Efficient real-time integration system and method based on multi-source heterogeneous data

Abstract

The invention provides an efficient real-time integration system based on multi-source heterogeneous data, which comprises a multi-source data distribution unit, a data communication node, a data classification and marking unit, a data processing and statistical analysis module and a real-time data integration and processing engine, the method comprises the following steps: establishing a data communication node, performing pre-classification according to data sources and formats, establishing the data communication node, importing different source data into a data classification and marking unit according to pre-classification in the data communication node, comparing format vectors, parameter vectors and a basic parameter model by the data classification and marking unit, and performing correlation marking. And according to a standard data difference value between the associated parameters and the basic parameter model, the marking parameters are distributed to the data processing and statistical analysis module through the data communication node, the data processing and statistical analysis module predefines processing, statistics and layering templates, and the received marking parameters are correspondingly processed.

Description

Technical Field

[0001] The present invention belongs to the field of multi-source data integration, and in particular relates to an efficient real-time integration system and method based on multi-source heterogeneous data. Background Art

[0002] With the rapid development of information technology, the generation and application scenarios of data are becoming increasingly complex. Especially in the fields of big data, the Internet of Things, and artificial intelligence, the integration and analysis of multi-source heterogeneous data has become an important research direction. Multi-source heterogeneous data refers to data from different sources, formats, structures, and semantics. These data usually have different storage methods, update frequencies, and access rights. They mainly include structured data, semi-structured data, and unstructured data. Although the existing technology has achieved data integration and utilization to a certain extent when processing multi-source heterogeneous data, it still has many significant shortcomings.

[0003] Existing multi-source data integration technologies usually rely on predefined data models, which are often difficult to adapt to rapidly changing business needs and data formats. With the continuous increase of new data sources and the diversification of data types, traditional data integration systems are insufficient in flexibility and scalability. Due to the lack of dynamic adaptation capabilities, when facing new data sources, tedious manual adjustments and system reconstruction are required, which greatly affects integration efficiency and response speed.

[0004] Existing technologies also have obvious deficiencies in data cleaning and preprocessing. Multi-source heterogeneous data often have problems such as redundancy, duplication, and noise, and complex cleaning and conversion are required before data integration. However, existing data cleaning tools are often unable to effectively handle the contradictions and inconsistencies between different data sources, resulting in low quality of cleaned data and affecting the accuracy of subsequent analysis. Especially in the scenario of real-time data integration, the timeliness and effectiveness of data cleaning are directly related to the timeliness and reliability of analysis results.

[0005] Existing multi-source data integration systems also face challenges in terms of real-time performance. Many traditional technologies use batch processing mode for data integration, which results in significant delays in data updates and processing, and cannot meet the needs of real-time analysis. In many application scenarios, such as financial monitoring, smart transportation, and healthcare, real-time performance is critical, and delays may lead to major decision-making errors or safety hazards. However, existing systems often lack efficient data stream processing capabilities and cannot perform real-time integration and analysis at the moment data is generated.

[0006] Existing technologies also have deficiencies in data security and privacy protection. With increasingly stringent data privacy protection regulations, how to ensure the security and privacy of user data during integration and analysis has become an urgent problem to be solved. Existing integration systems often do not pay enough attention to data access control and permission management, which can easily lead to the leakage and abuse of sensitive data. In the process of integrating multi-source heterogeneous data, how to achieve secure data sharing and protection is still a technical problem.

[0007] Existing multi-source data integration technologies also have bottlenecks in understanding data semantics. Since the semantic definitions of data in different data sources may differ, how to effectively align and map semantics is a major challenge in data integration. Existing technologies usually lack flexible semantic processing capabilities and cannot automatically identify and eliminate semantic differences between data sources, which greatly reduces the value of information after data integration. Summary of the invention

[0008] The present invention proposes an efficient real-time integration system and method based on multi-source heterogeneous data, which aims to solve technical problems such as data classification, labeling, processing and real-time analysis in the process of multi-source heterogeneous data integration. By establishing a multi-source data distribution unit and a data communication node, automatic classification and labeling of different data sources are realized, thereby eliminating the problems of inconsistent data formats and information redundancy. At the same time, the system ensures efficient processing and analysis of the received data through predefined processing, statistics and hierarchical templates, eliminates data processing delays caused by manual operations in traditional systems, and provides real-time data integration and analysis capabilities.

