DNA Detection Data Synchronization and Classification Method Based on Decision Tree Model

Abstract

The present invention relates to a DNA detection data synchronization and classification method based on a decision tree model, belonging to the technical field of DNA data classification processing. The present invention obtains the file size information of the detection data after DNA classification and the real-time network communication data of the communication channel, and finally generates a first synchronization strategy or a second synchronization strategy according to the file size information of the detection data after DNA classification and the real-time network communication data of the communication channel, and performs data synchronization according to the first synchronization strategy or the second synchronization strategy. By integrating a Bayesian network into the decision tree model, the present invention can optimize the situation where the decision tree model has low accuracy in the process of classifying DNA detection data. Secondly, the present invention can also optimize the data transmission situation of DNA detection data during the data synchronization process, so that the DNA detection data can be synchronized to the medical platform in a timely manner, improving the query efficiency of DNA detection data.

Description

Technical Field

[0001] The present invention relates to the technical field of DNA data classification, and particularly to a method for synchronizing and classifying DNA detection data based on a decision tree model. Background Art

[0002] A gene is a functional fragment on a DNA molecule, the basic unit of genetic information, and the most fundamental factor determining all biological species; genes determine a person's birth, aging, illness, and death, are the causes of health, beauty, and longevity, and are the controllers and regulators of life. Gene detection is a technology for detecting DNA through blood, other body fluids, or cells. Gene detection is a technology for detecting DNA through blood, other body fluids, or cells. It is to take the exfoliated oral mucosal cells or other tissue cells of the person to be tested, amplify their gene information, and then detect the DNA molecular information in the cells of the person to be tested through specific equipment, predict the risk of the body suffering from diseases, analyze the various gene conditions it contains, so that people can understand their own gene information, and thus avoid or delay the occurrence of diseases by improving their living environment and living habits.

[0003] Gene detection can be used to diagnose diseases and also for predicting disease risks. Disease diagnosis is to use gene detection technology to detect mutant genes that cause genetic diseases. Currently, the most widely used gene detection is the detection of neonatal genetic diseases, the diagnosis of genetic diseases, and the auxiliary diagnosis of certain common diseases. Currently, more than 1,000 genetic diseases can be diagnosed through gene detection technology. Predictive gene detection is to use gene detection technology to detect the risk of disease occurrence before the disease occurs, and take early prevention or effective intervention measures. Currently, more than 20 diseases can be predicted by gene detection methods. When detecting, first extract the genes of the person to be tested from blood or other cells. Then use primers that can recognize genes that may have mutations and PCR technology to replicate this part of the genes many times, and use methods such as mutant gene probes with special markers, enzyme digestion methods, and gene sequence detection methods to determine whether there are mutations or sensitive genotypes in this part of the genes. However, after gene detection, most of the gene detection data are massive data, and it is necessary to classify and process the massive data, identify and classify abnormal data. The decision tree model algorithm is one of the classification algorithms. However, the problems brought by the model in practical applications are also endless. For example, the decision tree algorithm has low classification accuracy. How to improve the classification accuracy of the decision tree algorithm is a crucial aspect in the field of machine learning. Summary of the Invention

[0004] The present invention overcomes the deficiencies of the prior art and provides a method for synchronizing and classifying DNA detection data based on a decision tree model.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] In the first aspect of the present invention, a method for synchronizing and classifying DNA detection data based on a decision tree model is provided, which is characterized in that it specifically includes:

[0007] Obtain DNA detection data, construct a decision tree model, input the DNA detection data into the decision tree model for initial classification, and fuse a Bayesian network;

[0008] Monitor the process of DNA detection data during classification through the Bayesian network, perform observation processing on each leaf node, obtain the detection data after DNA classification, and obtain the real-time network communication data of each communication channel;

[0009] Initial select a number of candidate communication channels according to the real-time network communication data of each communication channel, and obtain the file size information of the detection data after DNA classification and the real-time network communication data of the candidate communication channels;

[0010] Generate a first synchronization strategy or a second synchronization strategy according to the file size information of the detection data after DNA classification and the real-time network communication data of the communication channel, and perform data synchronization according to the first synchronization strategy or the second synchronization strategy.

