DNA sequencing and encryption method based on microfluidic technology

Abstract

The application discloses a DNA sequencing and encryption method based on micro-fluidic technology, and the method is characterized in that: through grey correlation weight factor analysis and feature sequence analysis based on an ant colony algorithm, a DNA fragment with a relatively short length and sufficient feature content is optimized to construct a feature information library; DNA detection is completed through fluorescent coding microspheres and high-precision fluorescence detection technology, information is stored in a single-chip microcomputer after microsatellite repeat sequence analysis; finally, the information is transmitted to an MCU by using a Bluetooth module, and the information is stored in a decentralized manner by using an ECC elliptic curve encryption algorithm and a block chain, so that the permanent storage and calling verification of personal identity information are realized. The application can be widely applied to the technical field of micro-fluidic chips.

Description

Technical Field

[0001] This invention relates to the field of digital microfluidic chip technology, and in particular to a DNA sequencing and encryption method based on microfluidic technology. Background Technology

[0002] DNA encryption technology, as an ultra-strong encryption technique, is widely considered "one of the technologies capable of changing the course of the future." Its core advantage lies in combining knowledge from the fields of biology and informatics, storing data within DNA molecules and encrypting and protecting the data through sequence encoding. This technology not only significantly increases information storage density but also possesses security and durability far exceeding traditional encryption methods. Its main applications include information security and encryption, bioinformatics management, data storage, anti-counterfeiting, and copyright protection.

[0003] Currently, DNA encryption technology is not widely used in daily life, mainly due to the large amount of information involved in DNA sequencing and encryption, as well as the slow DNA detection process. For DNA sequencing and encryption, based on the double helix structure and base pairing principle of DNA, the most widely used DNA sequencing method is whole-sequence analysis. This requires a large storage space and yields long natural DNA keys, resulting in a lengthy process (lasting from several weeks to a month) and high resource consumption. Summary of the Invention

[0004] In view of this, in order to solve the problem of the difficulty in implementing existing DNA information encryption technologies in practical applications, this invention proposes a DNA sequencing and encryption method based on microfluidic technology, the method comprising the following steps:

[0005] Select appropriate DNA fragments and initially establish an entity sequence library;

[0006] Specifically, the entity sequence library consists of multiple DNA feature fragments with abundant features and sufficient information content, initially selected from human feature libraries, personal feature libraries, and blood feature protein libraries.

[0007] By setting corresponding reference factors and digitizing the entity sequence library, a virtual database is obtained.

[0008] Specifically, the digitization process of an entity sequence library requires first making equivalent digitization assumptions about the fragment factors within it. By labeling specific locations, the information in the human feature library, personal feature library, and blood feature protein library of the entity sequence library can be accurately digitized, ultimately resulting in a corresponding virtual database. The labeling order and sequence sorting in this virtual database represent the feature information in the original entity sequence library.

[0009] Grey relational weight factor analysis is performed on the information in the virtual database to obtain the weight factor of each sub-database relative to the whole. Then, combined with the local weight factor, the computing power weight database is obtained.

[0010] Specifically, the digitized virtual database undergoes mean normalization, initial value normalization, standardization, and extreme value normalization processes to calculate multiple grey relational influence coefficients. The average of these correlation coefficients is taken to obtain the grey relational degree, which is then sorted to obtain the final weight factor influence degree of the three sub-databases. Based on the obtained weight factor influence degree and the weight of each segment in the sub-database, computing power is allocated to the corresponding sub-databases to form a computing power weight database.

[0011] The ant colony algorithm is used to perform recursive calculations on the computing power weight library. Boundary conditions are set and pheromones are updated according to actual conditions. The number of recursions is considered to analyze and obtain the optimal DNA feature fragments.

[0012] Specifically, for the computing power weight library, various influencing factors are comprehensively considered and transformed into ant colony pheromone constraints. Then, feature sequence analysis of the ant colony algorithm is performed on it to obtain a feature sequence library with the most information content and appropriate length among the three sub-libraries.

