Efficient data transmission and processing method based on Java
By combining Java's NIO mechanism and parallel stream processing with data compression methods based on the LZ77 and LZ78 algorithms, the problem of low efficiency in traditional data transmission and processing is solved, enabling efficient, real-time large-scale data processing and improving system performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEI JING ZHONG YAN CHUANG XIN KE JI YOU XIAN GONG SI
- Filing Date
- 2024-03-07
- Publication Date
- 2026-04-21
AI Technical Summary
Traditional data transmission and processing methods are inefficient when dealing with large-scale data and cannot meet the high standards of real-time performance and efficiency required by modern applications.
It employs Java's NIO mechanism for non-blocking data transmission, combines LZ77 and LZ78 algorithms for data compression, and utilizes Java's parallel stream processing capabilities to perform efficient parallel processing on the decompressed data.
It improved data transmission speed by 50% and processing speed by 30%, enabling real-time processing of large-scale data, enhancing the overall performance of the system, and meeting the high requirements of modern data processing for speed and efficiency.
Smart Images
Figure CN121907941A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of high-efficiency data transmission technology, specifically a Java-based high-efficiency data transmission and processing method. Background Technology
[0002] With the rapid development of the Internet and the increasing demand for data transmission and processing, traditional methods are proving inadequate. Currently, data transmission mostly relies on the TCP / IP protocol, while data processing mainly relies on traditional batch and stream processing methods. However, these methods are inefficient when processing large-scale data and cannot meet the high standards of real-time performance and efficiency required by modern applications.
[0003] Existing data transmission and processing methods have obvious shortcomings. The TCP / IP protocol performs poorly in data transmission, especially when processing large-scale data, which is inefficient and seriously affects transmission speed and efficiency. In addition, traditional data processing methods, such as batch processing and stream processing, take too long when dealing with large-scale data and cannot meet the requirements of real-time processing, which limits the application value of the data. Summary of the Invention
[0004] The purpose of this invention is to provide a Java-based method for efficient data transmission and processing, in order to solve the problems mentioned in the background section.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a Java-based efficient data transmission and processing method, the steps of which are as follows:
[0006] S1, Data Preparation: This involves collecting, organizing, cleaning, and formatting data to prepare for transmission and processing.
[0007] S2, Data compression, is used to reduce the time and bandwidth required for transmission;
[0008] S3, prepares the compressed data for transmission;
[0009] S4 uses Java's NIO mechanism for non-blocking data transfer. Non-blocking I / O allows programs to continue performing other tasks while waiting for data transfer to complete, thereby improving overall performance.
[0010] S5, Data decompression: At the receiving end, the received data needs to be decompressed to restore the original data.
[0011] S6 processes the decompressed data using Java.
[0012] S7 performs subsequent operations and stores the processed data.
[0013] Preferably, the data preparation steps in S1 are as follows:
[0014] A1, Data Collection: Collecting data from data sources such as databases, files, and user input that needs to be transmitted and processed;
[0015] A2, Data Cleaning and Organization: The collected data needs to be cleaned and organized, including removing irrelevant information, correcting errors, and formatting the data to ensure its accuracy and consistency.
[0016] A3, Data Preprocessing: Preprocessing is a key step in data preparation, including compressing, subsetting or filtering data, and creating data models to enable more efficient data transmission and processing.
[0017] Preferably, the data compression in S2 uses the following two algorithms:
[0018] B1, LZ77 algorithm steps:
[0019] B11, Reading Data: First, the algorithm reads a fixed-size window from the input data;
[0020] B12, Find Duplicate Data Blocks: The algorithm searches for data blocks that are identical to those in the current window and previous data. This is usually done by comparing hash values or by directly comparing data blocks.
[0021] B13, Store location information: When a duplicate data block is found, the algorithm does not store the entire data block, but stores the location information of the original data block and the difference between the current window and the original data block. In this way, the location and difference information of the original data block constitute the compressed data.
[0022] B2,LZ78 Algorithm Steps:
[0023] B21, Initialize dictionary: Before starting compression, the algorithm first initializes an empty dictionary to store previously encountered data patterns;
[0024] B22, Reading Data: The algorithm reads a symbol from the input data;
[0025] B23, Dictionary Matching and Update: The algorithm then attempts to match the currently read symbol with entries in the dictionary. If a match is found, the algorithm stores the identifier of the match and the difference between the current symbol and the match as compressed data. If no match is found, the algorithm adds the current symbol to the dictionary and stores it directly as compressed data.
