Efficient streaming media data analysis method

Abstract

The invention discloses an efficient streaming media data analysis method, and relates to the technical field of data analysis. Comprising data cleaning, data denoising and data format conversion to ensure data quality and data availability, and the data denoising comprises the following steps: step 1, training a CNN model by using a marked data set to obtain a CNN model data set; model parameters are optimized through a back propagation algorithm, and noise is continuously reduced through loop iteration until a noise value reaches a set value. According to the efficient streaming media data analysis method, the streaming media data can be analyzed and processed in real time, and the requirements of real-time monitoring and prediction are met. In addition, through the advanced data processing and analysis technology, the method can accurately capture complex data modes and changes, and the accuracy of analysis results is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an efficient streaming media data analysis method. Background Art

[0002] With the rapid development of the Internet and digital technology, streaming media data has been widely used in multimedia fields such as video and audio. The real-time, large data volume and diversity of streaming media data make it an important challenge to analyze and process it efficiently. Traditional streaming media data analysis methods often have problems such as slow processing speed, low accuracy and high resource consumption. It is necessary to find a more efficient streaming media data analysis method to meet the requirements of real-time and accuracy.

[0003] At present, the analysis methods for streaming media data mainly adopt methods based on rule engines or machine learning algorithms. The method based on rule engines requires a large number of rules and logics to be defined in advance, which is often difficult to adapt to complex and changeable streaming media data scenarios, and the cost of updating and maintaining rules is high. The method based on machine learning algorithms usually requires a large amount of labeled data for training, and model training takes a long time and cannot meet real-time requirements. Therefore, the existing technical solutions have problems such as slow processing speed, low accuracy, and high resource consumption when processing streaming media data.

[0004] Disadvantages: 1. Slow processing speed: When processing large-scale streaming media data, the existing technical solutions have high algorithm complexity, resulting in slow processing speed and unable to meet the needs of real-time analysis.

[0005] 2. Low accuracy: The rule engine-based approach is limited by the formulation and updating of rules and often cannot accurately capture complex data patterns and changes, resulting in low accuracy of analysis results.

[0006] 3. High resource consumption: Machine learning algorithm-based methods require a large amount of labeled data and computing resources for model training, which is time-consuming and labor-intensive. In practical applications, there may be problems of overfitting or underfitting, which affects the reliability of the analysis results.

[0007] Therefore, the existing technical solutions have a series of problems and shortcomings when processing streaming media data. It is necessary to find an efficient streaming media data analysis method to solve these problems and improve the efficiency and accuracy of analysis and processing. Summary of the invention

[0008] The purpose of the present invention is to provide an efficient streaming media data analysis method to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solution: an efficient streaming media data analysis method, comprising: Data collection and preprocessing: Obtain data from streaming media sources and perform preprocessing. Data preprocessing includes at least data cleaning, data denoising, and data format conversion to ensure data quality and data availability. The data denoising comprises the following steps: Step 1: Use the labeled data set to train the CNN model, optimize the model parameters through the back propagation algorithm, and continuously reduce the noise through loop iteration until the noise value reaches the set value; Step 2: Select an adaptive filter and apply the selected adaptive filter to each pixel or local area output by the CNN model to effectively remove residual noise and improve the quality and reliability of the image processing results; Real-time streaming media processing: processing and analyzing real-time streaming media data, wherein the processing of real-time streaming media data at least includes real-time feature extraction, real-time pattern recognition and real-time anomaly detection; Distributed computing and parallel processing: Distributed computing and parallel processing technologies are used to achieve efficient processing and analysis of large-scale streaming media data, thereby improving data processing speed and efficiency; Real-time feedback and adjustment: Establish a real-time feedback mechanism to adjust CNN model parameters and algorithms based on analysis results, achieve dynamic optimization and adjustment, and continuously improve the accuracy and efficiency of data analysis.

[0010] Furthermore, the processing method of the data format conversion is: Obtain sample data in the source and target formats that need to be converted, analyze the data structures of the source and target formats in detail, and identify the particularities of different data formats; Create a comparison table of source data and target format data fields to clarify the correspondence between each field. If the field name in the target format is inconsistent with the source data, it needs to be renamed. Set corresponding conversion rules according to the field types of the source data and target format data; Verify the source data to ensure its integrity and accuracy, and design a fault-tolerant mechanism to ensure the stability of the conversion process; Transform data through parallel processing technology; Compare the source data and the target format data to confirm that the converted data maintains the logical consistency of the original data, and then use automated tools to check the data quality to ensure that there are no omissions or errors.

[0011] Furthermore, the real-time feature extraction of real-time streaming media data is achieved using deep learning technology based on deep autoencoders and attention mechanisms.

[0012] Furthermore, the real-time pattern recognition of real-time streaming media data is achieved using deep learning technology based on attention mechanism and Transformer model.

[0013] Furthermore, the anomaly detection method based on the generative adversarial network realizes the real-time anomaly detection of the real-time streaming media data.

