Data network system for cross-domain data interconnection and intercommunication

Abstract

The invention discloses a cross-domain data interconnection and intercommunication data network system, and relates to the technical field of data network systems, a preprocessing module is used for processing data obtained from different sources, cleaning, converting and standardizing the data, a security module is used for guaranteeing the security of the data in the transmission and storage process, and a privacy protection measure is implemented; the standardization module defines a data format and a communication protocol in the data transmission process so that data between different systems can be explained and exchanged, the cross-domain communication module provides a communication mechanism between the different systems in the data transmission process, the communication mechanism comprises message transmission and remote calling, the data mining module analyzes and mines the processed data, and the cross-domain communication module provides a communication mechanism between the different systems. Valuable information and modes are extracted, and the automatic decision-making module realizes automatic decision-making and optimization based on a data analysis result. According to the network system, the cross-domain data interconnection and intercommunication data network system is provided for cross-domain data intercommunication, so that the business process efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data network systems, and in particular to a data network system for cross-domain data interconnection. Background Art

[0002] As business develops, organizations need to work more closely with external partners, suppliers, and customers. Traditional information systems are often closed and independent, leading to the existence of data islands. It is difficult for different organizations and departments to share data, which limits the full use of information and the maximization of its value. In the traditional network environment, different organizations, enterprises, or departments usually have independent data systems and network structures, which leads to the problem of data islands and makes cross-domain data interoperability difficult.

[0003] Based on this, the present invention proposes a data network system for cross-domain data interconnection and intercommunication to perform cross-domain data intercommunication and improve business process efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a data network system for cross-domain data interconnection and intercommunication to solve the deficiencies in the background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a data network system for cross-domain data interconnection and intercommunication, comprising a data acquisition module, a preprocessing module, a data storage module, a security module, a standardization module, a cross-domain communication module, a data mining module, a user interface module, and an automated decision module;

[0006] Data acquisition module: responsible for collecting data from different sources, including sensors, databases, API data, and processing multiple data formats and sources;

[0007] Preprocessing module: processes data obtained from different sources and performs cleaning, transformation and standardization;

[0008] Data storage module: stores the cleaned and preprocessed data in a central or distributed database;

[0009] Security module: ensures the security of data during transmission and storage, and implements privacy protection measures;

[0010] Standardization module: During data transmission, it defines the data format and communication protocol to enable data to be interpreted and communicated between different systems;

[0011] Cross-domain communication module: provides communication mechanisms between different systems during data transmission, including message passing and remote calls;

[0012] Data mining module: analyzes and mines the processed data to extract valuable information and patterns;

[0013] User interface module: provides a user interface that enables users to easily access and query the integrated data, including data visualization and reporting;

[0014] Automated decision-making module: realizes automated decision-making and optimization based on the results of data analysis.

[0015] Preferably, the data mining module obtains data parameters and network parameters during data transmission, the data parameters include data integrity and data volume deviation, and the network parameters include network flow deviation and network security warning frequency;

[0016] The data integrity, data volume deviation, network traffic deviation and network security warning frequency are calculated comprehensively to obtain the data coefficient, which is expressed as follows: Wherein, is the data coefficient, swz, spc, wlp, ajs are data integrity, data volume deviation, network traffic deviation and network security warning frequency, respectively, α, β, γ, δ are the proportional coefficients of data integrity, data volume deviation, network traffic deviation and network security warning frequency, respectively, and α, β, γ, δ are all greater than 0.

[0017] Preferably, after the automated decision module obtains the data coefficient, the larger the data coefficient is, the more abnormal the data transmission process is. The obtained data coefficient is compared with a preset abnormal threshold value, and the abnormal threshold value is used to distinguish whether there is an abnormality in the data transmission process. If the data coefficient is greater than or equal to the abnormal threshold value, it is analyzed that there is no abnormality in the data transmission process. If the data coefficient is less than the abnormal threshold value, it is analyzed that there is an abnormality in the data transmission process, and the data transmission is automatically stopped.

[0018] During the data transmission process, the data transmission data coefficient is obtained in real time, and the period when the data coefficient is less than the abnormal threshold is recorded as the abnormal warning period, and the period when the data coefficient is greater than or equal to the abnormal threshold is recorded as the data transmission period;

[0019] The data fluctuation coefficient is obtained by integrating the abnormal warning period and the data transmission period. The function expression is:

[0020]

[0021] ; In the formula, zsx is the data fluctuation coefficient, S(t) is the change in real-time data transmission rate, [tx, ty] is the data transmission period, and [ti, tj] is the abnormal warning period;

[0022] The data fluctuation coefficient obtained at a fixed time is mapped onto a line graph to form a plurality of data points, the line graph is obtained after connecting the plurality of data points on the line graph, and the line graph is sent to an administrator.