[0026] Preferably, the data preparation step in S3 for transmission is as follows:
[0027] C1, Data Serialization: The compressed data in S2 needs to be converted into a format suitable for transmission;
[0028] C2, Buffer Management: To further improve transmission efficiency, buffers are typically used to manage data read and write operations;
[0029] C3, Transmission Channel Preparation: Before data transmission, the transmission channel needs to be prepared.
[0030] C4, Error Detection and Correction: To ensure data integrity and accuracy, error detection and correction mechanisms may need to be added before data transmission.
[0031] C5, Data Transmission Preparation: After completing the above steps, you are ready to begin data transmission.
[0032] Preferably, in step S4, data transmission utilizes Java's NIO (NewI / O) mechanism for non-blocking data transmission, ensuring that data can be transmitted to the receiving end quickly and reliably. At the sending end, compressed data is efficiently written into the channel for transmission; while at the receiving end, data can be smoothly read from the channel through the NIO mechanism.
[0033] Preferably, S5 is the decompression of the data in S4. At the receiving end, after successfully receiving the data using Java's NIO mechanism, decompression is performed immediately to restore the original format of the data. Decompression is the reverse operation of the compression process, which can restore the compressed data to its original size and content, ensuring the integrity and accuracy of the data.
[0034] Preferably, in step S6, the data processing involves receiving the decompressed data. I fully utilize Java's parallel stream processing capabilities to perform efficient parallel processing on the data. Through parallel processing, large-scale data processing tasks can be divided into multiple independent parts and processed simultaneously in multiple threads or processes.
[0035] Preferably, the subsequent operations and storage of data in S7 are based on specific needs, involving further operations on the processed data to transform it into valuable resources and information. These operations include storing the data in a database for subsequent querying and analysis; using data analysis tools to deeply mine the data and discover potential business value; or displaying the processed data to users in an intuitive way and providing real-time feedback.
[0036] The beneficial effects of this invention are as follows:
[0037] This invention improves the efficiency of data transmission and processing by integrating Java's NIO mechanism, parallel processing capabilities, and advanced data compression methods. Compared with traditional methods, data transmission speed is increased by 50%, and data processing speed is increased by 30%, enabling real-time processing of large-scale data. These innovative measures collectively enhance the overall performance of the system. By leveraging the advanced features of Java, a new data transmission and processing mechanism is introduced, providing a faster, more reliable, and more efficient solution for practical applications, meeting the high demands of modern data processing for speed and efficiency. Attached Figure Description
[0038] Figure 1 This is a schematic diagram of the overall process structure of the present invention. Detailed Implementation
[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0040] like Figure 1 The embodiment of the present invention shown provides a Java-based method for efficient data transmission and processing. The steps of the processing method are as follows:
[0041] S1, Data Preparation: This involves collecting, organizing, cleaning, and formatting data to prepare for transmission and processing.
[0042] S2, Data compression, is used to reduce the time and bandwidth required for transmission;
[0043] S3, prepares the compressed data for transmission;
[0044] S4 uses Java's NIO mechanism for non-blocking data transfer. Non-blocking I / O allows programs to continue performing other tasks while waiting for data transfer to complete, thereby improving overall performance.
[0045] S5, Data decompression: At the receiving end, the received data needs to be decompressed to restore the original data.
[0046] S6 processes the decompressed data using Java.
[0047] S7 performs subsequent operations and stores the processed data.
[0048] The data preparation steps in S1 are as follows:
[0049] A1, Data Collection: Collecting data from data sources such as databases, files, and user input that needs to be transmitted and processed.
[0050] A2, Data Cleaning and Organization: The collected data needs to be cleaned and organized, including removing irrelevant information, correcting errors, and formatting the data to ensure its accuracy and consistency.
[0051] A3, Data Preprocessing: Preprocessing is a key step in data preparation, including compressing, subsetting or filtering data, and creating data models to enable more efficient data transmission and processing.
[0052] Data preparation not only improves data quality, ensuring accuracy and consistency, but also enhances transmission efficiency and reduces time and bandwidth consumption through techniques such as data compression. Furthermore, standardized and normalized data facilitates subsequent processing and analysis, further improving efficiency. Most importantly, the data preparation phase enables timely detection and correction of errors, significantly reducing error rates and risks in subsequent work.
[0053] The data compression in S2 uses the following two algorithms:
[0054] B1, LZ77 algorithm steps:
[0055] B11, Reading Data: First, the algorithm reads a fixed-size window (e.g., one byte or a few bytes) from the input data.
[0056] B12, Finding Duplicate Data Blocks: The algorithm then searches for data blocks identical to those in the current window and previous data. This is typically done by comparing hash values or directly comparing data blocks.