[0014] Furthermore, the method of distributed computing and parallel processing is: Establish a computing framework, store data in a distributed manner on multiple nodes, and process data in parallel on multiple computing nodes; Based on the parallel processing algorithm of the computing framework, the data is broken down into small blocks and processed in parallel on multiple computing nodes; Design dynamic task scheduling and load balancing strategies for adaptive data allocation.

[0015] Furthermore, the processing method of the real-time feedback and adjustment is: Establish a real-time monitoring system to capture system output results in real time, compare the system output results with the expected results, and obtain the deviation value; Build an end-to-end data processing pipeline through a real-time data stream processing framework to receive, process and feedback data in real time; The monitoring system transmits the output results and deviation value data back to the model through the data processing pipeline, and updates the model parameters and algorithms in real time; Establish visual monitoring tools to monitor system operation status, provide real-time visualization of key performance indicators, and set up early warning mechanisms to promptly detect and resolve potential problems.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This efficient streaming media data analysis method can realize real-time analysis and processing of streaming media data, meeting the needs of real-time monitoring and prediction.

[0017] In addition, through advanced data processing and analysis techniques, this method can accurately capture complex data patterns and changes and improve the accuracy of analysis results.

[0018] In addition, this method uses distributed computing and parallel processing technology, which can efficiently process large-scale streaming media data and improve data processing speed and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Data collection and preprocessing: Data is obtained from streaming media sources and preprocessed. Data preprocessing includes at least data cleaning, data denoising and data format conversion to ensure data quality and data availability. Combined with the experience and knowledge of domain experts, adaptive rule detection based on machine learning can identify and remove common abnormal data in specific fields.

[0022] For denoising techniques, a deep learning-based approach was used, combining convolutional neural networks (CNNs) and adaptive filters to improve data clarity and remove various types of noise.

[0023] The specific steps of data denoising are as follows: Data preprocessing: First, the input data is preprocessed, including removing outliers, standardizing data, and other operations to ensure the quality and reliability of the data.

[0024] Design a CNN model to learn the features in the data and extract effective signals in the case of large noise. It is known that CNN performs well in the field of image processing and is suitable for processing data with spatial correlation.

[0025] In order to better adapt to different types of noise, an adaptive filter is introduced. It is known that this filter can automatically adjust the filtering parameters according to the characteristics of the data and the type of noise, thereby effectively removing noise and retaining important information of the signal.

[0026] The CNN model is trained using a labeled data set, and the model parameters are optimized through the back-propagation algorithm. The noise is continuously reduced through loop iterations until the noise value reaches the set value. At the same time, during the training process, the parameters of the adaptive filter are adjusted to ensure the best denoising effect.

[0027] Finally, the denoising algorithm is verified and evaluated by comparing it with real data to evaluate its performance and accuracy, and the results are visualized.

[0028] The processing method for data format conversion is: Obtain the source data and sample data in the target format that need to be converted, analyze the data structure of the source data and the target format in detail, including fields, data types, hierarchical structures, etc., and identify the particularities of different data formats, such as character encoding, date format, value range, etc.; Create a comparison table of source data and target format data fields to clarify the correspondence between each field. If the field name in the target format is inconsistent with the source data, it needs to be renamed. Set corresponding conversion rules according to the field types of the source data and target format data; Verify the source data to ensure its integrity and accuracy, and design a fault-tolerant mechanism to ensure the stability of the conversion process; Transform data through parallel processing technology; Compare the source data and the target format data to confirm that the converted data maintains the logical consistency of the original data, and then use automated tools to check the data quality to ensure that there are no omissions or errors.

[0029] Real-time streaming media processing: processing and analyzing real-time streaming media data, wherein the processing of real-time streaming media data at least includes real-time feature extraction, real-time pattern recognition and real-time anomaly detection.

[0030] This solution uses a feature extraction method based on deep learning, combined with technologies such as deep autoencoders, attention mechanisms, and generative adversarial networks to better handle complex features and pattern recognition requirements in data. Specifically: Feature extraction methods based on deep learning can better understand and represent the deep-level characteristics of data, thereby better capturing high-level features and complex patterns in the data.

[0031] By introducing the attention mechanism and Transformer model, we have improved the accuracy and robustness of pattern recognition. The attention mechanism can help the model better understand the correlation between data, thereby improving the ability to recognize complex patterns.

[0032] Anomaly detection methods based on generative adversarial networks can more accurately model complex data distributions and better capture nonlinear relationships, thereby achieving higher accuracy and generalization capabilities.

[0033] Distributed computing and parallel processing: Distributed storage and computing frameworks (Hadoop and Spark) are used to process large-scale data to improve processing speed and efficiency. Specifically: Distributed storage and computing frameworks: Compared with traditional single-machine processing, distributed storage and computing frameworks have better scalability and efficiency. These frameworks can store data on multiple nodes and process data in parallel on multiple computing nodes, thereby improving processing speed and efficiency.

[0034] Parallel processing algorithm: We designed a parallel processing algorithm based on a distributed computing framework that can break data into small blocks and process them in parallel on multiple computing nodes. This parallel processing method can make full use of cluster resources and improve processing speed and efficiency.