[0013] A feature information database was constructed based on selected DNA feature fragments;

[0014] The blood sample to be tested flows into the microfluidic chip and is evenly distributed into the four chambers due to the tree-like flow channel design;

[0015] The blood sample to be tested is lysed in the chamber and then mixed with fluorescently encoded microspheres;

[0016] Specifically, blood cells enter the lysis and mixing chamber in the microfluidic channel, where they are lysed with pre-embedded lysis buffer and then mixed with fluorescently coded microspheres to display individual user information.

[0017] The mixed samples are sequentially introduced into the inertial focusing channel, where centrifugal force is used to transition the microspheres from multiple arrangements to a single arrangement, thereby improving the accuracy of high-precision fluorescence detection technology.

[0018] Next, after the mixed sample flows into the fluorescence detection area, the laser emitter illuminates the fluorescently encoded microspheres; after being reflected by the mirror, the light is decomposed into four colors by the filter; the detector receives the light signal, which is amplified by the amplifier and converted into a digital signal by the signal converter;

[0019] Based on microsatellite repeat sequence analysis, the digital signal is processed, and the microcontroller analyzes the point and segment ratio images to obtain detection information;

[0020] The detection information is encrypted using ECC elliptic curve cryptography and permanently stored on the blockchain.

[0021] The real-time user samples are compared with the detection information. If the comparison result is within the error range, the verification is successful; otherwise, it fails.

[0022] Based on the above scheme, this invention provides a DNA sequencing and encryption method based on microfluidic technology. It utilizes grey relational analysis and ant colony optimization to efficiently construct a feature information database. Through microsatellite repeat sequence analysis combined with fluorescently encoded microspheres and high-precision fluorescence detection, it accurately obtains the frequency ratio and individual user characteristics. The system, with a microfluidic chip at its core, enables rapid collection, multiple measurements, and accurate analysis of blood DNA, while reducing sequencing errors and improving detection efficiency. Through optimized feature extraction and data structure design, it significantly shortens sequencing and encryption time, balancing accuracy and portability, and is suitable for efficient identity verification and precision medicine needs in various scenarios. Attached Figure Description

[0023] Figure 1 This is a flowchart of the steps of a DNA sequencing and encryption method based on microfluidic technology according to the present invention;

[0024] Figure 2 This is a schematic diagram illustrating the process of constructing a feature information database according to a specific embodiment of the present invention;

[0025] Figure 3 This is a solid rendering of the microneedles according to a specific embodiment of the present invention;

[0026] Figure 4 This is a top view of the digital microfluidic chip according to a specific embodiment of the present invention;

[0027] Figure 5 This is a physical rendering of the microfluidic chip according to a specific embodiment of the present invention;

[0028] Figure 6 This is a schematic diagram of the fluorescence detection technology, a specific example of the present invention.

[0029] Figure 7 This is a perspective view of the apparatus for implementing the method of the present invention;

[0030] Figure 8 This is a top view of the apparatus for implementing the method of the present invention;

[0031] Figure 9 This is a physical rendering of the apparatus for implementing the method of the present invention. Detailed Implementation

[0032] 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.

[0033] It should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0034] It should be understood that the terms "system," "apparatus," "unit," and / or "module" used in this application are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0035] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "a," and / or "the" are not specifically singular and may include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. An element defined by the phrase "comprising an..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0036] In the description of the embodiments of this application, "a plurality of" refers to two or more. 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 indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0037] Furthermore, flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Additionally, other operations can be added to these processes, or one or more steps can be removed from them.

[0038] Reference Figure 1 This is a schematic flowchart of an optional example of the DNA sequencing and encryption method based on microfluidic technology proposed in this invention. The method can be applied to computer devices, and the DNA sequencing and encryption method proposed in this embodiment may include, but is not limited to, the following steps:

[0039] S1, DNA sequencing.

[0040] Step S1.1: Select the corresponding DNA fragments and enter them into the human characteristic database, personal characteristic database and blood characteristic protein database to initially establish the physical sequence database;

[0041] Step S1.2: Assuming the corresponding reference factors in the three sub-databases, digitize the entity sequence database to obtain the corresponding virtual database.

[0042] Step S1.3: Perform grey relational weight factor analysis on the information in the virtual database to obtain the weight factor of each sub-database relative to the whole, and then combine it with the weight factor of each segment relative to the local area to obtain the computing power weight database.

