Multi-platform collaborative data acquisition and analysis method

Abstract

The invention provides a multi-platform collaborative data acquisition and analysis method, and relates to the technical field of data processing and analysis. The method comprises the following steps: receiving and converting data from different platforms into a unified intermediate format through a data adaptation intermediate layer, and processing a data structure and an interface of a specific platform by using a plurality of adapters; the data abstraction layer defines a standardized interface, so that multiple platforms request data through a unified interface, and a mapping rule converts the request into calling of the adaptation layer; the data integration engine preprocesses the data to ensure data consistency and accuracy; the data warehouse adopts a distributed architecture to ensure the service continuity through load balancing, data partitioning, a caching mechanism and a redundant storage and fault transfer mechanism; and the data processing and analysis module converts the data into decision suggestions by using a visual tool and a data mining algorithm. According to the method, manual intervention is reduced, the data processing speed and accuracy are improved, the data mode and trend are deeply mined, and service decision is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and in particular to a multi-platform collaborative data collection and analysis method. Background Art

[0002] In the era of big data, enterprises are faced with the challenge of integrating information from multiple data sources, including social media, e-commerce platforms, and customer relationship management systems, which usually have different data structures and interface specifications.

[0003] Existing technologies often encounter compatibility issues when processing these heterogeneous data because they lack a unified data format and interface, resulting in a cumbersome and inefficient data integration process. In addition, traditional data preprocessing methods often rely on manual operations, which are not only time-consuming but also prone to errors, especially when dealing with large-scale data sets. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides a multi-platform collaborative data collection and analysis method, which solves the problems of integration difficulties, low processing efficiency and insufficient analysis depth caused by the heterogeneity of multi-platform data.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-platform collaborative data collection and analysis method, comprising:

[0008] S1. Receive data from different platforms through the data adaptation middleware and convert it into a unified intermediate format; wherein the data adaptation middleware includes multiple adapters, each of which is specifically used to process the data structure and interface of a specific platform;

[0009] S2. Through the data abstraction layer, a unified interface access is provided for multiple platforms. According to the platform's request, the corresponding adapter in the adaptation middle layer is called to obtain data from different platforms; through the data mapping relationship, the requests of multiple platforms are converted into calls to the adaptation layer;

[0010] S3. Build a data integration engine to coordinate the operations of the data adaptation middle layer and clean, convert and make consistency processing for data on different platforms;

[0011] S4. Import the processed data into a data warehouse, which has high concurrent data access capabilities and supports the storage of large amounts of data to ensure data security and reliability;

[0012] S5. Based on the data warehouse, build data processing and analysis modules to provide data mining and analysis.

[0013] Preferably, the data adaptation middle layer receives and converts data from different platforms by using multiple adapters, identifies and parses data from the platform; when data flows in from various platforms, the adapter first determines the source of the data, and then converts the data into a unified intermediate format according to a set of predefined rules and algorithms; this intermediate format is usually a standardized data model, which defines the structure, type and relationship of the data to ensure the consistency and readability of the data; the preset rules and algorithms include data cleaning, format conversion, and encoding standardization, which convert the specific data formats of different platforms into formats that the middle layer can understand and process; the adapter brings together data scattered on different platforms to form a unified data pool to facilitate subsequent data analysis and processing.

[0014] Preferably, the data abstraction layer provides a unified data access method for multiple platforms by defining standardized interfaces; the interfaces follow the RESTful principle and use HTTP methods and resource identifiers to operate data resources; for example, the GET method is used to retrieve data, POST to create new data, PUT to update existing data, and DELETE to delete data; the interface design clearly defines the data format of requests and responses, usually using JSON or XML to ensure the consistency and predictability of data exchange; when defining the interface, factors such as the security of data operations, authentication mechanisms, error handling, and version control are considered to ensure the robustness and maintainability of the interface; when applications in the platform send data requests through these standardized interfaces, the data abstraction layer parses the data requests and converts them into specific calls to the corresponding adapters in the adaptation middle layer according to predefined data mapping rules.