[0035] Horizontal expansion and task parallelism: Horizontal expansion and task parallelism are important features of distributed computing, which can improve data processing speed and efficiency. We designed dynamic task scheduling and load balancing strategies to achieve adaptive task allocation and efficient use of resources, thereby improving overall processing performance.

[0036] Real-time feedback and adjustment mechanism: Specific: A real-time monitoring system has been established to capture the system output results in real time and compare them with the expected results. By monitoring the system output results, we can feed feedback information back to the model and update the model parameters and algorithms in real time, thus achieving dynamic adjustment and optimization.

[0037] Using real-time data stream processing frameworks (such as Apache Flink and Spark Streaming) to build an end-to-end data processing pipeline, you can receive, process, and feedback data in real time. Compared with traditional batch processing methods, this achieves faster data processing speed and real-time response capabilities.

[0038] Establish a set of visual monitoring tools that can display the key performance indicators of the system in real time. Through the real-time dashboard, we can monitor the system operation status, provide real-time visualization of key performance indicators, and set up early warning mechanisms to promptly discover and solve potential problems.

[0039] Through the above methods, we have achieved a reliable way to establish a real-time feedback and adjustment mechanism. Through the real-time monitoring system, real-time data stream processing framework and monitoring tools, we can achieve real-time monitoring, dynamic adjustment and optimization of the system, improve the performance and stability of the system, and ensure that the system can effectively respond to the needs and challenges in various situations.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. An efficient streaming media data analysis method, characterized in that: include: Data collection and preprocessing: Obtain data from streaming media sources and perform preprocessing. Data preprocessing includes at least data cleaning, data denoising, and data format conversion to ensure data quality and data availability. The data denoising comprises the following steps: Step 1: Use the labeled data set to train the CNN model, optimize the model parameters through the back propagation algorithm, and continuously reduce the noise through loop iteration until the noise value reaches the set value; Step 2: Select an adaptive filter and apply the selected adaptive filter to each pixel or local area output by the CNN model to effectively remove residual noise and improve the quality and reliability of the image processing results; Real-time streaming media processing: processing and analyzing real-time streaming media data, wherein the processing of real-time streaming media data at least includes real-time feature extraction, real-time pattern recognition and real-time anomaly detection; Distributed computing and parallel processing: Distributed computing and parallel processing technologies are used to achieve efficient processing and analysis of large-scale streaming media data, thereby improving data processing speed and efficiency; Real-time feedback and adjustment: Establish a real-time feedback mechanism to adjust CNN model parameters and algorithms based on analysis results, achieve dynamic optimization and adjustment, and continuously improve the accuracy and efficiency of data analysis.

2. According to claim 1, an efficient streaming media data analysis method is characterized in that: The processing method of the data format conversion is: Obtain sample data in the source and target formats that need to be converted, analyze the data structures of the source and target formats in detail, and identify the particularities of different data formats; Create a comparison table of source data and target format data fields to clarify the correspondence between each field. If the field name in the target format is inconsistent with the source data, it needs to be renamed. Set corresponding conversion rules according to the field types of the source data and target format data; Verify the source data to ensure its integrity and accuracy, and design a fault-tolerant mechanism to ensure the stability of the conversion process; Transform data through parallel processing technology; Compare the source data and the target format data to confirm that the converted data maintains the logical consistency of the original data, and then use automated tools to check the data quality to ensure that there are no omissions or errors.

3. According to the efficient streaming media data analysis method of claim 1, it is characterized in that: The real-time feature extraction of real-time streaming media data is achieved using deep learning technology based on deep autoencoders and attention mechanisms.

4. The efficient streaming media data analysis method according to claim 1, characterized in that: The real-time pattern recognition of real-time streaming data is achieved using deep learning technology based on attention mechanism and Transformer model.

5. The efficient streaming media data analysis method according to claim 1, characterized in that: The anomaly detection method based on the generative adversarial network realizes the real-time anomaly detection of real-time streaming media data.

6. The efficient streaming media data analysis method according to claim 1, characterized in that: The method of distributed computing and parallel processing is: Establish a computing framework, store data in a distributed manner on multiple nodes, and process data in parallel on multiple computing nodes; Based on the parallel processing algorithm of the computing framework, the data is broken down into small blocks and processed in parallel on multiple computing nodes; Design dynamic task scheduling and load balancing strategies for adaptive data allocation.

7. The efficient streaming media data analysis method according to claim 1, characterized in that: The processing method of the real-time feedback and adjustment is: Establish a real-time monitoring system to capture system output results in real time, compare the system output results with the expected results, and obtain the deviation value; Build an end-to-end data processing pipeline through a real-time data stream processing framework to receive, process and feedback data in real time; The monitoring system transmits the output results and deviation value data back to the model through the data processing pipeline, and updates the model parameters and algorithms in real time; Establish visual monitoring tools to monitor system operation status, provide real-time visualization of key performance indicators, and set up early warning mechanisms to promptly detect and resolve potential problems.

Images