[0043] Step S1.4: Perform recursive calculations of the ant colony algorithm on the computing power weight library, set boundary conditions and update pheromones according to actual conditions, consider the number of recursions, and analyze to obtain the optimized DNA feature fragments.

[0044] Step S1.5: Organize the selected DNA feature fragments to form a DNA feature sequence library, which is the source of complementary DNA sequence fragments for the microspheres pre-embedded in the microfluidic chip.

[0045] S2, DNA testing.

[0046] Step S2.1: Obtain a blood sample from the user and flow the blood sample into the microfluidic chip, where it is evenly distributed into the four chambers due to the tree-like flow channel design;

[0047] Specifically, four consecutive blood samples can be taken using a microneedle to reduce measurement and detection errors and ensure the accuracy of user information entry. See the enlarged diagram of the microneedle for details. Figure 3 .

[0048] Step S2.2: After the blood sample is lysed in the chamber, it is mixed with fluorescently encoded microspheres; the linear change in the chamber volume controls the outflow time difference, so as to reduce the detection error value by multiple detections with less influence.

[0049] Step S2.3: Blood samples enter the inertial focusing channel in sequence. Centrifugation is used to transition the microspheres from multiple arrangements to a single arrangement, thereby improving the accuracy of fluorescence detection technology.

[0050] Step S2.4: After the blood sample flows into the fluorescence detection area, the laser emitter irradiates the fluorescent coded microspheres; after being reflected by the mirror, the light is decomposed into four colors by the filter; the detector receives the light signal, which is amplified by the amplifier, converted into a digital signal by the signal converter, and then transmitted to the microcontroller for processing;

[0051] Specifically, this high-precision fluorescence detection technology is carried out by an optical path system, which includes: a laser emitter, a mirror, a filter, a detector, an amplifier, and a signal converter. The overall structure is as follows: Figure 6 As shown.

[0052] Step S2.5: Based on the microsatellite repeat sequence analysis, the microcontroller analyzes the location and segment ratio images and obtains the biometric code, which is then transmitted to the MCU via Bluetooth module for encryption.

[0053] S3, DNA Encryption

[0054] Step S3.1: The MCU performs ECC elliptic curve encryption on the input information and permanently stores it on the blockchain to verify the user's corresponding information;

[0055] Step S3.2: When verifying user information, the user performs microneedle blood sampling again. The system automatically retrieves the recorded information for comparison. If the comparison result is within the error range, the verification is successful; otherwise, it fails.

[0056] In some feasible embodiments, step S1.1 specifically includes:

[0057] The initially selected human characteristic database, personal characteristic database, and blood characteristic protein database each correspond to different usage purposes: the human characteristic database is constructed based on DNA fragments common to all humans and is used to determine whether the test subject has human characteristics, serving as the first screening criterion to distinguish human and non-human samples; the personal characteristic database is based on the differences in the frequency of DNA loci between individuals and is used to compare and distinguish users' personal information to achieve the uniqueness of stored information; the blood characteristic protein database mainly considers anti-counterfeiting issues, selecting only unique DNA sequence fragments in blood to distinguish them from other bodily fluids, ensuring a unique interface for verification.

[0058] In some feasible embodiments, step S1.2 specifically includes:

[0059] For the digitization of the sequence fragments and the selection of reference factors, this invention uses the numbers 1, 2, 3, and 4 to replace the four basic base pairs in the sequence: 1-A (adenine), 2-T (thymine), 3-G (guanine), and 4-C (cytosine). FOXP2 (language control gene), HLA (leukocyte antigen gene), and ABO (blood type gene) are used as reference factors for the human characteristic database, personal characteristic database, and blood characteristic protein database, respectively. This helps in the comparison and weight calculation of grey relational weight factor analysis.