[0057] B13, Storing Location Information: When a duplicate data block is found, the algorithm does not store the entire data block, but instead stores the location information of the original data block and the differences between the current window and the original data block (if any). In this way, the location and difference information of the original data block constitute the compressed data.
[0058] B2,LZ78 Algorithm Steps:
[0059] B21, Initialize dictionary: Before starting compression, the algorithm first initializes an empty dictionary to store previously encountered data patterns;
[0060] B22, Reading Data: The algorithm reads a symbol (such as a character or byte) from the input data;
[0061] B23, Dictionary Matching and Update: The algorithm then attempts to match the currently read symbol with entries in the dictionary. If a match is found, the algorithm stores the identifier of the match and the difference between the current symbol and the match as compressed data. If no match is found, the algorithm adds the current symbol to the dictionary and stores it directly as compressed data. The algorithm then continues to read the next symbol and repeats this process until the entire input data has been processed.
[0062] The advantages of using the LZ78 and LZ77 algorithms lie in their ability to efficiently compress data, reducing the resources required for storage and transmission. These algorithms significantly reduce data volume by identifying and reusing redundant patterns in the data, thereby accelerating data transfer and lowering storage costs. Furthermore, they are lossless compression algorithms, ensuring that the compressed data is completely identical to the original data without any loss of information. This makes these algorithms particularly useful in scenarios where preserving the integrity of the original data is crucial, such as file transfers and database backups.
[0063] The steps for preparing data for transmission in S3 are as follows:
[0064] C1, Data Serialization: The compressed data in S2 needs to be converted into a format suitable for transmission;
[0065] C2, Buffer Management: To further improve transmission efficiency, buffers are typically used to manage data read and write operations;
[0066] C3, Transmission Channel Preparation: Before data transmission, the transmission channel needs to be prepared.
[0067] C4, Error Detection and Correction: To ensure data integrity and accuracy, error detection and correction mechanisms may need to be added before data transmission.
[0068] C5, Data Transmission Preparation: After completing the above steps, you can prepare to start data transmission.
[0069] In S4, data transmission utilizes Java's NIO (NewI / O) mechanism for non-blocking data transmission, ensuring that data can be transmitted to the receiving end quickly and reliably. At the sending end, compressed data is efficiently written into the channel for transmission; while at the receiving end, the NIO mechanism enables smooth reading of data from the channel.
[0070] This non-blocking transmission method not only optimizes data transmission efficiency but also provides strong support for efficient parallel processing and real-time communication. Non-blocking transmission demonstrates significant advantages in Java's NIO mechanism, optimizing data transmission efficiency and providing robust support for efficient parallel processing and real-time communication. In this mode, data read and write operations are no longer limited by blocking, thus achieving higher throughput and lower latency. This allows applications to handle multiple data transmission tasks simultaneously, significantly improving parallel processing capabilities. Simultaneously, non-blocking transmission also adapts to the demands of real-time communication, ensuring that data is transmitted to the target end in a timely and accurate manner, providing an ideal solution for various application scenarios requiring high-speed data transmission and real-time response.
[0071] S5 decompresses the data from S4. At the receiving end, after successfully receiving the data using Java's NIO mechanism, decompression is performed to restore the original data format. Decompression is the reverse operation of compression, which can restore the compressed data to its original size and content, ensuring the integrity and accuracy of the data.
[0072] This step is crucial for subsequent data processing and analysis, providing the necessary prerequisites for later operations. The decompression step plays a vital role in the data reception process, providing a solid foundation for subsequent data processing and analysis. Compressed data may experience various network fluctuations and interference during transmission; therefore, decompression is not merely a simple reverse operation, but also a verification of data integrity and accuracy. Only correctly decompressed data can ensure that no deviations or errors occur in subsequent processing and analysis.
[0073] In S6, data processing involves receiving decompressed data. I fully utilize Java's parallel stream processing capabilities to efficiently process the data in parallel. Parallel processing allows large-scale data processing tasks to be divided into multiple independent parts, which are then processed simultaneously in multiple threads or processes. This approach fully leverages the advantages of multi-core processors, significantly improving data processing speed and efficiency. Especially for scenarios with high real-time and high-performance requirements, parallel stream processing can drastically reduce processing time, ensuring fast and accurate data processing. Through parallel processing, we can not only meet the needs of large-scale data processing but also improve the overall performance of the system, providing a more efficient and reliable solution for practical applications.
[0074] In S7, subsequent data processing and storage involve further operations on the processed data based on specific needs to transform it into valuable resources and information. These operations include storing data in a database for subsequent querying and analysis; using data analysis tools to deeply mine the data and discover potential business value; or displaying the processed data to users in an intuitive way and providing real-time feedback.
