Parallel processing and optimizing method for large-scale complex data set

Abstract

The invention belongs to the technical field of data set processing, and particularly relates to a large-scale complex data set parallel processing and optimization method which comprises the following steps: step 1, data preprocessing: cleaning and normalizing data, processing missing values and abnormal values, and converting the data into a format suitable for parallel processing; the data set is divided into smaller parts according to characteristics and processing requirements of the data set, and the processing task is decomposed into a plurality of independent sub-tasks, and the dependency relationship among the tasks is designed. According to the method, the data processing speed can be increased through task decomposition and parallel processing, resource use is optimized, the result is ensured to be accurate and reliable, the flexibility and expansibility of the system are enhanced, the performance and data security are ensured through monitoring and optimization, the cost is reduced, and the productivity is improved.

Description

Technical Field

[0001] This invention belongs to the field of dataset processing technology, specifically relating to parallel processing and optimization methods for large-scale complex datasets. Background Technology

[0002] Large-scale complex datasets refer to data collections containing a large number of data points (usually millions or even billions) and with diverse structures and types. These datasets are typically characterized by their large size, diverse data types, fast data generation and processing speed, wide range of authentic data sources, and complex relationships and dependencies between data points. Typical applications include big data analytics, data-driven decision support, real-time monitoring and prediction, and artificial intelligence training. Processing these datasets requires the use of big data technologies and tools (such as Hadoop, Spark, NoSQL databases, distributed computing and storage systems, and data stream processing frameworks), and requires professional data science and engineering teams to design and implement efficient data processing and analysis workflows.

[0003] Existing methods for processing large-scale complex datasets have low processing speeds and lack means to optimize resource usage. They also suffer from poor system flexibility and scalability, making it difficult to handle conflicts and duplicate data during the merging process, resulting in high data processing costs. To address these issues, we propose a parallel processing and optimization method for large-scale complex datasets. Summary of the Invention

[0004] The purpose of this invention is to provide a parallel processing and optimization method for large-scale complex datasets. This method can improve data processing speed, optimize resource utilization, ensure accurate and reliable results, and enhance system flexibility and scalability through task decomposition and parallel processing. It can also ensure performance and data security through monitoring and optimization, while reducing costs and increasing productivity.

[0005] The specific technical solution adopted by this invention is as follows:

[0006] Parallel processing and optimization methods for large-scale complex datasets include the following steps:

[0007] Step 1. Data preprocessing: Clean and normalize the data, handle missing and outlier values, and convert the data into a format suitable for parallel processing;

[0008] Step 2. Data Sharding: Divide the dataset into smaller parts based on its characteristics and processing requirements;

[0009] Step 3. Task decomposition: Decompose the processing task into multiple independent subtasks and design the dependencies between tasks;

[0010] Step 4. Select a parallel framework: Choose a suitable parallel processing framework or platform, including Hadoop, Spark, or MPI, and configure and optimize the selected framework;

[0011] Step 5. Data Distribution and Scheduling: Distribute data and tasks to different computing nodes, and use scheduling algorithms to optimize resource utilization, reduce waiting time and resource waste;

[0012] Step 6. Parallel execution: Each computing node independently executes its assigned task and processes data slices simultaneously;

[0013] Step 7. Merging intermediate results: Collect the intermediate results from each node, merge or summarize these results, and handle conflicts and duplicate data during the merging process;

[0014] Step 8. Result Verification and Optimization: Verify the results of parallel processing, analyze and optimize the processing process, and identify and resolve performance bottlenecks;

[0015] Step 9. Post-processing and storage: Perform necessary post-processing on the final result, including format conversion and data compression, and store the processed data in a suitable storage system.

[0016] In a preferred embodiment, the data preprocessing further includes determining the data source, including databases, file systems, APIs, and sensors; extracting data from the data source by writing scripts or using existing tools; filling missing values ​​using the mean, median, mode, or predictive models; identifying outliers using box plots, standard deviation, or machine learning methods; deleting or correcting outliers as appropriate; converting string-type numbers to numeric types; scaling the data to a uniform range using normalization methods; detecting duplicate data using unique identifiers or key fields; retaining one instance and deleting the remaining duplicates; checking the consistency of data across different tables or data sources; selecting task-relevant features; and removing redundant or useless features.