[0060] The specific manifestations are as follows:

[0061] Before defining the mapping relationship between bases and numbers:

[0062] Base-to-Digit: A=1, T=2, G=3, C=4

[0063] Assume the DNA fragment is S = [b1, b2, ..., b n ], where b i This represents the i-th base. Its digitized sequence is:

[0064] D = [d1, d2, ..., d n ],

[0065] The gene sequences FOXP2, HLA, and ABO were selected as reference factors for three types of feature libraries, representing the most heavily weighted comparison factors in the language feature gene library, personal feature library, and blood feature protein library, respectively. Assume the corresponding digitized sequences of these genes are as follows:

[0066] D FOXP2 D HLA D ABO

[0067] Their digitized sequences are calculated according to base mapping rules, and the reference factor for each gene can be represented as:

[0068] D ref =[d1,d2,...,d m ]

[0069] In some feasible embodiments, step S1.3 specifically includes:

[0070] Assume the target sequence is digitized as D target =[d'1,d'2,...,d' n The correlation between the target sequence and the reference factor is calculated using grey relational analysis.

[0071] Calculate the absolute difference sequence between the target sequence and the reference factor:

[0072] Δ ij =|d' i -d j |

[0073] Where i = 1, 2, ..., n; j = 1, 2, ..., m.

[0074] To eliminate the influence of dimensions, the difference sequence is normalized:

[0075]

[0076] Where ρ is the resolution coefficient, which is usually taken as 0 < ρ ≤ 1.

[0077] Calculate the target sequence D targetWith reference factor D ref Relevance:

[0078]

[0079] Where γ ij D represents target With reference factor D ref The grey relational degree.

[0080] The above process has been used to calculate γ using the grey relational degree formula. FOXP2 ,γ HLA and γ ABO , respectively representing the target sequence D target The degree of association with each reference sub-library. The weighting of sub-libraries is then normalized based on these degrees of association.

[0081] set up And the sum of the grey relational degrees of all sub-libraries.

[0082] The grey relational degree of each sub-database is normalized accordingly:

[0083]

[0084] This weight reflects the relative importance of sub-library j in the overall target sequence analysis.

[0085] (Computing power weight allocation) Sub-library weight factors obtained from weight factor analysis Allocate overall computing resources and limit the computing power limit of sub-libraries to provide a basis for the selection of feature fragments within sub-libraries.

[0086] Assume the total computing power is C. total

[0087] The computing power allocated to sub-library j

[0088] Furthermore, the fragment selection of sub-library j must satisfy the following:

[0089]

[0090] c i,j The computational power requirement for segment i within the sub-library.

[0091] In some feasible embodiments, step S1.4 specifically includes:

[0092] Suppose that for any fragment x in each library i,j The corresponding local weighting factor is a. i,j (This reflects the importance of the fragment in the corresponding library).

[0093] Due to the existence of the computing power weight library Cj The existence of this allows the initial pheromone to be allocated by combining it with the local weight factor of the fragment, accelerating the convergence of the addition and making it easier to select fragments with high weights.

[0094] For each fragment x i,j The initial pheromone is:

[0095]

[0096] Combined with the heuristic function η i,j To improve the rationality of route selection:

[0097]

[0098] Where θ represents the preference for shorter segments, the overall function can select the key segments, and improve the feature and overall effectiveness of path construction.

[0099] Define the set of fragments as S = {x} i,j Then each ant starts from the initial node and chooses a path according to probability:

[0100]

[0101] Where α and β represent the importance weight of pheromones and the importance weight of heuristic functions, respectively, and are calculated by human calculation.

[0102] Evaluate the path completed by the ant and calculate the corresponding objective function value:

[0103]

[0104] Among them I i,j V i,j D i,j F i,j L i,j R i,j These represent the bioinformatics content, variability, detection adaptability, functional relevance, length, and redundancy of the corresponding fragments, respectively. This refers to the boundary condition constraints, which control the path result sequence obtained by the algorithm to be short and have sufficient feature information.

[0105] Update pheromones based on path evaluation results:

[0106] τ i,j (t)=(1-ρ)·τ i,j (t-1)+Δτ i,j (t)

[0107] Where ρ is the volatility factor, which is usually set between 0.1 ≤ ρ ≤ 0.5.

[0108] Finally, the decision to terminate is made based on the number of iterations or the convergence of the objective function, and the corresponding output is the global optimal path, i.e., the feature sequence library S'.