[0015] Preferably, the data mapping relationship is implemented through predefined mapping rules. When data needs to be obtained from a specific platform, it will send a request to the data abstraction layer, and the request includes the required data type and query conditions; after receiving the request, the data abstraction layer searches for an adapter that matches the request according to the mapping rules; the mapping rules are stored in a database, defining the correspondence between the platform identifier, data type, request parameters and the adapter; once a matching adapter is found, the data abstraction layer converts the parameters and conditions in the original request into a format that the adapter can recognize and process; the converted request is sent to the adapter, and the adapter performs data acquisition, parsing and conversion operations based on this information; after completing the operation, the adapter returns the processed data to the data abstraction layer, and the data abstraction layer provides the data to the platform in a unified format.

[0016] Preferably, the data integration engine is responsible for receiving data from the adaptation middle layer. The data comes from multiple platforms and has different formats, encodings and data structures. To ensure the consistency and accuracy of the data, the data integration engine first pre-processes the data. The pre-processing includes data cleaning, removing duplicate data, correcting erroneous data, and filling in incomplete data records. For example, a deduplication algorithm is used to identify and delete duplicate records, or data verification rules are applied to correct format errors or logical errors. The data integration engine performs data conversion to convert data from different sources into a unified format and standard. For example, if different platforms use different date formats, the data integration engine will convert them into the ISO standard date format.

[0017] Preferably, the data warehouse adopts a distributed architecture to improve data processing capabilities and scalability by dispersing and storing data on multiple servers or nodes; this architecture allows the data warehouse to significantly improve performance through parallel processing technology when processing large-scale data sets; the data warehouse dynamically distributes data access requests to different nodes through load balancing, and allows the data warehouse to divide the data into multiple parts, each of which is independently stored on a different node. When processing query requests, they are executed in parallel on multiple nodes, thereby speeding up the query and improving overall processing efficiency; the data warehouse uses a caching mechanism to pre-load frequently accessed data into fast-access storage media, such as memory or solid-state drives; when the user requests the data again, it is directly obtained from the cache without reading from the disk, reducing data access time; the data warehouse stores multiple copies on multiple nodes through redundant storage and failover. When a node fails, it automatically switches to another node to continue providing services, thereby minimizing the risk of service interruption.

[0018] Preferably, the data processing and analysis module performs exploratory analysis on the preprocessed data through visualization tools, so that users can intuitively understand the distribution, characteristics and correlation of the data; the visualization tool provides a variety of chart types, such as bar charts, line charts, scatter plots and heat maps, etc. Users can choose the most appropriate chart type according to their needs to present the data in a graphical manner; in the in-depth analysis stage, the data processing and analysis module will apply different data mining algorithms according to the platform's goals and data characteristics; for example, using classification algorithms to predict user behavior patterns will help personalized recommendations and target marketing; using clustering algorithms to group users in order to better understand the characteristics and needs of user groups; using association rule mining algorithms to discover the correlation between user behaviors, which is very helpful for the formulation of cross-selling and product bundling strategies; the data processing and analysis module interprets and applies the results of data mining and analysis, and transforms complex data into easy-to-understand decision recommendations.

[0019] (III) Beneficial effects

[0020] The present invention provides a multi-platform collaborative data collection and analysis method, which has the following beneficial effects:

[0021] 1. The present invention can integrate data from different platforms, regardless of the original format and structure of the data; it significantly improves the efficiency of data integration, reduces the time for manual conversion and error handling, and thus speeds up the process of data analysis and decision-making.

[0022] 2. The present invention reduces the reliance on human intervention through the data integration engine; automated processing not only improves the speed and accuracy of data processing, but also reduces the risk of data errors and inconsistencies caused by improper human operations.

[0023] 3. The present invention can deeply mine patterns and trends in data, provide more comprehensive and in-depth data analysis results, gain richer understanding from data, and thus make more informed business decisions, driving business innovation and growth. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] In the modern business environment, enterprises need to obtain information from multiple data sources to support decision making; these data sources include social media, e-commerce platforms, customer relationship management systems, etc., which usually have different data structures and interface specifications; in order to achieve effective data analysis and decision support, the heterogeneity and inconsistency of data must be solved first; the present invention provides an innovative solution by constructing a platform that can process multi-source heterogeneous data.