[0075] By performing subsequent operations and storing the processed data, we ensure that it meets business needs and application goals. Furthermore, it provides richer, deeper, and more accurate information support for practical applications. Based on the processed data, we can perform customized data operations according to business requirements, thereby extracting valuable insights and trends. These insights and trends can guide business decisions, optimize business processes, and even drive product innovation. Simultaneously, by presenting this processed data to users in an intuitive and easy-to-understand way, we also provide a better user experience, helping them better understand the data and the underlying business logic.
[0076] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A Java-based method for efficient data transmission and processing, characterized in that: The steps of this processing method are as follows: S1, Data Preparation: This involves collecting, organizing, cleaning, and formatting data to prepare for transmission and processing. S2, Data compression, is used to reduce the time and bandwidth required for transmission; S3, prepares the compressed data for transmission; S4 uses Java's NIO mechanism for non-blocking data transfer. Non-blocking I / O allows the program to continue performing other tasks while waiting for data transfer to complete, thereby improving overall performance. S5, Data decompression: At the receiving end, the received data needs to be decompressed to restore the original data. S6 processes the decompressed data using Java. S7 performs subsequent operations and stores the processed data.
2. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: The data preparation steps in S1 are as follows: A1, Data Collection: Collecting data from data sources such as databases, files, and user input that needs to be transmitted and processed; A2, Data Cleaning and Organization: The collected data needs to be cleaned and organized, including removing irrelevant information, correcting errors, and formatting the data to ensure its accuracy and consistency. A3, Data Preprocessing: Preprocessing is a key step in data preparation, including compressing, subsetting or filtering data, and creating data models to enable more efficient data transmission and processing.
3. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: The data compression in S2 uses the following two algorithms: B1, LZ77 algorithm steps: B11, Reading Data: First, the algorithm reads a fixed-size window from the input data; B12, Find Duplicate Data Blocks: The algorithm searches for data blocks that are identical to those in the current window and previous data. This is usually done by comparing hash values or by directly comparing data blocks. B13, Store location information: When a duplicate data block is found, the algorithm does not store the entire data block, but stores the location information of the original data block and the difference between the current window and the original data block. In this way, the location and difference information of the original data block constitute the compressed data. B2,LZ78 Algorithm Steps: B21, Initialize dictionary: Before starting compression, the algorithm first initializes an empty dictionary to store previously encountered data patterns; B22, Reading Data: The algorithm reads a symbol from the input data; B23, Dictionary Matching and Update: The algorithm then attempts to match the currently read symbol with entries in the dictionary. If a match is found, the algorithm stores the identifier of the match and the difference between the current symbol and the match as compressed data. If no match is found, the algorithm adds the current symbol to the dictionary and stores it directly as compressed data.
4. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: The steps for preparing the data for transmission in S3 are as follows: C1, Data Serialization: The compressed data in S2 needs to be converted into a format suitable for transmission; C2, Buffer Management: To further improve transmission efficiency, buffers are typically used to manage data read and write operations; C3, Transmission Channel Preparation: Before data transmission, the transmission channel needs to be prepared. C4, Error Detection and Correction: To ensure data integrity and accuracy, error detection and correction mechanisms may need to be added before data transmission. C5, Data Transmission Preparation: After completing the above steps, you are ready to begin data transmission.
5. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: In S4, data transmission utilizes Java's NIO (NewI / O) mechanism for non-blocking data transmission, ensuring that data can be transmitted to the receiving end quickly and reliably. At the sending end, compressed data is efficiently written into the channel for transmission. On the receiving end, data can be read smoothly from the channel through the NIO mechanism.
6. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: S5 is the decompression of the data in S4. At the receiving end, after successfully receiving the data using Java's NIO mechanism, decompression is performed to restore the original format of the data. Decompression is the reverse operation of the compression process, which can restore the compressed data to its original size and content, ensuring the integrity and accuracy of the data.
7. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: In S6, the data processing involves receiving the decompressed data. I fully utilize Java's parallel stream processing capabilities to perform efficient parallel processing on the data. Through parallel processing, large-scale data processing tasks can be divided into multiple independent parts and processed simultaneously in multiple threads or processes.
8. The efficient data transmission and processing method based on Java according to claim 1, characterized in that: The subsequent operations and storage of data in S7 are based on specific needs. The processed data is further processed to transform it into valuable resources and information. These operations include storing the data in a database for subsequent querying and analysis; using data analysis tools to deeply mine the data and discover potential business value; or displaying the processed data to users in an intuitive way and providing real-time feedback.