[0011] Further, in the method for synchronizing and classifying DNA detection data based on a decision tree model, obtaining DNA detection data, constructing a decision tree model, inputting the DNA detection data into the decision tree model for initial classification, and fusing a Bayesian network is specifically as follows:

[0012] Obtain DNA detection data and construct a decision tree model. According to the DNA detection data, construct a sample data set, use the sample data set as the root node in the decision tree model, and set the splitting standard threshold;

[0013] Calculate the Euclidean distance value between each sample data set in the root node, and combine the sample data sets with the Euclidean distance value not greater than the splitting standard threshold into one data set to form a leaf node;

[0014] Separate the sample data with the Euclidean distance value greater than the splitting standard threshold into another data set to form a leaf node. Through continuous splitting until no new leaf nodes are generated in the decision tree model, and fuse a Bayesian network.

[0015] Further, in the method for synchronizing and classifying DNA detection data based on a decision tree model, monitoring the process of DNA detection data during classification through the Bayesian network, performing observation processing on each leaf node, and obtaining the detection data after DNA classification is specifically as follows:

[0016] Obtain the leaf nodes after initial classification, and use each sample data in the leaf nodes after initial classification as an observation node, and perform sample search with one of the observation nodes in each leaf node after initial classification;

[0017] Construct the association relationship between the observation nodes, connect the current observation node and another observation node based on the association relationship between the observation nodes, construct a directed acyclic graph, and calculate the joint probability value between the observation nodes in the directed acyclic graph;

[0018] When the joint probability value between the observation nodes in the directed acyclic graph is greater than the joint probability threshold, then keep the current observation node and another observation node unchanged in the current leaf node;

[0019] When the joint probability value between the observation nodes in the directed acyclic graph is not greater than the joint probability threshold, then separate the current observation node and another observation node into different leaf nodes until all the leaf nodes after initial classification are observed, and output the final DNA detection data, and output it as the detection data after DNA classification.

[0020] Further, in the DNA detection data synchronization and classification method based on the decision tree model, several candidate communication channels are initially selected according to the real-time network communication data of each communication channel, specifically:

[0021] Set a network communication data threshold, and judge whether the real-time network communication data of each communication channel is greater than the network communication data threshold;

[0022] When the real-time network communication data is greater than the network communication data threshold, then use the communication channel whose real-time network communication data is greater than the network communication data threshold as a candidate communication channel;

[0023] When the real-time network communication data is greater than the network communication data threshold, then use the communication channel whose real-time network communication data is greater than the network communication data threshold as a non-candidate communication channel.

[0024] Further, in the DNA detection data synchronization and classification method based on the decision tree model, a first synchronization strategy or a second synchronization strategy is generated according to the file size information of the detection data after DNA classification and the real-time network communication data of the candidate communication channels, specifically including:

[0025] Set the data transmission required time, and calculate the required rate information of the detection data after DNA classification during data transmission according to the file size information of the detection data after DNA classification and the data transmission required time;

[0026] Determine whether the required rate information of the detected data after DNA classification during data transmission is greater than the real-time network communication data of the communication channel;

[0027] When there is at least one communication channel where the required rate information of the detected data after DNA classification during data transmission is not greater than the real-time network communication data of the communication channel, select the communication channel with the minimum real-time network communication data as the final communication channel, generate the first synchronization strategy, and output the first synchronization strategy;

[0028] When there is no communication channel where the required rate information of the detected data after DNA classification during data transmission is greater than the real-time network communication data of the communication channel, use each candidate communication channel as the communication transmission channel, generate the second synchronization strategy, and output the second synchronization strategy.