[0009] The technical solution of the present invention is implemented as follows: an efficient real-time integration system based on multi-source heterogeneous data includes a multi-source data allocation unit, a data communication node, a data classification and marking unit, a data processing and statistical analysis module and a real-time data integration and processing engine, wherein the multi-source data allocation unit receives data from different data sources, and pre-classifies according to the data source and format, establishes a data communication node, and imports the data from different sources into the data classification and marking unit according to the pre-classification in the data communication node, wherein the data classification and marking unit associates and marks after comparing the format vector, the parameter vector and the basic parameter model, and assigns the marking parameter to the data processing and statistical analysis module through the data communication node according to the standard data difference between the associated parameter and the basic parameter model, wherein the data processing and statistical analysis module pre-defines processing, statistics and stratification templates, performs corresponding processing on the received marking parameters, and generates table structure data after synchronous statistics and data stratification, and transmits the table structure data to the real-time data integration and processing engine, wherein the real-time data integration and processing engine simultaneously collects the table structure data generated by different data sources, and constructs an integrated data model using the table structure and data attributes, and performs real-time data analysis and data report generation through the integrated data model.

[0010] Compared with traditional data integration systems, the present application achieves efficient processing and integration of multiple heterogeneous data through the comprehensive layout of multi-source data distribution units, data communication nodes, data classification and labeling units, data processing and statistical analysis modules, and real-time data integration and processing engines.

[0011] Traditional data integration systems usually face the problem of inconsistent data formats and diverse sources from multiple data sources, and often can only rely on a simple ETL (extraction, transformation, loading) process to process data. The multi-source data distribution unit design of the present application receives data from different data sources and pre-classifies it according to the data source and format, establishes data communication nodes, and realizes the preliminary screening and organization of data. This design significantly improves the flexibility and efficiency of data processing, and can classify data as soon as it enters the system, thereby reducing the complexity of subsequent processing.

[0012] The introduction of data classification and labeling units is a major innovation of this application. By comparing the format vector, parameter vector and basic parameter model, the system can associate and label the received data to ensure the accuracy and consistency of the data in subsequent processing. In the prior art, data labeling and classification often rely on manual intervention or simple rules, resulting in inefficient and error-prone processing. However, this application not only improves the speed of data processing, but also enhances the accuracy of data integration through an automated labeling mechanism.

[0013] In the data processing and statistical analysis module, the predefined processing, statistics and stratification templates can be flexibly adjusted according to the received tag parameters. This templated design enables the system to quickly adapt to the needs of different data sources and application scenarios. Compared with the fixed processing flow in the prior art, the design of this application provides higher flexibility and adaptability. After statistics and data stratification are performed simultaneously, table structure data is generated, which makes the foundation of subsequent data analysis more solid and meets the requirements of modern data analysis for real-time and accuracy.

[0014] As a preferred implementation, the multi-source data distribution unit uses a unified data access and management interface to connect different data sources and pre-classify the data formats of different data sources after pre-processing and marking.

[0015] As a preferred embodiment, several channels are set in the data communication node, and the data information uploaded by the multi-source data distribution unit is matched through several signals. When the data information exceeds the communication upper limit of a single channel, the remaining standby channels are called for auxiliary data transmission. The data communication node performs encrypted transmission through a separately set channel when transmitting with the data classification and marking unit.

[0016] As a preferred embodiment, the basic parameter model in the data classification and labeling unit pre-sets the characteristic parameter data in different data sources, and after receiving the data source of the corresponding format through the data communication node, compares the data source with the characteristic parameter data, and performs association labeling after the characteristic parameters match qualifiedly.

[0017] As a preferred implementation, in the data processing and statistical analysis module, when performing predefined processing templates, data cleaning, format conversion and missing value filling operations are set according to actual application scenarios and data characteristics; statistical indicators and statistical algorithms are preset when predefining statistical templates, and the statistical module is guided by mean, variance and frequency distribution to apply corresponding statistical methods and statistical indicators for analysis after receiving the data; the stratified template is stratified according to user attributes, time dimension or set indicators, and the data is grouped and summarized after stratification is completed.