[0017] In a preferred embodiment, the data sharding further includes selecting sharding criteria based on the characteristics of the dataset, including time, geographical location, and category; sharding methods including range sharding, hash sharding, and list sharding; analyzing the distribution of data; counting the amount of data at each possible sharding point; evaluating the uniformity after sharding; setting sharding boundaries based on the analysis results, including time range and geographical region; dividing the dataset according to the determined sharding boundaries; allocating data to each shard; designing the storage structure of the sharded data, including a file directory structure or a database partition table; and writing the sharded data to the corresponding storage location.

[0018] In a preferred embodiment, the task decomposition further includes clarifying the overall processing task requirements, objectives, and outputs; assessing the complexity and scale of the task; identifying key parts and difficulties in the task; selecting decomposition criteria based on the characteristics of the task, including functional modules, data processing steps, or computational steps; decomposing the task into functionally independent modules, each module corresponding to a subtask; setting clear objectives and outputs for each subtask; identifying the dependencies between subtasks; drawing a task dependency graph; clarifying the execution order and parallel execution possibilities of the tasks; grouping subtasks that can be executed in parallel; simultaneously planning task groups to be executed sequentially; further decomposing complex subtasks; allocating subtasks to different computing nodes or processing units; starting the execution of subtasks according to the plan; monitoring the execution status of each subtask in real time to ensure that the subtasks are completed within the planned time; handling anomalies in a timely manner; collecting the execution results of each subtask; and handling conflicts when integrating subtask results.

[0019] In a preferred embodiment, selecting a parallel framework further includes assessing the requirements of the processing task, including data volume, computational complexity, and processing time requirements; identifying the hardware and software constraints of the existing system, including computing resources, storage resources, and network bandwidth; comparing the functional characteristics of each parallel processing framework, including data processing capabilities, supported programming languages, and fault tolerance mechanisms; analyzing the performance of each framework under different data scales and task complexities, including processing speed, scalability, and resource utilization; evaluating whether each framework is suitable for a specific processing task type, including batch processing, real-time processing, and stream processing; selecting parallel processing frameworks based on requirements analysis and feature comparison; adjusting the framework's configuration parameters, including memory allocation, parallelism settings, and task scheduling strategies, according to the specific processing task and data scale; optimizing the framework's operating parameters and resource configuration through performance testing and analysis; and integrating the selected parallel framework into the existing system.

[0020] In a preferred embodiment, the data distribution and scheduling further includes selecting a suitable data distribution strategy based on task characteristics and node resources, including hash-based distribution and range-based distribution, distributing data to various computing nodes according to the distribution strategy to avoid overloading some nodes, distributing corresponding subtasks to various nodes, and selecting a scheduling algorithm, including round-robin scheduling and shortest task first scheduling.

[0021] In a preferred embodiment, the parallel execution further includes initiating assigned subtasks on each node, with each computing node independently executing the assigned tasks and processing data slices simultaneously. Multi-threading or multi-processing techniques are utilized to improve execution efficiency. Data slices are processed in real-time according to task type. Data locks or distributed lock mechanisms are used to ensure data consistency when parallel tasks access shared resources. When multiple data operations are involved, transaction management is used to ensure the atomicity, consistency, isolation, and durability of the operations. Intermediate processing results are cached in local storage or a distributed caching system to reduce redundant computation. The task execution status is monitored in real-time, and errors or anomalies are detected and recorded. In the event of an error or node failure, failed tasks are automatically restarted. Checkpointing mechanisms or logging are used. After each computing node completes its task, the final processing results are aggregated in a designated storage location. Temporary data and cache generated during task execution are cleaned up, system resources are released, and performance metrics of each computing node, including CPU utilization, memory utilization, and task execution time, are monitored in real-time. Performance monitoring data is analyzed, and system configuration and task scheduling strategies are optimized based on the analysis results.