[0029] Further, in the DNA detection data synchronization and classification method based on the decision tree model, data synchronization is performed according to the first synchronization strategy or the second synchronization strategy, which specifically includes:

[0030] When it is the first synchronization strategy, continuously transmit the detected data after DNA classification according to the first synchronization strategy, and monitor the real-time network communication data of the current communication channel;

[0031] When it is the first synchronization strategy, obtain the file size information of the detected data after DNA classification, and divide the file size information of the detected data after DNA classification into several new file data for transmission, and initialize the size information of each new file data;

[0032] Calculate the required rate information of each new file data according to the size information of each new file data and the data transmission required time, and initialize several candidate communication channels for data transmission;

[0033] Allocate candidate communication channels one by one to transmit each new file data, and determine whether the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data;

[0034] When the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data, perform data synchronization according to the new file data;

[0035] When the real-time network communication data of each candidate communication channel is not all greater than the required rate information of the new file data, readjust the size information of each new file data until it is all greater than the required rate information of the new file data.

[0036] In a second aspect of the present invention, a DNA detection data synchronization and classification system based on a decision tree model is provided, including a memory and a processor. The memory includes a program for the DNA detection data synchronization and classification method based on the decision tree model. When the program for the DNA detection data synchronization and classification method based on the decision tree model is executed by the processor, the steps of any of the DNA detection data synchronization and classification methods based on the decision tree model are implemented.

[0037] In a third aspect of the present invention, a computer-readable storage medium is provided, including a program for the DNA detection data synchronization and classification method based on the decision tree model. When the program for the DNA detection data synchronization and classification method based on the decision tree model is executed by a processor, the steps of any of the DNA detection data synchronization and classification methods based on the decision tree model are implemented.

[0038] The present invention solves the defects in the background art and has the following beneficial effects:

[0039] The present invention obtains DNA detection data, constructs a decision tree model, inputs the DNA detection data into the decision tree model for initial classification, and integrates a Bayesian network. Furthermore, the process of classifying DNA detection data is monitored through the Bayesian network, and each leaf node is observed and processed to obtain the detected data after DNA classification. The real-time network communication data of each communication channel is obtained, so as to initially select several candidate communication channels according to the real-time network communication data of each communication channel, and obtain the file size information of the detected data after DNA classification and the real-time network communication data of the communication channel. Finally, a first synchronization strategy or a second synchronization strategy is generated according to the file size information of the detected data after DNA classification and the real-time network communication data of the communication channel, and data synchronization is performed according to the first synchronization strategy or the second synchronization strategy. By integrating the Bayesian network into the decision tree model, the present invention can optimize the situation of low accuracy in the process of classifying DNA detection data by the decision tree model. Secondly, the present invention can also optimize the data transmission situation in the process of data synchronization of DNA detection data, so that the DNA detection data can be synchronized to the medical platform in time, improving the query efficiency of DNA detection data. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1Shows the overall flowchart of the DNA detection data synchronization and classification method based on the decision tree model;

[0042] Figure 2 Shows a partial method flowchart of the DNA detection data synchronization and classification method based on the decision tree model;

[0043] Figure 3 Shows the system block diagram of the DNA detection data synchronization and classification system based on the decision tree model. Specific embodiments

[0044] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0045] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0046] As Figure 1 shown, the first aspect of the present invention provides a DNA detection data synchronization and classification method based on the decision tree model, which is characterized in that it specifically includes:

[0047] S102: Obtain DNA detection data, construct a decision tree model, input the DNA detection data into the decision tree model for initial classification, and fuse the Bayesian network;

[0048] S104: Monitor the process of DNA detection data during classification through the Bayesian network, perform observation processing on each leaf node, obtain the detected data after DNA classification, and obtain the real-time network communication data of each communication channel;

[0049] S106: Initially select several candidate communication channels according to the real-time network communication data of each communication channel, and obtain the file size information of the detected data after DNA classification and the real-time network communication data of the candidate communication channels;

[0050] S108: Generate a first synchronization strategy or a second synchronization strategy according to the file size information of the detected data after DNA classification and the real-time network communication data of the communication channel, and perform data synchronization according to the first synchronization strategy or the second synchronization strategy.

[0051] It should be noted that by integrating the Bayesian network into the decision tree model, the present invention can optimize the situation of low accuracy in the process of classifying DNA detection data by the decision tree model. Secondly, the present invention can also optimize the data transmission situation during the data synchronization of DNA detection data, so that the DNA detection data can be synchronized to the medical platform in time, improving the query efficiency of DNA detection data.