[0018] An efficient real-time integration method based on multi-source heterogeneous data comprises the following steps:

[0019] S1: The multi-source data distribution unit receives data from different data sources, pre-classifies the data according to the data source and format, and establishes data communication nodes;

[0020] S2: In the data communication node, data from different sources are imported into the data classification and marking unit according to the pre-classification, and the data classification and marking unit associates and marks the data after comparing the format vector, the parameter vector and the basic parameter model, and distributes the marking parameters to the data processing and statistical analysis module through the data communication node according to the standard data difference between the associated parameters and the basic parameter model;

[0021] S3: The data processing and statistical analysis module predefines processing, statistics and stratification templates, processes the received tag parameters accordingly, and generates table structure data after performing statistics and data stratification simultaneously;

[0022] S4: The table structure data is transferred to the real-time data integration and processing engine. The real-time data integration and processing engine collects the table structure data generated by different data sources at the same time, and uses the table structure and data attributes to build an integrated data model. The integrated data model is used to perform real-time data analysis and generate data reports.

[0023] After adopting the above technical solution, the beneficial effects of the present invention are: the efficient real-time integration system based on multi-source heterogeneous data can realize automatic classification and labeling of data from different sources and formats, thereby improving the efficiency and accuracy of data processing. By establishing standardized data communication nodes, the system can quickly identify and integrate information from different data sources, reducing the need for manual intervention, thereby improving the real-time performance of data processing. In addition, the system's built-in processing, statistics, and hierarchical templates make the data analysis process more systematic and efficient, and can generate structured data reports according to needs, helping users quickly obtain valuable insights. Ultimately, the system makes the real-time integration and analysis of multi-source data more efficient and reliable, provides strong data support for business decisions, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] Example:

[0028] An efficient real-time integration system based on multi-source heterogeneous data includes a multi-source data allocation unit, a data communication node, a data classification and marking unit, a data processing and statistical analysis module and a real-time data integration and processing engine. The multi-source data allocation unit receives data from different data sources, and pre-classifies the data according to the data source and format, establishes a data communication node, and imports the data from different sources into the data classification and marking unit in the data communication node according to the pre-classification. The data classification and marking unit associates and marks the data after comparing the format vector and the parameter vector with the basic parameter model, and distributes the marking parameters to the data processing and statistical analysis module through the data communication node according to the standard data difference between the associated parameters and the basic parameter model. The data processing and statistical analysis module pre-defines processing, statistics and stratification templates, performs corresponding processing on the received marking parameters, and generates table structure data after synchronously performing statistics and data stratification, and transmits the table structure data to the real-time data integration and processing engine. The real-time data integration and processing engine simultaneously collects the table structure data generated by different data sources, and constructs an integrated data model using the table structure and data attributes, and performs real-time data analysis and data report generation through the integrated data model.

[0029] The working principle of the efficient real-time integration system based on multi-source heterogeneous data involves the coordinated operation of multiple modules to achieve efficient reception, processing and analysis of data from different data sources. The system mainly consists of a multi-source data distribution unit, a data communication node, a data classification and labeling unit, a data processing and statistical analysis module, and a real-time data integration and processing engine. First, as the entrance to the system, the multi-source data distribution unit is responsible for receiving data from different data sources, including sensor data, user input, database records, etc. The unit will pre-classify the data according to its source and format for subsequent processing. This pre-classification is to ensure that the data can be accurately identified and processed when entering the subsequent processing process, reducing data confusion and errors.

[0030] After establishing the data communication node, the system will import data from different sources into the data classification and labeling unit according to the pre-classification results. At this stage, the data classification and labeling unit compares the format vector and parameter vector with the basic parameter model to complete the data association labeling. This process is a key step in achieving data standardization and consistency because it lays the foundation for subsequent analysis and processing. In addition, the data difference calculation in the labeling process is also very important. This calculation helps identify the deviation between the data and the standard, thereby guiding the subsequent data processing.

[0031] The tag parameters are distributed to the data processing and statistical analysis module through the data communication node. This module predefines a variety of processing, statistical and stratification templates to process the received tag parameters accordingly. This processing includes not only data cleaning and conversion, but also statistical analysis and data stratification to generate table structure data. The generation of table structure data is to facilitate subsequent real-time data integration and processing, because this structured data form is easier to query and analyze efficiently.

[0032] In the real-time data integration and processing engine, the system collects table structure data generated from different data sources at the same time and uses the attributes of this data to build an integrated data model. This integrated data model is the core of the system, through which the system can perform real-time data analysis and generate data reports. The construction of the integrated data model takes into account the diversity and complexity of data, ensuring that the model can adapt to different types of data and provide accurate analysis results.

[0033] In terms of the installation process, the system hardware and software must be prepared first, including the server setup and data source access. Next, the multi-source data distribution unit is installed and the connections to each data source are configured. Subsequently, data communication nodes are set up to ensure that data can flow effectively to each module. When installing the data classification and labeling unit, the basic parameter model needs to be preset to facilitate the subsequent data labeling and standardization process.