[0026] The platform receives data through the data adaptation middle layer, and the data adaptation middle layer receives and converts data from different platforms through multiple adapters; each adapter is customized for a specific platform so that it can understand and parse the data structure and interface of the corresponding platform; when data flows in from each platform, the adapter first identifies the source of the data, and then converts the data into a unified intermediate format based on preset rules and algorithms, so that the data on multiple platforms remains consistent and readable; the adapter collects data scattered on different platforms together, which facilitates subsequent processing.

[0027] The data abstraction layer provides unified interface access for multiple platforms. By defining a series of standardized interfaces, multiple platforms can request the required data through the interfaces without directly interacting with the underlying data sources. When multiple platforms issue data requests, the data abstraction layer converts the requests into calls to the corresponding adapters in the adaptation middle layer based on the preset data mapping relationships. The mapping rules are stored in a database, defining the correspondence between platform identifiers, data types, request parameters and adapters. Once a matching adapter is found, the data abstraction layer converts the parameters and conditions in the original request into a format that the adapter can recognize and process. The converted request is sent to the adapter, which performs data acquisition, parsing and conversion operations based on this information.

[0028] The data integration engine receives data from the adaptation middle layer, including the format, encoding and data structure between different platforms; it pre-processes the data, including data cleaning, to remove duplicate, erroneous or incomplete data; data conversion, converting the data into a unified format and standard; the data cleaning stage first cleans the data by identifying and processing duplicate items in the data, identifying duplicate data by comparing the identifiers of the records, and merging or deleting them; correcting errors in the data, for example, using predefined rules or algorithms to correct data with format errors or logical errors; for missing data, depending on the nature and importance of the data, deleting records or using statistical methods, including interpolation and completion of the mean, median, and mode.

[0029] The data warehouse adopts a distributed architecture, dispersing data across multiple servers or nodes to disperse the load and improve processing capabilities; through load balancing technology, data access requests are dynamically allocated to avoid overloading of a single node; data partitioning strategies allow data warehouses to logically divide data to achieve parallel processing of multiple requests; data warehouses use caching mechanisms to store frequently accessed data in fast-access storage media; data warehouses implement redundant storage and failover mechanisms to maintain service continuity when a platform failure occurs.

[0030] The data processing and analysis module explores the pre-processed data through visualization tools, allowing users to intuitively understand the distribution, characteristics and correlation of the data; by selecting the chart type, the data is presented in a graphical way to discover the patterns and trends in the data; according to the platform's goals and data characteristics, classification algorithms are used to predict user behavior, clustering algorithms are used to group users, and association rules are used to mine and discover the correlation between user behaviors; the results of data mining and analysis are interpreted and applied, and converted into actionable decision-making recommendations.

[0031] During implementation, the design and development of this method needs to take into account data security and privacy protection; all data transmission and storage need to be encrypted to ensure that the data is not intercepted or tampered with during transmission; at the same time, this method needs to comply with relevant data protection regulations to ensure the legal use of user data.

[0032] In addition, the implementation of this method also needs to consider the scalability and maintainability of the platform. As enterprise data sources increase and change, the method needs to be able to easily add new adapters and data mapping rules to adapt to new data sources; the method needs to provide logging and monitoring functions to promptly discover and resolve problems in platform operation.

[0033] 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 defined by the appended claims and their equivalents.

Claims

1. A multi-platform collaborative data collection and analysis method, characterized in that; The following steps are involved: S1. Receive data from different platforms through the data adaptation middleware and convert it into a unified intermediate format; wherein the data adaptation middleware includes multiple adapters, each of which is specifically used to process the data structure and interface of a specific platform; S2. Through the data abstraction layer, a unified interface access is provided for multiple platforms. According to the platform's request, the corresponding adapter in the adaptation middle layer is called to obtain data from different platforms; through the data mapping relationship, the requests of multiple platforms are converted into calls to the adaptation layer; S3. Build a data integration engine to coordinate the operations of the data adaptation middle layer and clean, convert and make consistency processing for data on different platforms; S4. Import the processed data into a data warehouse, which has high concurrent data access capabilities and supports storage of large amounts of data to ensure data security and reliability; S5. Based on the data warehouse, build data processing and analysis modules to provide data mining and analysis.