[0052] Further, in the DNA detection data synchronization and classification method based on the decision tree model, DNA detection data is obtained, a decision tree model is constructed, the DNA detection data is input into the decision tree model for initial classification, and the Bayesian network is integrated. Specifically:

[0053] Obtain DNA detection data, and construct a decision tree model. According to the DNA detection data, construct a sample data set, use the sample data set as the root node in the decision tree model, and set the splitting standard threshold;

[0054] Calculate the Euclidean distance values between each sample data set in the root node, and combine the sample data sets with Euclidean distance values not greater than the splitting standard threshold into one data set to form leaf nodes;

[0055] Separate the sample data with Euclidean distance values greater than the splitting standard threshold into another data set to form leaf nodes. Through continuous splitting until no new leaf nodes are generated in the decision tree model, and integrate the Bayesian network.

[0056] It should be noted that since the smaller the Euclidean distance indicates the more similar the samples, the splitting standard threshold is the Euclidean distance standard threshold; the decision tree model includes CART decision tree, C5.0 decision tree, CHAID decision tree, ID3 decision tree, etc. DNA detection data includes DNA nucleotide sequence data, genomic information, data source information, etc.

[0057] As Figure 2 shown, further, in the DNA detection data synchronization and classification method based on the decision tree model, the process of DNA detection data during classification is monitored by the Bayesian network, and each leaf node is observed and processed to obtain the detected data after DNA classification. Specifically:

[0058] S202: Obtain the leaf nodes after initial classification, and use each sample data in the leaf nodes after initial classification as an observation node, and search for samples with one of the observation nodes in each leaf node after initial classification;

[0059] S204: Construct the association relationship between the observation nodes, connect the current observation node and another observation node based on the association relationship between the observation nodes to construct a directed acyclic graph, and calculate the joint probability value between the observation nodes in the directed acyclic graph;

[0060] S206: When the joint probability value between the observed nodes in the directed acyclic graph is greater than the joint probability threshold, the current observed node and another observed node are maintained unchanged in the current leaf node;

[0061] S208: When the joint probability value between the observed nodes in the directed acyclic graph is not greater than the joint probability threshold, the current observed node and another observed node are separated into different leaf nodes until all the leaf node observations after initialization classification are completed, and the final DNA detection data is output and used as the detection data after DNA classification.

[0062] It should be noted that by fusing the decision tree model, the correlation relationship between the observed nodes is constructed, and thus the joint probability value between the observed nodes in the directed acyclic graph is calculated according to the correlation relationship of the correlation relationship between the observed nodes. By judging the joint probability value, the relationship between the observed nodes is determined. When the joint probability value is greater than the preset joint probability value, it indicates that the correlation of the two sample data is high and can be understood as the same sample data, so as to further improve the classification accuracy of the decision tree model.

[0063] Furthermore, in the DNA detection data synchronization and classification method based on the decision tree model, several candidate communication channels are initially selected according to the real-time network communication data of each communication channel, specifically:

[0064] Set the network communication data threshold, and judge whether the real-time network communication data of each communication channel is greater than the network communication data threshold;

[0065] When the real-time network communication data is greater than the network communication data threshold, the communication channel with the real-time network communication data greater than the network communication data threshold is used as the candidate communication channel;

[0066] When the real-time network communication data is greater than the network communication data threshold, the communication channel with the real-time network communication data greater than the network communication data threshold is used as the non-candidate communication channel.

[0067] It should be noted that the network communication data includes the upload information transmission rate, the download information transmission rate, the information transmission volume data within a unit time, etc. Through this method, appropriate communication channels can be selected, where the communication channels include expressions such as communication methods and communication links.

[0068] Furthermore, in the DNA detection data synchronization and classification method based on the decision tree model, a first synchronization strategy or a second synchronization strategy is generated according to the file size information of the detection data after DNA classification and the real-time network communication data of the candidate communication channels, specifically including:

[0069] Set the data transmission required time, and calculate the required rate information of the detected data after DNA classification during data transmission according to the file size information of the detected data after DNA classification and the data transmission required time;

[0070] Judge whether the required rate information of the detected data after DNA classification during data transmission is greater than the real-time network communication data of the communication channel;

[0071] When there is at least one communication channel where the required rate information of the detected data after DNA classification during data transmission is not greater than the real-time network communication data of the communication channel, select the communication channel with the minimum real-time network communication data as the final communication channel, generate the first synchronization strategy, and output the first synchronization strategy;

[0072] When there is no communication channel where the required rate information of the detected data after DNA classification during data transmission is greater than the real-time network communication data of the communication channel, use each candidate communication channel as the communication transmission channel, generate the second synchronization strategy, and output the second synchronization strategy.