[0109] In some feasible embodiments, step S2.1 specifically includes:

[0110] Assuming the blood sample can be evenly distributed into the four chambers after entering the microfluidic chip:

[0111] Q = Q1 + Q2 + Q3 + Q4

[0112] Q represents the total flow rate entering the main channel, and Q1, Q2, Q3, and Q4 represent the flow rates allocated to the four chambers.

[0113] By adjusting the flow channel radius r and length L, equal fluid resistance can be achieved in each branch channel:

[0114]

[0115] R and μ represent the resistance of the flow channel and the viscosity of the blood, respectively.

[0116] After adjustment, the amount of blood sample allocated to the four chambers is approximately considered to be equal.

[0117] The data flow direction in step S1.2 is referenced. Figure 2 ;

[0118] In some feasible embodiments, the microfluidic chip includes a tree-like flow channel, a mixing chamber, a pre-embedded assembly, and an inertial focusing flow channel, the overall structure of which refers to Figure 4 Its rendered image is referenced Figure 5 .

[0119] In some feasible embodiments, step S2.2 specifically includes:

[0120] To reduce the error of the aforementioned technology, the chamber width needs to be controlled for mixed outflow, and the outflow time difference is mainly determined by the volume of the chamber.

[0121]

[0122] Δt i,j V i V j Q' and Q' represent the outflow time difference between chambers i and j, the volume of chamber i, the volume of chamber j, and the flow rate at the chamber outlet, respectively (assuming the outlet flow rates of the chambers are the same).

[0123] If the width of the chamber gradually increases, the volume increases linearly:

[0124] V n =V0+n·ΔV

[0125] V n V0 and ΔV represent the volume of the nth chamber, the volume of the narrowest chamber, and the volume difference between adjacent chambers, respectively.

[0126] If a fixed time difference T is required, then the volume difference satisfies:

[0127] ΔV=Q·T

[0128] In some feasible embodiments, step S2.3 specifically includes:

[0129] After inertial focusing, the fluorescently encoded microspheres transition from a multi-row arrangement to a single-row arrangement, facilitating high-precision detection.

[0130]

[0131] The derived formulas respectively represent the inertial focusing characteristics and characteristic dimensions of the flow channel, reflecting the basic principle of inertial focusing and the control conditions of the characteristic dimensions:

[0132] F c , ρ, v 2 D represents the magnitude of the centrifugal force, the density of the fluorescent microspheres, the flow velocity of the microspheres, and the diameter of the inertial focusing channel, respectively; D0, h, and Re represent the width of the focusing region, the height of the channel, the Reynolds number, and the ratio of the inertia to viscosity of the flow, respectively.

[0133] In some feasible embodiments, step S2.4 specifically includes:

[0134] Since the characteristic sequence library S' was obtained from DNA sequencing, the fluorescently encoded microspheres correspond to the superior specific fragments in the display information sequence, which are distinguished by the frequency difference of the occurrence of the specific fragments among users.

[0135] Suppose there are M fluorescently encoded microspheres, and N DNA fragment sites are detected. The detection result for each fragment can be expressed as:

[0136]

[0137] , representing the frequency proportion of fragment i in microsphere group j, the number of times fragment i is detected in microsphere group j, and the total number of times fragment i is detected across all microsphere groups, respectively.

[0138] For each segment, calculate its average frequency of occurrence and error range:

[0139]

[0140] ε iand represent the average frequency of occurrence of segment i, and the standard error range of the frequency of segment i, respectively.

[0141] The normalized frequency is expressed as:

[0142]

[0143] Normalized fragment frequency f i A fixed error range δ is set to ensure stability. The biometric encoding of the fragment can be represented as:

[0144]

[0145] Where b i This represents the biometric value of fragment i, while round() represents the rounding function.

[0146] All biometric values ​​of the fragments {b1, b2, ..., b N The sequences B are concatenated and used as an individual's biometric code.

[0147] B = [b1, b2, ..., b N ]

[0148] This solution also constructs a device applicable to the above method, the overall structure of which is as follows: Figure 7 and Figure 8 For actual rendering reference Figure 9 .

[0149] A DNA sequencing and encryption system based on microfluidic technology, comprising:

[0150] Database building units are used to perform DNA sequencing steps;

[0151] The detection unit is used to perform DNA testing steps;

[0152] Encryption unit, used to perform DNA encryption steps.