2. The multi-platform collaborative data collection and analysis method according to claim 1, characterized in that: The data adaptation middle layer receives and converts data from different platforms by using multiple adapters. Each adapter is customized for a specific platform so that it can understand and parse the data structure and interface of the corresponding platform. When data flows in from various platforms, the adapter first identifies the source of the data, and then converts the data into a unified intermediate format according to preset rules and algorithms, so that the data on multiple platforms remains consistent and readable. Adapters collect data scattered across different platforms.

3. The multi-platform collaborative data collection and analysis method according to claim 1, characterized in that: The role of the data abstraction layer is to provide a unified interface for multiple platforms. By defining a series of standardized interfaces, multiple platforms can request the required data through the interface without directly interacting with the underlying data source. When multiple platforms issue data requests, the data abstraction layer converts the requests into calls to the corresponding adapters in the adaptation middle layer based on the preset data mapping relationship.

4. The multi-platform collaborative data collection and analysis method according to claim 3, characterized in that: The data mapping relationship is implemented through pre-defined mapping rules. When data needs to be obtained from a specific platform, it will send a request to the data abstraction layer, which contains the required data type and query conditions. After receiving the request, the data abstraction layer searches for an adapter that matches the request according to the mapping rules. The mapping rules are stored in a database and define the corresponding relationship between platform identifiers, data types, request parameters and adapters. Once a matching adapter is found, the data abstraction layer converts the parameters and conditions in the original request into a format that the adapter can recognize and process; The converted request is sent to the adapter, which performs data acquisition, parsing and conversion operations based on this information; After completing the operation, the adapter returns the processed data to the data abstraction layer, which then provides the data to the platform in a unified format.

5. The multi-platform collaborative data collection and analysis method according to claim 1, characterized in that: The data integration engine receives data from the adaptation middle layer, including the format, encoding and data structure between different platforms; the data integration engine pre-processes the data, including data cleaning, to remove duplicate, erroneous or incomplete data; Data conversion, converting data into unified formats and standards.

6. The multi-platform collaborative data collection and analysis method according to claim 5, characterized in that: The data preprocessing stage first involves data cleaning, which involves identifying and processing duplicate items in the data, identifying duplicate data by comparing record identifiers, and merging or deleting them. Next, errors in the data are corrected. For example, data with format errors or logical errors are corrected using predefined rules or algorithms. For missing data, records are deleted or statistical methods, including mean, median, and mode, are used for interpolation and completion, depending on the nature and importance of the data. After data cleaning, data conversion is performed to convert data in different formats into a unified standard format, including conversion of data types, unification of units, and standardization of coding. After conversion, data verification is performed to check whether the data conforms to the input format of the platform.

7. The multi-platform collaborative data collection and analysis method according to claim 1, characterized in that: The data warehouse adopts a distributed architecture to distribute data to multiple servers or nodes to distribute the load and improve processing capacity; through load balancing technology, data access requests are dynamically allocated to avoid overloading of a single node; Data partitioning strategies allow data warehouses to logically divide data and process multiple requests in parallel; Data warehouses use caching mechanisms to store frequently accessed data in fast-access storage media; The data warehouse implements redundant storage and failover mechanisms to maintain service continuity when a platform failure occurs.

8. The multi-platform collaborative data collection and analysis method according to claim 1, characterized in that: The data processing and analysis module explores the pre-processed data through visualization tools, allowing users to intuitively understand the distribution, characteristics and correlation of the data; by selecting the chart type, the data is presented in a graphical way to discover the patterns and trends in the data; according to the platform's goals and data characteristics, the classification algorithm is used to predict user behavior, the clustering algorithm is used to group users, and the association rule mining is used to discover the correlation between user behaviors; the results of data mining and analysis are interpreted and applied, and converted into actionable decision-making recommendations.