[0022] In a preferred embodiment, the intermediate result merging further includes each computing node transmitting the processed intermediate results to a designated aggregation node or storage system, temporarily storing the intermediate results in a high-speed storage device or distributed storage system of the aggregation node, classifying and organizing the intermediate results according to task type and data characteristics, preparing for merging, selecting a merging strategy (including key-based merging and time-window merging), reading the intermediate results transmitted by each node from the storage system, sorting the read data according to the merging strategy, detecting data conflicts during the merging process (including key conflicts and time conflicts), handling detected conflicts according to priority and latest value overwrite conflict resolution strategies, filtering duplicate data during the merging process, performing the actual merging operation on the intermediate results according to the merging strategy, generating a summary result, verifying the merged result, storing the final merged result in a designated storage system or database, and backing up the final result.

[0023] In a preferred embodiment, the result verification and optimization further includes checking the correctness of the parallel processing results, verifying the integrity of the data, checking the consistency of the results, comparing the processing results with known benchmark data to confirm the accuracy of the results, randomly selecting a portion of sample data for detailed verification, performing statistical analysis on the results, generating various statistical indicators and reports, identifying outliers or abnormal patterns in the results, further analyzing their causes, collecting performance indicators of each node during parallel processing, identifying performance bottlenecks and inefficient links in the system by analyzing performance indicators, designing targeted optimization schemes based on the performance analysis results, including algorithm optimization and resource allocation adjustment, implementing the optimization schemes, adjusting and optimizing the system, and optimizing key algorithms and code.

[0024] In a preferred embodiment, the post-processing and storage further includes confirming that all parallel processing tasks have been completed and collecting all final results, summarizing the final results of each node to the central processing node, parsing the summarized result data, converting the data according to a predetermined standard format, performing data verification during the format conversion process, compressing the data, verifying the integrity of the compressed data, encrypting the data, verifying the readability of the encrypted data, configuring the storage system according to the data characteristics and usage requirements, and writing the processed data into the selected storage system.

[0025] The technical effects achieved by this invention are as follows:

[0026] By decomposing tasks into parallel processing, the speed of data processing can be greatly improved and the overall processing time reduced. Choosing a suitable parallel processing framework and scheduling strategy can optimize the use of computing and storage resources, reduce resource waste, and enable the processing of large-scale complex datasets. Through data sharding and task decomposition, it can adapt to data processing needs of various scales and complexities. By choosing different sharding and distribution strategies, it can flexibly adapt to the characteristics and processing needs of different datasets.

[0027] During the intermediate result merging process, data conflicts and duplications are handled to ensure the accuracy and uniqueness of the final result. Multiple verification steps, including correctness checks, benchmark comparisons, and sample verification, are used to ensure the accuracy and reliability of the processing results. Performance indicators during parallel execution are monitored in real time to identify and resolve performance bottlenecks, ensuring the stability and efficient operation of the system. Data is encrypted during post-processing to ensure data security during storage and transmission. The final result is backed up to ensure data recoverability in case of accidents.

[0028] The choice of task decomposition and parallel frameworks gives the system high flexibility and scalability, allowing it to be adjusted and optimized according to different task requirements. By selecting scalable parallel processing frameworks such as Hadoop, Spark, or MPI, the system can be easily expanded to handle larger-scale data or more complex tasks. The system provides real-time monitoring and performance analysis functions, facilitating timely discovery and resolution of problems, maintaining normal system operation, and continuous optimization and adjustment strategies to continuously improve system performance and processing capabilities. Through effective resource scheduling and optimization, the system can maximize the use of existing resources, reduce additional hardware and energy costs, significantly reduce data processing time, and improve the efficiency of data analysis and decision-making, thereby enhancing overall productivity. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the parallel processing and optimization method for large-scale complex datasets according to the present invention. Detailed Implementation

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Please see Figure 1 As shown, this invention provides a method for parallel processing and optimization of large-scale complex datasets. The parallel processing and optimization method includes the following steps:

[0032] Step 1. Data preprocessing: Clean and normalize the data, handle missing and outlier values, and convert the data into a format suitable for parallel processing;

[0033] Step 2. Data Sharding: Divide the dataset into smaller parts based on its characteristics and processing requirements;