[0153] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0154] A storage medium storing processor-executable instructions, which, when executed by a processor, are used to implement a microfluidic-based DNA sequencing and encryption method as described above.

[0155] The content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0156] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A DNA sequencing and encryption method based on microfluidic technology, characterized in that, Includes the following steps: Select DNA fragments and build physical sequence libraries; The entity sequence library is digitized and subjected to grey relational weight factor analysis to construct a feature information library; Based on the aforementioned feature information database, the sample to be tested flows into the microfluidic chip, and DNA detection is completed through fluorescently encoded microspheres and microsatellite repeat sequence analysis to obtain detection information; The detection information is stored in a decentralized manner using the ECC elliptic curve cryptography algorithm and the blockchain; The step of digitizing the entity sequence library and performing grey relational weight factor analysis to construct a feature information library specifically includes: A reference factor is set and the entity sequence library is digitized to obtain a virtual database; Grey relational weight factor analysis is performed on the information in the virtual database, and the computing power weight library is obtained by combining the overall weight factor and the local weight factor. The computing power weight library is subjected to recursive operation of ant colony algorithm, and filtered in combination with preset conditions to obtain preferred DNA feature fragments; The selected DNA feature fragments are organized to obtain a feature information database; The formula for grey relational degree weighting factor analysis is expressed as follows: in, This represents the grey relational degree between the target sequence and the reference factor. n This represents the total number of target sequences. i Indicates the sequence number of the target sequence. Represents the resolution coefficient. Indicates the first i Target sequences, Indicates the first j One reference factor.

2. The DNA sequencing and encryption method based on microfluidic technology according to claim 1, characterized in that, The microfluidic chip includes a tree-like flow channel, a mixing chamber, a pre-embedded assembly, and an inertial focusing flow channel. The pre-embedded assembly is placed in the mixing chamber and includes a fission fluid and fluorescently encoded microspheres.

3. The DNA sequencing and encryption method based on microfluidic technology according to claim 1, characterized in that, The step of feeding the sample into a microfluidic chip based on the feature information database and performing DNA detection through fluorescently encoded microspheres and microsatellite repeat sequence analysis to obtain detection information specifically includes: The sample to be tested flows into the microfluidic chip and is evenly distributed into the mixing chamber; The sample to be tested is lysed in the mixing chamber and mixed with the fluorescently encoded microspheres to obtain a mixed sample; The mixed sample enters the inertial focusing channel in sequence, and the microspheres are transformed from multiple arrangements to a single arrangement by centrifugation. The mixed sample flows into the fluorescence detection area, where it is irradiated by a laser emitter to obtain the optical signal and convert it into a digital signal. Based on the digital signal and the feature information database, and according to the microsatellite repeat sequence analysis, the microcontroller analyzes the point and segment ratio images to obtain detection information.

4. The DNA sequencing and encryption method based on microfluidic technology according to claim 3, characterized in that, The process of the mixed sample flowing into the fluorescence detection area, irradiating the fluorescently encoded microspheres with a laser emitter, and converting the received light signal into a digital signal specifically includes: The mixed sample flows into the fluorescence detection area; When the fluorescently coded microspheres pass through the designated area, the laser emitter emits a laser beam that irradiates the fluorescently coded microspheres, and the light is directed toward the filter by a mirror. The filter filters out the color corresponding to each color of light, and the detector detects the intensity of each color of light and converts it into a digital signal.

5. The DNA sequencing and encryption method based on microfluidic technology according to claim 1, characterized in that, Also includes: The real-time samples are compared with the detection information.

6. A DNA sequencing and encryption system based on microfluidic technology, characterized in that, A method for performing DNA sequencing and encryption based on microfluidic technology as described in claim 1, comprising: A database construction unit is used to select DNA fragments and establish an entity sequence library; the entity sequence library is digitized and subjected to grey relational weight factor analysis to construct a feature information library; The detection unit, based on the feature information database, feeds the sample to be tested into the microfluidic chip and completes DNA detection through fluorescently encoded microspheres and microsatellite repeat sequence analysis to obtain detection information; An encryption unit is used to store the detection information in a decentralized manner using the ECC elliptic curve cryptography algorithm and the blockchain.

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