[0073] It should be noted that when there is at least one communication channel where the required rate information of the detected data after DNA classification during data transmission is not greater than the real-time network communication data of the communication channel, it means that at least one communication channel is sufficient to support data transmission. On the contrary, it is not sufficient to support data transmission.

[0074] Further, in the DNA detection data synchronization and classification method based on the decision tree model, data synchronization is performed according to the first synchronization strategy or the second synchronization strategy, specifically including:

[0075] When it is the first synchronization strategy, continuously transmit the detected data after DNA classification according to the first synchronization strategy, and monitor the real-time network communication data of the current communication channel;

[0076] When it is the first synchronization strategy, obtain the file size information of the detected data after DNA classification, and divide the file size information of the detected data after DNA classification into several new file data for transmission, and initialize the size information of each new file data;

[0077] Calculate the required rate information of each new file data according to the size information of each new file data and the data transmission required time, and initialize several candidate communication channels for data transmission;

[0078] Allocate candidate communication channels one by one to transmit each new file data, and judge whether the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data;

[0079] When the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data, data synchronization is performed according to the new file data;

[0080] When the real-time network communication data of each candidate communication channel is not all greater than the required rate information of the new file data, the size information of each new file data is readjusted until it is all greater than the required rate information of the new file data.

[0081] It should be noted that through this method, the data transmission situation during data synchronization of DNA detection data is further optimized, so that the DNA detection data can be synchronized to the medical platform in a timely manner, improving the query efficiency of DNA detection data.

[0082] In addition, this method further includes:

[0083] Obtain the data storage rate information of DNA detection data under different DNA detection data storage amounts through big data, and introduce a graph neural network, and input the data storage rate information of DNA detection data under different DNA detection data storage amounts into the graph neural network;

[0084] Take the DNA detection data storage amount as the first node of the graph neural network, take the data storage rate information of the DNA detection data as the second node of the graph neural network, and construct a topological structure diagram based on the first node and the second node;

[0085] Obtain the relevant adjacency matrix based on the topological structure diagram, construct a knowledge graph, input the relevant adjacency matrix into the knowledge graph for storage, and obtain the real-time storage amount of the DNA detection database;

[0086] Input the real-time storage amount of the DNA detection database into the knowledge graph for prediction, obtain the data storage rate information of DNA detection data under the current DNA detection data storage amount, and formulate a relevant data synchronization plan according to the data storage rate information of DNA detection data under the current DNA detection data storage amount.

[0087] It should be noted that different data storage amounts will affect the data storage speed when data is synchronized and stored. Through this method, the transmission of new file data within a unit time can be dynamically adjusted, improving the rationality of data synchronization.

[0088] Among them, formulating a relevant data synchronization plan according to the data storage rate information of DNA detection data under the current DNA detection data storage amount specifically means:

[0089] Set a data storage rate threshold for DNA detection data, and determine whether the data storage rate information of DNA detection data below the current DNA detection data storage amount is greater than the data storage rate threshold of the DNA detection data;

[0090] When the data storage rate information of DNA detection data below the current DNA detection data storage amount is not greater than the data storage rate threshold of the DNA detection data, expand the data storage area of the DNA detection database, and calculate the storage amount of the storage area that needs to be expanded;

[0091] Expand the DNA detection database according to the storage amount of the storage area that needs to be expanded, and continuously monitor the storage amount of the data storage area of the DNA detection database, so that the data storage rate information of DNA detection data below the current DNA detection data storage amount in each time stamp is greater than the data storage rate threshold of the DNA detection data;

[0092] When the data storage rate information of DNA detection data below the current DNA detection data storage amount is greater than the data storage rate threshold of the DNA detection data, perform storage processing on the current DNA detection data.