[0034] When installing the data processing and statistical analysis modules, you need to define processing, statistics, and layering templates according to actual needs to ensure that they can adapt to the processing needs of different data. Finally, build a real-time data integration and processing engine and build an integrated data model. During the entire installation process, multiple tests are required to ensure that each module can collaborate efficiently and that data can flow and process accurately.

[0035] Through the above working principles and installation processes, the efficient real-time integration system based on multi-source heterogeneous data can achieve effective management of complex data environments, provide efficient data analysis and real-time decision support, and meet modern data-driven business needs.

[0036] The multi-source data distribution unit adopts a unified data access and management interface to connect different data sources through the unified data access and management interface and pre-classify the data formats of different data sources after pre-processing and marking.

[0037] The core of this design idea is to connect multiple heterogeneous data sources through a unified interface, thereby simplifying the complexity of data access and improving the flexibility and convenience of data processing. Traditional data access methods often require the development of adapters or interfaces for each data source, which not only increases the development and maintenance costs of the system, but may also lead to inconsistent data standards and affect data integration and analysis efficiency. Through a unified data access and management interface, the system can flexibly process data from multiple sources on a single platform. Whether it comes from sensors, databases, API interfaces or other data sources, unified access can be achieved through this interface.

[0038] Another important aspect of this design idea is the preprocessing and labeling of data formats. After connecting to different data sources, the system will first preprocess their data formats. This process includes data cleaning, format conversion, and standardization to ensure that all connected data can be stored and processed in a unified format. At this stage, the system will label the data and give each data item specific identification information, so that subsequent classification and analysis can be more efficient and accurate. By combining data format preprocessing with labeling, the system can eliminate potential data inconsistencies and errors and improve data quality before the data enters the subsequent processing process.

[0039] Compared with existing technologies, this unified data access and management interface design has obvious advantages. First, existing technologies often lack flexibility. When faced with multiple heterogeneous data sources, the system needs to be configured and adapted separately for each data source, resulting in poor scalability of the system. Through the design of a unified interface, users can easily add new data sources by simply configuring the interface layer without making large-scale adjustments to the entire system, thereby improving the scalability and adaptability of the system.

[0040] Traditional data processing systems usually lack effective preprocessing mechanisms when data is accessed, and are easily affected by data quality issues, resulting in inaccurate subsequent analysis results. However, through the preprocessing and labeling process in the design, the system can perform quality control on the data as soon as the data is accessed, ensuring that subsequent data processing and analysis are based on high-quality data.

[0041] Unified data access and management interfaces can also improve the efficiency of team collaboration. In a multi-source data integration environment, data scientists, engineers, and business analysts often need to coordinate different data sources and interfaces. Through a unified interface, team members can more easily understand and use data, promoting cross-departmental collaboration and communication.

[0042] The data communication node is provided with several channels, and the data information uploaded by the multi-source data distribution unit is matched through several signals. When the data information exceeds the communication upper limit of a single channel, the remaining standby channels are called for auxiliary data transmission. The data communication node performs encrypted transmission through a separately set channel in the transmission with the data classification and marking unit.

[0043] Existing technologies usually rely on fixed channel configurations. When the data flow exceeds the set upper limit, data loss or transmission delays often occur. However, this design sets up multiple channels and uses a signal matching mechanism to dynamically allocate multi-source data. When the data flow surges, it can automatically call the standby channel for auxiliary data transmission, thereby improving the efficiency and reliability of data transmission. Secondly, in terms of data security, the existing technology may fail to take special encryption measures during the transmission of data classification and marking, which may easily lead to data leakage risks. However, this design sets up independent channels for data classification and marking units for encrypted transmission, ensuring the security of information during transmission and reducing the risk of malicious attacks or theft. In addition, the dynamic channel scheduling capability of this solution improves the flexibility of the system, enabling it to adapt to different data transmission scenarios and requirements, thereby achieving a significant improvement in overall performance. These differences together constitute the innovation of this design in the field of data communication technology and solve the efficiency and security problems faced by existing technologies.

[0044] The basic parameter model in the data classification and marking unit pre-sets the characteristic parameter data in different data sources, and after receiving the data source in the corresponding format through the data communication node, compares the data source with the characteristic parameter data, and performs association marking after the characteristic parameters match.

