] In an optional embodiment, the S400 includes: S410, obtaining an index tag input by an accessing user, and based on the index tag, obtaining biological sub-data indicated by the index tag; recording the biological sub-data indicated by the index tag input by the accessing user as target data; S420, determining whether the user's access level is greater than or equal to the data level of the target data; S430, if the user's access level is lower than the data level of the target data, stop the user's access; S440: If the user's access level is greater than or equal to the data level of the target data, then detect the environmental data.

[0033] It should be noted that after calculating the access level of the accessing user, the index tag entered by the accessing user is obtained, and the target data is searched based on the index tag. When the access level of the accessing user is greater than or equal to the data level of the target data, the accessing user has the opportunity to view the target data. However, before viewing the target data, it is also necessary to first perform a fragmentation of the environmental data to ensure the security of the target data.

[0034] In an optional embodiment, the S500 includes: S510, collecting multiple environmental data of the storage unit of the target data; the environmental data includes storage temperature, pressure of the storage unit and volume of the storage unit; S520, setting an environmental data threshold; S530, determining whether each environmental data of the storage unit of the target data satisfies the environmental data threshold value; S540: When the environmental data of all items of the storage unit of the target data meets the environmental data threshold, the target data is called and displayed.

[0035] It should be noted that each environmental data has its corresponding environmental data threshold. When any environmental data does not meet its corresponding environmental data threshold, the user is also stopped from accessing the target data.

[0036] Regarding the storage temperature, since the insulation temperature of biological data is relatively low, for example, the storage temperature of biological data stored in liquid nitrogen is -196°C, if the ambient temperature is higher than this temperature, the biological data will be easily damaged when viewing it. Therefore, before viewing the target data, it is necessary to check whether multiple environmental data of the storage unit of the target data are qualified.

[0037] like Figure 2 As shown, the present application also provides a biological big data storage and management system, including a storage component 100 and a processing component 200.

[0038] The storage component 100 adopts distributed storage, and stores multi-source biological data through the storage component 100. The biological big data storage and management method described in the first embodiment is executed through the processing component 200. The processing component 200 includes an index unit 201 and a data unit 202. The biological data in the storage component 100 is indexed through the index unit 201, and the biological data is processed through the data unit 202. It should be noted that after acquiring multi-source biological data, the multi-source biological data needs to be stored in the storage component 100 through distributed storage. When the accessing user views the biological data, the accessing user needs to input the index tag and the accessing user identity information through the index unit 201, and the processing component 200 combines the index tag and the accessing user identity information to determine whether to display the target data.

[0039] In an optional embodiment, the storage component 100 is in communication with the processing component 200, and data transmission and storage are encrypted via SSL / TLS transmission and hierarchical storage encryption.

[0040] It should be noted that SSL Secure Sockets Layer is a network security protocol first adopted by Netscape. It is a security protocol implemented on the transmission communication protocol (TCP / IP) and uses public key technology. SSL widely supports various types of networks and provides three basic security services; TLS is a protocol used to provide confidentiality, data integrity, and authenticity between two communicating applications. TLS consists of two layers: TLS Record Protocol and TLS Handshake Protocol. The TLS protocol uses a master-slave architecture model to create a secure connection between two applications over the network to prevent eavesdropping and tampering when exchanging data. The encryption of biometric data during transmission is improved through SSL / TLS transmission encryption and hierarchical storage encryption, thereby improving the security of biometric data.

[0041] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A biological big data storage and management method, characterized in that: include: Acquire multi-source biological data, set up multiple storage units, and distribute and store the multi-source biological data based on a redundancy mechanism; the biological data includes multiple biological sub-data; Setting an index tag for each storage unit, and setting a data level of each biological sub-data based on the biological sub-data; Obtain the identity information of the accessing user, and set the access level of the accessing user based on the identity information of the accessing user; Obtaining the index tag input by the accessing user, determining the target data according to the index tag input by the accessing user, and judging whether the access level of the accessing user is greater than or equal to the data level of the target data; If the access level of the accessing user is greater than or equal to the data level of the target data, the environmental data of the target data is collected, and it is determined whether the environmental data of the target data meets the environmental data threshold. When the environmental data of the target data meets the environmental data threshold, the target data is called and displayed.