[0093] It should be noted that through this method, the rationality of data synchronization can be further improved.

[0094] As Figure 3 shown, the second aspect of the present invention provides a DNA detection data synchronization and classification system 4 based on a decision tree model, including a memory 41 and a processor 42. The memory 41 includes a DNA detection data synchronization and classification method program based on a decision tree model. When the DNA detection data synchronization and classification method program based on a decision tree model is executed by the processor 42, the following technical solutions are implemented:

[0095] Obtain DNA detection data, construct a decision tree model, input the DNA detection data into the decision tree model for initial classification, and fuse a Bayesian network;

[0096] Monitor the process of DNA detection data during classification through a Bayesian network, and perform observation processing on each leaf node to obtain the detected data after DNA classification, and obtain the real-time network communication data of each communication channel;

[0097] Initial select several candidate communication channels according to the real-time network communication data of each communication channel, and obtain the file size information of the detected data after DNA classification and the real-time network communication data of the communication channel;

[0098] Generate the first synchronization strategy or the second synchronization strategy based on the file size information of the detection data classified by DNA and the real-time network communication data of the communication channel, and perform data synchronization according to the first synchronization strategy or the second synchronization strategy.

[0099] Further, in the DNA detection data synchronization and classification system based on the decision tree model, obtain the DNA detection data, construct the decision tree model, input the DNA detection data into the decision tree model for initial classification, and fuse the Bayesian network. Specifically:

[0100] Obtain the DNA detection data and construct the decision tree model. Construct the sample data set according to the DNA detection data, use the sample data set as the root node in the decision tree model, and set the splitting standard threshold.

[0101] Calculate the Euclidean distance values between each sample data set in the root node, and combine the sample data sets with Euclidean distance values not greater than the splitting standard threshold into one data set to form leaf nodes.

[0102] Separate the sample data with Euclidean distance values greater than the splitting standard threshold into another data set to form leaf nodes. Through continuous splitting until no new leaf nodes are generated in the decision tree model, and fuse the Bayesian network.

[0103] Further, in the DNA detection data synchronization and classification system based on the decision tree model, monitor the process of classifying the DNA detection data through the Bayesian network, and perform observation processing on each leaf node to obtain the detection data after DNA classification. Specifically:

[0104] Obtain the leaf nodes after initial classification, and use each sample data in the leaf nodes after initial classification as an observation node to search for samples with one of the observation nodes in each leaf node after initial classification.

[0105] Construct the association relationship between the observation nodes, connect the current observation node and another observation node based on the association relationship between the observation nodes to construct a directed acyclic graph, and calculate the joint probability value between the observation nodes in the directed acyclic graph.

[0106] When the joint probability value between the observation nodes in the directed acyclic graph is greater than the joint probability threshold, then maintain the current observation node and another observation node unchanged in the current leaf node.

[0107] When the joint probability value between the observation nodes in the directed acyclic graph is not greater than the joint probability threshold, then separate the current observation node and another observation node into different leaf nodes until all the leaf nodes after initial classification are observed, and output the final DNA detection data, which is output as the detection data after DNA classification.

[0108] Further, in the DNA detection data synchronization and classification system based on the decision tree model, several candidate communication channels are initially selected according to the real-time network communication data of each communication channel, specifically:

[0109] Set a network communication data threshold, and determine whether the real-time network communication data of each communication channel is greater than the network communication data threshold;

[0110] When the real-time network communication data is greater than the network communication data threshold, the communication channel with the real-time network communication data greater than the network communication data threshold is used as a candidate communication channel;

[0111] When the real-time network communication data is greater than the network communication data threshold, the communication channel with the real-time network communication data greater than the network communication data threshold is used as a non-candidate communication channel.