[0045] The construction of the basic parameter model is based on the in-depth analysis and understanding of different data sources. The characteristic parameter data preset in the model represents the key features that the system needs to identify. These characteristic parameters can cover the attributes of multiple data sources, such as data format, content type, data source, etc.

[0046] After receiving the formatted data source from the data communication node, the system determines the validity and applicability of the data source by comparing the received data source with the preset characteristic parameter data. This comparison process is automated and can quickly identify qualified data sources, thus avoiding errors and inefficiencies caused by manual intervention. In this way, the system can ensure that only matched data sources are further processed, improving the reliability of data processing.

[0047] After the feature parameters are matched, the system will perform the associated tagging operation. This step is to add corresponding tags to the qualified data sources for subsequent data management and analysis. Through tagging, the system can better organize and classify data, making subsequent query and analysis more efficient. The tagging process not only provides contextual information for the data, but also enhances the traceability of the data, thus providing a rich foundation for subsequent data mining and analysis.

[0048] This design approach has many advantages. The preset basic parameter model can be adjusted and optimized according to the specific application scenario, making the system highly adaptable and flexible. Different application scenarios may involve different data characteristics. By adjusting the parameters in the model, the system can quickly adapt to new data source types to ensure the effectiveness and accuracy of data processing. The automated matching and labeling process significantly improves the speed of data processing, reduces the need for manual intervention, and thus reduces the risk of human error. This efficient processing method enables the system to maintain good response speed and processing capabilities when faced with large amounts of data.

[0049] By classifying and labeling data sources, the system can quickly locate relevant data sets in subsequent data analysis. This structured data management method provides a good foundation for data analysis, making data mining and decision support more efficient. Labeled data can not only be quickly retrieved, but also reused in different analysis tasks, further improving data utilization.

[0050] When the data processing and statistical analysis module performs the predefined processing template, data cleaning, format conversion and missing value filling operations are set according to the actual application scenario and data characteristics; statistical indicators and statistical algorithms are preset when predefining the statistical template, and the statistical module is guided by the mean, variance and frequency distribution to apply corresponding statistical methods and statistical indicators for analysis after receiving the data; the stratified template is stratified according to user attributes, time dimension or set indicators, and the data is grouped and summarized after the stratification is completed.

[0051] This module is divided into two parts: predefined processing templates and predefined statistical templates. It aims to provide systematic data cleaning, format conversion, missing value filling and statistical analysis capabilities based on actual application scenarios and data characteristics. In terms of data processing, the design of predefined processing templates takes into account the data characteristics in different application scenarios. For example, data in different fields may have different formats, structures and quality issues. Therefore, setting up data cleaning, format conversion and missing value filling operations can ensure that the quality and consistency of the data are guaranteed before it enters the analysis stage. Data cleaning operations are used to remove noise data and outliers, format conversion ensures that data can be processed with a unified standard, and missing value filling corrects the data through a reasonable algorithm to avoid deviations in analysis results due to missing values. This multi-level data preprocessing lays a solid foundation for subsequent analysis and helps to improve the accuracy and reliability of analysis results.

[0052] In terms of statistical analysis, predefined statistical templates enable in-depth analysis of data by setting statistical indicators and statistical algorithms. Through preset statistical indicators such as mean, variance and frequency distribution, the system can quickly apply corresponding statistical methods for analysis after receiving the data. This process not only improves the efficiency of analysis, but also ensures the consistency and comparability of analysis results. Predefined statistical indicators can be set according to industry standards or best practices to ensure that the analysis results can effectively reflect the actual situation and provide strong support for decision-making.

[0053] The layered templates in the design take into account the diverse needs of users. By layering according to user attributes, time dimensions or set indicators, the system can organize and process data at different levels. This design idea makes data analysis not limited to grasping the overall trend, but also to go deep into specific market segments, user groups or time periods, so as to obtain more targeted and practical analysis results. After completing the layering, the system groups and summarizes the data so that data at different levels can be effectively integrated, which is convenient for subsequent comparison and analysis.

[0054] The advantage of this design approach is its flexibility and scalability. Faced with different data types and application requirements, users can customize processing and statistical templates according to specific circumstances to ensure that the system can adapt to various data analysis scenarios. At the same time, predefined templates reduce the user's learning cost during data processing and analysis, allowing non-professional users to quickly get started and conduct effective data analysis.