[0034] Step 3. Task decomposition: Decompose the processing task into multiple independent subtasks and design the dependencies between tasks;

[0035] Step 4. Select a parallel framework: Choose a suitable parallel processing framework or platform, including Hadoop, Spark, or MPI, and configure and optimize the selected framework;

[0036] Step 5. Data Distribution and Scheduling: Distribute data and tasks to different computing nodes, and use scheduling algorithms to optimize resource utilization, reduce waiting time and resource waste;

[0037] Step 6. Parallel execution: Each computing node independently executes its assigned task and processes data slices simultaneously;

[0038] Step 7. Merging intermediate results: Collect the intermediate results from each node, merge or summarize these results, and handle conflicts and duplicate data during the merging process;

[0039] Step 8. Result Verification and Optimization: Verify the results of parallel processing, analyze and optimize the processing process, and identify and resolve performance bottlenecks;

[0040] Step 9. Post-processing and storage: Perform necessary post-processing on the final result, including format conversion and data compression, and store the processed data in a suitable storage system.

[0041] The data preprocessing also includes determining the data source, including databases, file systems, APIs, and sensors; extracting data from the data source by writing scripts or using existing tools; filling missing values ​​using the mean, median, mode, or predictive models; identifying outliers using box plots, standard deviation, or machine learning methods; deleting or correcting outliers as appropriate; converting string-type numbers to numeric types; scaling the data to a uniform range using normalization methods; detecting duplicate data using unique identifiers or key fields; retaining one instance and deleting the rest of the duplicates; checking the consistency of data across different tables or data sources; selecting task-relevant features; and removing redundant or useless features.

[0042] The data sharding also includes selecting sharding criteria based on the characteristics of the dataset, including time, geographical location, and category. Sharding methods include range sharding, hash sharding, and list sharding. The data distribution is analyzed, the amount of data at each possible sharding point is counted, the uniformity after sharding is evaluated, and sharding boundaries are set based on the analysis results, including time range and geographical region. The dataset is divided according to the determined sharding boundaries, the data is allocated to each shard, the storage structure of the sharded data is designed, including file directory structure or database partition table, and the sharded data is written to the corresponding storage location.

[0043] The task decomposition also includes clarifying the overall processing task requirements, objectives, and outputs; assessing the complexity and scale of the task; identifying key parts and difficulties in the task; selecting decomposition criteria based on the characteristics of the task, including functional modules, data processing steps, or computational steps; decomposing the task into functionally independent modules, each module corresponding to a subtask; setting clear objectives and outputs for each subtask; identifying the dependencies between subtasks; drawing a task dependency graph; clarifying the execution order and parallel execution possibilities of the tasks; grouping subtasks that can be executed in parallel; simultaneously planning to execute task groups sequentially; further breaking down complex subtasks; allocating subtasks to different computing nodes or processing units; starting the execution of subtasks according to the plan; monitoring the execution status of each subtask in real time to ensure that subtasks are completed within the planned time; handling anomalies promptly; collecting the execution results of each subtask; and handling conflicts when integrating subtask results.

[0044] The selection of a parallel framework also includes evaluating the requirements of the processing task, including data volume, computational complexity, and processing time requirements; identifying the hardware and software constraints of the existing system, including computing resources, storage resources, and network bandwidth; comparing the functional characteristics of each parallel processing framework, including data processing capabilities, supported programming languages, and fault tolerance mechanisms; analyzing the performance of each framework under different data scales and task complexities, including processing speed, scalability, and resource utilization; evaluating whether each framework is suitable for specific processing task types, including batch processing, real-time processing, and stream processing; selecting parallel processing frameworks based on requirements analysis and feature comparison; adjusting the framework's configuration parameters, including memory allocation, parallelism settings, and task scheduling strategies, according to the specific processing task and data scale; optimizing the framework's operating parameters and resource configuration through performance testing and analysis; and integrating the selected parallel framework into the existing system.

[0045] The data distribution and scheduling also includes selecting appropriate data distribution strategies based on task characteristics and node resources, including hash-based distribution and range-based distribution, distributing data to various computing nodes according to the distribution strategy to avoid overloading some nodes, distributing corresponding subtasks to various nodes, and selecting scheduling algorithms, including round-robin scheduling and shortest task first scheduling.