[0112] Further, in the DNA detection data synchronization and classification system based on the decision tree model, a first synchronization strategy or a second synchronization strategy is generated according to the file size information of the detection data after DNA classification and the real-time network communication data of the candidate communication channels, specifically including:

[0113] Set a data transmission required time, and calculate the required rate information of the detection data after DNA classification during data transmission according to the file size information of the detection data after DNA classification and the data transmission required time;

[0114] Determine whether the required rate information of the detection data after DNA classification during data transmission is greater than the real-time network communication data of the communication channel;

[0115] When there is at least one communication channel where the required rate information of the detection data after DNA classification during data transmission is not greater than the real-time network communication data of the communication channel, select the communication channel with the minimum real-time network communication data as the final communication channel, generate a first synchronization strategy, and output the first synchronization strategy;

[0116] When there is no communication channel where the required rate information of the detection data after DNA classification during data transmission is greater than the real-time network communication data of the communication channel, use each candidate communication channel as a communication transmission channel, generate a second synchronization strategy, and output the second synchronization strategy.

[0117] Further, in the DNA detection data synchronization and classification system based on the decision tree model, data synchronization is performed according to the first synchronization strategy or the second synchronization strategy, specifically including:

[0118] When it is the first synchronization policy, the detected data after DNA classification is continuously transmitted according to the first synchronization policy, and the real-time network communication data of the current communication channel is monitored;

[0119] When it is the first synchronization policy, the file size information of the detected data after DNA classification is obtained, and the file size information of the detected data after DNA classification is divided into several new file data for transmission, and the size information of each new file data is initialized;

[0120] According to the size information of each new file data and the data transmission required time, the required rate information of each new file data is calculated, and several candidate communication channels are initialized for data transmission;

[0121] Each candidate communication channel is allocated one by one to transmit each new file data, and it is judged whether the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data;

[0122] When the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data, data synchronization is performed according to the new file data;

[0123] When the real-time network communication data of each candidate communication channel is not all greater than the required rate information of the new file data, the size information of each new file data is readjusted until it is all greater than the required rate information of the new file data.

[0124] The third aspect of the present invention provides a computer-readable storage medium, including a program for the DNA detection data synchronization and classification method based on a decision tree model. When the program for the DNA detection data synchronization and classification method based on a decision tree model is executed by a processor, the technical solutions involved in the DNA detection data synchronization and classification method based on a decision tree model are implemented.

[0125] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0126] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units; and some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, in each embodiment of the present invention, all the functional units may be integrated into one processing unit, or each unit may be a separate unit alone, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0128] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other various media that can store program codes.

[0129] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks or optical disks and other various media that can store program codes.

[0130] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A DNA detection data synchronization and classification method based on a decision tree model, characterized in that: Specifically include: Acquire DNA test data, construct a decision tree model, input the DNA test data into the decision tree model for initial classification, and fuse the Bayesian network; The classification process of DNA detection data is monitored through the Bayesian network, and each leaf node is observed and processed to obtain the detection data after DNA classification and the real-time network communication data of each communication channel; Preliminarily selecting a number of candidate communication channels according to the real-time network communication data of each communication channel, and obtaining file size information of the detection data after DNA classification and the real-time network communication data of the candidate communication channels; Generate a first synchronization strategy or a second synchronization strategy according to the file size information of the detection data after the DNA classification and the real-time network communication data of the communication channel, and perform data synchronization according to the first synchronization strategy or the second synchronization strategy; The step of performing data synchronization according to the first synchronization strategy or the second synchronization strategy specifically includes: When the first synchronization strategy is used, the detection data after DNA classification is continuously transmitted according to the first synchronization strategy, and the real-time network communication data of the current communication channel is monitored; When the second synchronization strategy is used, the file size information of the detection data after DNA classification is obtained, and the file size information of the detection data after DNA classification is divided into a plurality of new file data for transmission, and the size information of each new file data is initialized; Calculating the required rate information of each new file data according to the size information of each new file data and the required time for data transmission, and initializing several candidate communication channels for data transmission; Allocate candidate communication channels one by one to perform data transmission on each new file data, and determine whether the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data; When the real-time network communication data of each candidate communication channel is greater than the required rate information of the new file data, data synchronization is performed according to the new file data; When the real-time network communication data of each candidate communication channel is not greater than the required rate information of the new file data, the size information of each new file data is readjusted until it is greater than the required rate information of the new file data.