[0055] like Figure 1 As shown, an efficient real-time integration method based on multi-source heterogeneous data includes the following steps:

[0056] S1: The multi-source data distribution unit receives data from different data sources, pre-classifies the data according to the data source and format, and establishes data communication nodes;

[0057] S2: In the data communication node, data from different sources are imported into the data classification and marking unit according to the pre-classification, and the data classification and marking unit associates and marks the data after comparing the format vector, the parameter vector and the basic parameter model, and distributes the marking parameters to the data processing and statistical analysis module through the data communication node according to the standard data difference between the associated parameters and the basic parameter model;

[0058] S3: The data processing and statistical analysis module predefines processing, statistics and stratification templates, processes the received tag parameters accordingly, and generates table structure data after performing statistics and data stratification simultaneously;

[0059] S4: The table structure data is transferred to the real-time data integration and processing engine. The real-time data integration and processing engine collects the table structure data generated by different data sources at the same time, and uses the table structure and data attributes to build an integrated data model. The integrated data model is used to perform real-time data analysis and generate data reports.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An efficient real-time integration system based on multi-source heterogeneous data, characterized by: The invention comprises a multi-source data distribution unit, a data communication node, a data classification and marking unit, a data processing and statistical analysis module and a real-time data integration and processing engine. The multi-source data distribution unit receives data from different data sources, and pre-classifies the data according to the data source and format, establishes a data communication node, and imports the data from different sources into the data classification and marking unit in the data communication node according to the pre-classification. The data classification and marking unit associates and marks the data after comparing the format vector and the parameter vector with the basic parameter model, and distributes the marking parameters to the data processing and statistical analysis module through the data communication node according to the standard data difference between the associated parameters and the basic parameter model. The data processing and statistical analysis module pre-defines processing, statistics and stratification templates, performs corresponding processing on the received marking parameters, and generates table structure data after synchronously performing statistics and data stratification, and transmits the table structure data to the real-time data integration and processing engine. The real-time data integration and processing engine simultaneously collects the table structure data generated by different data sources, and constructs an integrated data model by using the table structure and data attributes, and performs real-time data analysis and data report generation through the integrated data model.

2. The efficient real-time integration system based on multi-source heterogeneous data according to claim 1, characterized in that: The multi-source data distribution unit adopts a unified data access and management interface to connect different data sources through the unified data access and management interface and pre-classify the data formats of different data sources after pre-processing and marking.

3. The efficient real-time integration system based on multi-source heterogeneous data according to claim 1, characterized in that: The data communication node is provided with several channels, and the data information uploaded by the multi-source data distribution unit is matched through several signals. When the data information exceeds the communication upper limit of a single channel, the remaining standby channels are called for auxiliary data transmission. The data communication node performs encrypted transmission through a separately set channel in the transmission with the data classification and marking unit.

4. The efficient real-time integration system based on multi-source heterogeneous data according to claim 1, characterized in that: The basic parameter model in the data classification and marking unit pre-sets the characteristic parameter data in different data sources, and after receiving the data source in the corresponding format through the data communication node, compares the data source with the characteristic parameter data, and performs association marking after the characteristic parameters match.

5. The efficient real-time integration system based on multi-source heterogeneous data according to claim 1, characterized in that: When the data processing and statistical analysis module performs the predefined processing template, data cleaning, format conversion and missing value filling operations are set according to the actual application scenario and data characteristics; statistical indicators and statistical algorithms are preset when predefining the statistical template, and the statistical module is guided by the mean, variance and frequency distribution to apply corresponding statistical methods and statistical indicators for analysis after receiving the data; the stratified template is stratified according to user attributes, time dimension or set indicators, and the data is grouped and summarized after the stratification is completed.

6. An efficient real-time integration method based on multi-source heterogeneous data, characterized in that: The method comprises the following steps: S1: The multi-source data distribution unit receives data from different data sources, pre-classifies the data according to the data source and format, and establishes data communication nodes; S2: In the data communication node, data from different sources are imported into the data classification and marking unit according to the pre-classification, and the data classification and marking unit associates and marks the data after comparing the format vector, the parameter vector and the basic parameter model, and distributes the marking parameters to the data processing and statistical analysis module through the data communication node according to the standard data difference between the associated parameters and the basic parameter model; S3: The data processing and statistical analysis module predefines processing, statistics and stratification templates, processes the received tag parameters accordingly, and generates table structure data after performing statistics and data stratification simultaneously; S4: The table structure data is transferred to the real-time data integration and processing engine. The real-time data integration and processing engine collects the table structure data generated by different data sources at the same time, and uses the table structure and data attributes to build an integrated data model. The integrated data model is used to perform real-time data analysis and generate data reports.

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