[0046] The parallel execution also includes launching assigned subtasks on each node, with each computing node independently executing the assigned tasks and processing data slices simultaneously. Multi-threading or multi-processing techniques are used to improve execution efficiency. Data slices are processed in real-time according to task type. Data locks or distributed lock mechanisms are used to ensure data consistency when parallel tasks access shared resources. When multiple data operations are involved, transaction management is used to ensure the atomicity, consistency, isolation, and durability of the operations. Intermediate processing results are cached in local storage or a distributed caching system to reduce redundant computation. The task execution status is monitored in real-time, and errors or anomalies are detected and recorded. In the event of an error or node failure, failed tasks are automatically restarted. Checkpointing mechanisms or logging are used. After each computing node completes its task, the final processing results are aggregated to a designated storage location. Temporary data and cache generated during task execution are cleaned up, system resources are released, and performance metrics of each computing node, including CPU utilization, memory utilization, and task execution time, are monitored in real-time. Performance monitoring data is analyzed, and system configuration and task scheduling strategies are optimized based on the analysis results.

[0047] The intermediate result merging process also includes each computing node transmitting the processed intermediate results to a designated aggregation node or storage system, temporarily storing the intermediate results in the high-speed storage device or distributed storage system of the aggregation node, classifying and organizing the intermediate results according to task type and data characteristics, preparing for merging, selecting a merging strategy (including key-based merging and time-window merging), reading the intermediate results transmitted from each node from the storage system, sorting the read data according to the merging strategy, detecting data conflicts during the merging process (including critical conflicts and time conflicts), handling detected conflicts according to priority and latest value overwrite conflict resolution strategies, filtering duplicate data during the merging process, performing the actual merging operation on the intermediate results according to the merging strategy, generating a summary result, verifying the merged result, storing the final merged result in a designated storage system or database, and backing up the final result.

[0048] The result verification and optimization also includes checking the correctness of the parallel processing results, verifying the integrity of the data, checking the consistency of the results, comparing the processing results with known benchmark data to confirm the accuracy of the results, randomly selecting a portion of sample data for detailed verification, performing statistical analysis on the results, generating various statistical indicators and reports, identifying outliers or abnormal patterns in the results, further analyzing their causes, collecting performance indicators of each node in the parallel processing process, identifying performance bottlenecks and inefficient links in the system by analyzing performance indicators, designing targeted optimization schemes based on the performance analysis results, including algorithm optimization, resource allocation adjustment, implementing optimization schemes, adjusting and optimizing the system, and optimizing key algorithms and code.

[0049] The post-processing and storage also includes confirming that all parallel processing tasks have been completed and collecting all final results, summarizing the final results of each node to the central processing node, parsing the summarized result data, converting the data according to a predetermined standard format, performing data verification during the format conversion process, compressing the data, verifying the integrity of the compressed data, encrypting the data, verifying the readability of the encrypted data, configuring the storage system according to the data characteristics and usage requirements, and writing the processed data into the selected storage system.

[0050] In this invention, by decomposing tasks into parallel processing, the speed of data processing can be greatly improved and the overall processing time reduced. By selecting a suitable parallel processing framework and scheduling strategy, the use of computing and storage resources can be optimized, resource waste can be reduced, and large-scale complex datasets can be processed. Through data sharding and task decomposition, it can adapt to data processing needs of various scales and complexities. By selecting different sharding and distribution strategies, it can flexibly adapt to the characteristics and processing needs of different datasets.

[0051] During the intermediate result merging process, data conflicts and duplications are handled to ensure the accuracy and uniqueness of the final result. Multiple verification steps, including correctness checks, benchmark comparisons, and sample verification, are used to ensure the accuracy and reliability of the processing results. Performance indicators during parallel execution are monitored in real time to identify and resolve performance bottlenecks, ensuring the stability and efficient operation of the system. Data is encrypted during post-processing to ensure data security during storage and transmission. The final result is backed up to ensure data recoverability in case of accidents.