2. A biological big data storage and management method according to claim 1, characterized in that: Acquire multi-source biological data, set up multiple storage units, and distribute and store the multi-source biological data based on a redundant mechanism, including: Creating a biological data table; the biological data table includes a plurality of data sub-tables; Collecting a plurality of multi-source biological data respectively, and dividing the multi-source biological data into a plurality of biological sub-data according to the data source of each type of biological data; Based on the data source, each biological sub-data is placed in a data sub-table, and each data sub-table corresponds to one biological sub-data.

3. A biological big data storage and management method according to claim 2, characterized in that: An index tag is set for each storage unit, and a data level of each biological sub-data is set based on the biological sub-data, including: Select a data table; Obtaining each detailed biological data in the biological sub-data contained in the data sub-table; Based on each detailed biological data included in the data sub-table, obtain the same data segments between all the detailed biological data; record the longest same data segment as the data header, and record the first different data segment as the marking data; Set the interception length, intercept part of the data header from the starting point of the data header as the index header, and copy the index header part to the beginning of the tag data to form an index tag; Return and select a data sub-table until the index label of each biological sub-data is obtained; For all index labels, determine whether there are two identical index labels; If there are two identical index tags, one of the two identical index tags is randomly selected, the truncation length of the index tag is reset to obtain a new index tag, and all index tags are returned to determine whether there are two identical index tags, until there are no identical index tags.

4. A biological big data storage and management method according to claim 3, characterized in that: Setting an index tag for each storage unit and setting a data level for each biological sub-data based on the biological sub-data, further comprising: Set the data level range, Randomly select a biological sub-data from the biological data table; Calculating a grade score of the biological sub-data based on the biological sub-data; Setting the data level of the biological sub-data according to the level score of the biological sub-data; Return to randomly select a biological sub-data from the biological data table until all biological sub-data in the biological data table have been selected.

5. A biological big data storage and management method according to claim 4, characterized in that: Calculating a grade score of the biological sub-data based on the biological sub-data includes: The grade score of biological sub-data is calculated by formula 1; Formula 1; in, is the grade score of the biological sub-data, is the number of guanines contained in the DNA sequence in the biological sub-data, is the number of protein types contained in the proteome sequence in the biological sub-data, is the weight corresponding to the number of guanines contained in the DNA sequence of the biological sub-data, is the weight corresponding to the number of protein types contained in the proteome sequence of the biological sub-data, is the common logarithm.

6. A biological big data storage and management method according to claim 5, characterized in that: Obtain the identity information of the accessing user and set the access level of the accessing user based on the identity information of the accessing user, including: Obtain the identity information of the accessing user; Based on the identity information of the access user, the access level of the access user is calculated by formula 2; Formula 2; in, Is the identity information of the access user, is the job level of the accessing user, is the number of years of experience of the visiting user, is the weight corresponding to the job level of the accessing user, It is the weight corresponding to the working experience of the visiting user.

7. A biological big data storage and management method according to claim 6, characterized in that: Obtaining the index tag input by the accessing user, determining the target data according to the index tag input by the accessing user, and judging whether the access level of the accessing user is greater than or equal to the data level of the target data, including: Acquire the index tag input by the access user, and based on the index tag, acquire the biological sub-data indicated by the index tag; record the biological sub-data indicated by the index tag input by the access user as target data; Determine whether the user's access level is greater than or equal to the data level of the target data; If the user's access level is lower than the data level of the target data, the user's access is stopped; If the user's access level is greater than or equal to the data level of the target data, the environmental data is detected.

8. A biological big data storage and management method according to claim 7, characterized in that: If the access level of the accessing user is greater than or equal to the data level of the target data, the environment data of the target data is collected, and it is determined whether the environment data of the target data meets the environment data threshold. When the environment data of the target data meets the environment data threshold, the target data is called and displayed, including: Collecting multiple environmental data of the storage unit of the target data; the environmental data includes storage temperature, pressure of the storage unit and volume of the storage unit; Set environmental data thresholds; Determine whether each item of environmental data of the storage unit of the target data satisfies the environmental data threshold value; When the environmental data of all items of the storage unit of the target data meets the environmental data threshold, the target data is called and displayed.

9. A biological big data storage and management system, characterized in that: include: A storage component, wherein the storage component adopts distributed storage and stores multi-source biological data; A processing component, through which the biological big data storage and management method as described in any one of claims 1 to 8 is executed, the processing component includes an index unit and a data unit, the index unit is used to index the biological data in the storage component, and the data unit is used to process the biological data.

10. A biological big data storage and management system according to claim 9, characterized in that: The storage component is in communication with the processing component, and data transmission and storage are encrypted via SSL / TLS transmission and hierarchical storage encryption.

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