2. The DNA detection data synchronization and classification method based on the decision tree model according to claim 1 is characterized in that: Acquire DNA test data, build a decision tree model, input the DNA test data into the decision tree model for initial classification, and integrate the Bayesian network, specifically: Acquire DNA test data, build a decision tree model, build a sample data set according to the DNA test data, use the sample data set as the root node in the decision tree model, and set a splitting standard threshold; Calculating the Euclidean distance value between each sample data set in the root node, and combining the sample data whose Euclidean distance value is not greater than the splitting standard threshold into one data set to form a leaf node; The sample data whose Euclidean distance value is greater than the splitting standard threshold value is separated into another data set to form leaf nodes. Through continuous splitting, no new leaf nodes are generated in the decision tree model, and the Bayesian network is integrated.

3. The DNA detection data synchronization and classification method based on the decision tree model according to claim 1, characterized in that: The classification process of DNA test data is monitored through the Bayesian network, and each leaf node is observed and processed to obtain the test data after DNA classification, specifically: Obtaining leaf nodes after initialization and classification, and using each sample data in the leaf nodes after initialization and classification as an observation node, and performing sample search with one observation node in each leaf node after initialization and classification; Constructing an association relationship between observation nodes, connecting the current observation node with another observation node based on the association relationship between the observation nodes, constructing a directed acyclic graph, and calculating the joint probability value between the observation nodes in the directed acyclic graph; When the joint probability value between the observation nodes in the directed acyclic graph is greater than the joint probability threshold, the current observation node and another observation node are maintained unchanged in the current leaf node; When the joint probability value between the observation nodes in the directed acyclic graph is not greater than the joint probability threshold, the current observation node and another observation node are separated into different leaf nodes until the observation of all the initialized classified leaf nodes is completed, and the final DNA detection data is output and output as the detection data after DNA classification.

4. The DNA detection data synchronization and classification method based on the decision tree model according to claim 1, characterized in that: According to the real-time network communication data of each communication channel, a number of candidate communication channels are initially selected, specifically: Setting a network communication data threshold, and determining whether the real-time network communication data of each communication channel is greater than the network communication data threshold; When the real-time network communication data is greater than the network communication data threshold, the communication channel whose real-time network communication data is greater than the network communication data threshold is used as a candidate communication channel; When the real-time network communication data is not greater than the network communication data threshold, the communication channel whose real-time network communication data is not greater than the network communication data threshold is used as a non-candidate communication channel.

5. The DNA detection data synchronization and classification method based on the decision tree model according to claim 1, characterized in that: Generating a first synchronization strategy or a second synchronization strategy according to the file size information of the detection data after the DNA classification and the real-time network communication data of the candidate communication channel, specifically includes: Setting a data transmission requirement time, and calculating the required rate information of the detection data after DNA classification when performing data transmission according to the file size information of the detection data after DNA classification and the data transmission requirement time; Determine whether the required rate information of the detection data after the DNA classification during data transmission is greater than the real-time network communication data of the communication channel; When there is at least one communication channel whose required rate information of the detection data after DNA classification during data transmission is not greater than the real-time network communication data of the communication channel, the communication channel with the minimum real-time network communication data is selected as the final communication channel, a first synchronization strategy is generated, and the first synchronization strategy is output; When there does not exist a communication channel in which the required rate information of the detection data after DNA classification during data transmission is not greater than the real-time network communication data of the communication channel, each candidate communication channel is used as a communication transmission channel, a second synchronization strategy is generated, and the second synchronization strategy is output.

6. A DNA detection data synchronization and classification system based on a decision tree model, characterized in that: It comprises a memory and a processor, wherein the memory comprises a DNA detection data synchronization and classification method program based on a decision tree model, and when the DNA detection data synchronization and classification method program based on a decision tree model is executed by the processor, the steps of the DNA detection data synchronization and classification method based on a decision tree model as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium, characterized in that: It includes a DNA detection data synchronization and classification method program based on a decision tree model. When the DNA detection data synchronization and classification method program based on a decision tree model is executed by a processor, the steps of the DNA detection data synchronization and classification method based on a decision tree model as described in any one of claims 1 to 5 are implemented.

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