[0052] The choice of task decomposition and parallel frameworks gives the system high flexibility and scalability, allowing it to be adjusted and optimized according to different task requirements. By selecting scalable parallel processing frameworks such as Hadoop, Spark, or MPI, the system can be easily expanded to handle larger-scale data or more complex tasks. The system provides real-time monitoring and performance analysis functions, facilitating timely discovery and resolution of problems, maintaining normal system operation, and continuous optimization and adjustment strategies to continuously improve system performance and processing capabilities. Through effective resource scheduling and optimization, the system can maximize the use of existing resources, reduce additional hardware and energy costs, significantly reduce data processing time, and improve the efficiency of data analysis and decision-making, thereby enhancing overall productivity.

[0053] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for parallel processing and optimization of large-scale complex datasets, characterized by: Parallel processing and optimization methods include the following steps: Step 1. Data preprocessing: Clean and normalize the data, handle missing and outlier values, and convert the data into a format suitable for parallel processing; Step 2. Data Sharding: Divide the dataset into smaller parts based on its characteristics and processing requirements; Step 3. Task decomposition: Decompose the processing task into multiple independent subtasks and design the dependencies between tasks; Step 4. Select a parallel framework: Choose a suitable parallel processing framework or platform, including Hadoop, Spark, or MPI, and configure and optimize the selected framework; Step 5. Data Distribution and Scheduling: Distribute data and tasks to different computing nodes, and use scheduling algorithms to optimize resource usage, reduce waiting time and resource waste; Step 6. Parallel execution: Each computing node independently executes its assigned task and processes data slices simultaneously; Step 7. Merging intermediate results: Collect the intermediate results from each node, merge or summarize these results, and handle conflicts and duplicate data during the merging process; Step 8. Result Verification and Optimization: Verify the results of parallel processing, analyze and optimize the processing process, and identify and resolve performance bottlenecks; Step 9. Post-processing and storage: Perform necessary post-processing on the final result, including format conversion and data compression, and store the processed data in a suitable storage system.

2. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The data preprocessing also includes determining the data source, including databases, file systems, APIs, and sensors; extracting data from the data source by writing scripts or using existing tools; filling missing values ​​using the mean, median, mode, or predictive models; identifying outliers using box plots, standard deviation, or machine learning methods; deleting or correcting outliers as appropriate; converting string-type numbers to numeric types; scaling the data to a uniform range using normalization methods; detecting duplicate data using unique identifiers or key fields; retaining one instance and deleting the rest of the duplicates; checking the consistency of data across different tables or data sources; selecting task-relevant features; and removing redundant or useless features.

3. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The data sharding also includes selecting sharding criteria based on the characteristics of the dataset, including time, geographical location, and category. Sharding methods include range sharding, hash sharding, and list sharding. The data distribution is analyzed, the amount of data at each possible sharding point is counted, the uniformity after sharding is evaluated, and sharding boundaries are set based on the analysis results, including time range and geographical region. The dataset is divided according to the determined sharding boundaries, the data is allocated to each shard, the storage structure of the sharded data is designed, including file directory structure or database partition table, and the sharded data is written to the corresponding storage location.

4. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The task decomposition also includes clarifying the overall processing task requirements, objectives, and outputs; assessing the complexity and scale of the task; identifying key parts and difficulties in the task; selecting decomposition criteria based on the characteristics of the task, including functional modules, data processing steps, or computational steps; decomposing the task into functionally independent modules, each module corresponding to a subtask; setting clear objectives and outputs for each subtask; identifying the dependencies between subtasks; drawing a task dependency graph; clarifying the execution order and parallel execution possibilities of the tasks; grouping subtasks that can be executed in parallel; simultaneously planning to execute task groups sequentially; further breaking down complex subtasks; allocating subtasks to different computing nodes or processing units; starting the execution of subtasks according to the plan; monitoring the execution status of each subtask in real time to ensure that subtasks are completed within the planned time; handling anomalies promptly; collecting the execution results of each subtask; and handling conflicts when integrating subtask results.

5. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The selection of a parallel framework also includes evaluating the requirements of the processing task, including data volume, computational complexity, and processing time requirements; identifying the hardware and software constraints of the existing system, including computing resources, storage resources, and network bandwidth; comparing the functional characteristics of each parallel processing framework, including data processing capabilities, supported programming languages, and fault tolerance mechanisms; analyzing the performance of each framework under different data scales and task complexities, including processing speed, scalability, and resource utilization; evaluating whether each framework is suitable for specific processing task types, including batch processing, real-time processing, and stream processing; selecting parallel processing frameworks based on requirements analysis and feature comparison; adjusting the framework's configuration parameters, including memory allocation, parallelism settings, and task scheduling strategies, according to the specific processing task and data scale; optimizing the framework's operating parameters and resource configuration through performance testing and analysis; and integrating the selected parallel framework into the existing system.

6. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The data distribution and scheduling also includes selecting appropriate data distribution strategies based on task characteristics and node resources, including hash-based distribution and range-based distribution, distributing data to various computing nodes according to the distribution strategy to avoid overloading some nodes, distributing corresponding subtasks to various nodes, and selecting scheduling algorithms, including round-robin scheduling and shortest task first scheduling.

7. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The parallel execution also includes launching assigned subtasks on each node, with each computing node independently executing the assigned tasks and processing data slices simultaneously. Multi-threading or multi-processing techniques are used to improve execution efficiency. Data slices are processed in real-time according to task type. Data locks or distributed lock mechanisms are used to ensure data consistency when parallel tasks access shared resources. When multiple data operations are involved, transaction management is used to ensure the atomicity, consistency, isolation, and durability of the operations. Intermediate processing results are cached in local storage or a distributed caching system to reduce redundant computation. The task execution status is monitored in real-time, and errors or anomalies are detected and recorded. In the event of an error or node failure, failed tasks are automatically restarted. Checkpointing mechanisms or logging are used. After each computing node completes its task, the final processing results are aggregated to a designated storage location. Temporary data and cache generated during task execution are cleaned up, system resources are released, and performance metrics of each computing node, including CPU utilization, memory utilization, and task execution time, are monitored in real-time. Performance monitoring data is analyzed, and system configuration and task scheduling strategies are optimized based on the analysis results.

8. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The intermediate result merging process also includes each computing node transmitting the processed intermediate results to a designated aggregation node or storage system, temporarily storing the intermediate results in the high-speed storage device or distributed storage system of the aggregation node, classifying and organizing the intermediate results according to task type and data characteristics, preparing for merging, selecting a merging strategy (including key-based merging and time-window merging), reading the intermediate results transmitted from each node from the storage system, sorting the read data according to the merging strategy, detecting data conflicts during the merging process (including critical conflicts and time conflicts), handling detected conflicts according to priority and latest value overwrite conflict resolution strategies, filtering duplicate data during the merging process, performing the actual merging operation on the intermediate results according to the merging strategy, generating a summary result, verifying the merged result, storing the final merged result in a designated storage system or database, and backing up the final result.

9. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The result verification and optimization also includes checking the correctness of the parallel processing results, verifying the integrity of the data, checking the consistency of the results, comparing the processing results with known benchmark data to confirm the accuracy of the results, randomly selecting a portion of sample data for detailed verification, performing statistical analysis on the results, generating various statistical indicators and reports, identifying outliers or abnormal patterns in the results, further analyzing their causes, collecting performance indicators of each node in the parallel processing process, identifying performance bottlenecks and inefficient links in the system by analyzing performance indicators, designing targeted optimization schemes based on the performance analysis results, including algorithm optimization, resource allocation adjustment, implementing optimization schemes, adjusting and optimizing the system, and optimizing key algorithms and code.

10. The parallel processing and optimization method for large-scale complex datasets according to claim 1, characterized in that: The post-processing and storage also includes confirming that all parallel processing tasks have been completed and collecting all final results, summarizing the final results of each node to the central processing node, parsing the summarized result data, converting the data according to a predetermined standard format, performing data verification during the format conversion process, compressing the data, verifying the integrity of the compressed data, encrypting the data, verifying the readability of the encrypted data, configuring the storage system according to the data characteristics and usage requirements, and writing the processed data into the selected storage system.

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