[0023] Preferably, the preprocessing module detects and processes missing values, outliers and duplicate values ​​in the data, converts the data to adapt to the standards or requirements within the system, including conversion of data types and units, standardizes the data to have a consistent scale and format, integrates data from different data sources into a consistent data set, including merging tables and connecting data operations, and if the data contains time series information, performs time series processing, including conversion of timestamps, adjustment of time zones, and row outlier detection and processing.

[0024] Preferably, the security module implements an identity authentication mechanism so that only authorized users can access the system and data, including passwords, multi-factor authentication, configures fine-grained access control, restricts data based on user roles and permission management, uses encryption technology during data transmission and storage, including transport layer encryption and data storage layer encryption, adopts network security measures, including firewalls, intrusion detection systems, virtual private networks, records and audits system operations and accesses, monitors potential security threats, and provides log data for subsequent investigations, regularly performs system vulnerability scans and assessments, fixes discovered vulnerabilities, and implements regular data backup and recovery plans.

[0025] Preferably, the standardization module is used for data formats transmitted between systems, including JSON, XML, and CSV, defines fields in the data format, and formulates consistent naming conventions, defines communication protocols between systems, including the method of data transmission, protocol version, and encoding and decoding specifications used in the data transmission process, and writes documents for the defined data formats and communication protocols, which contain the structure of the data format, the meaning of the fields, the usage of the protocol, and manages the versions of the data format and communication protocol.

[0026] Preferably, the cross-domain communication module defines an interface for communication between systems, including API endpoints, request and response formats, uses an asynchronous communication mechanism for communication, encrypts and authenticates data using HTTPS during transmission, handles error data based on an error handling mechanism, including standardization of error codes and error messages, and records logs of the communication module, including sent and received messages.

[0027] Preferably, the user interface module visualizes the integrated data through data visualization tools, displays the data in the form of charts, graphs, and maps, and provides a report generation function, allowing users to generate customized reports and select different data dimensions and metrics as needed.

[0028] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0029] 1. The present invention processes data obtained from different sources through a preprocessing module, performs cleaning, conversion and standardization, and a security module ensures the security of data during transmission and storage, and implements privacy protection measures. The standardization module defines the data format and communication protocol during data transmission, so that data between different systems can be interpreted and communicated. The cross-domain communication module provides a communication mechanism between different systems during data transmission, including message transmission and remote call. The data mining module analyzes and mines the processed data to extract valuable information and patterns. The automated decision-making module realizes automated decision-making and optimization based on the results of data analysis. The network system proposes a data network system with cross-domain data interconnection to perform cross-domain data intercommunication, thereby improving the efficiency of business processes;

[0030] 2. The present invention obtains data transmission data coefficients in real time during the data transmission process, and records the time period when the data coefficient is less than the abnormal threshold as the abnormal warning period, and records the time period when the data coefficient is greater than or equal to the abnormal threshold as the data transmission period, obtains the data fluctuation coefficient after performing integral calculations on the abnormal warning period and the data transmission period, maps the data fluctuation coefficients obtained at regular intervals to a line graph to form multiple data points, obtains the line graph after connecting the multiple data points on the line graph, and sends the line graph to the administrator, thereby facilitating the administrator to understand the status of data transmission and make corresponding decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0032] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0034] Example: See Figure 1As shown, a data network system for cross-domain data interconnection and intercommunication described in this embodiment includes a data acquisition module, a preprocessing module, a data storage module, a security module, a standardization module, a cross-domain communication module, a data mining module, a user interface module, and an automated decision module;

[0035] Data acquisition module: responsible for collecting data from different sources, which may involve sensors, databases, APIs, etc. It can handle multiple data formats and sources, and the data is sent to the pre-processing module;

[0036] Data source identification: Identify and determine the source of the data to be collected. This can include sensors, databases, external APIs, etc. The system needs to be able to interact with different types of data sources. Connection establishment: After determining the source of the data, establish a connection with the data source. This may involve network communication, database connection, or other appropriate communication protocols. Data collection: Get data from the established connection. Data collection can be real-time or periodic, depending on the nature of the data source and the collection requirements. Data format processing: Process the data obtained from different sources and convert it into a unified data format. This includes data parsing, cleaning, and standardization to ensure consistency and reliability of subsequent processing. Exception handling: During the data collection process, exceptions may occur, such as network interruption, data source unavailability, etc. The collection module needs to have an exception handling mechanism to ensure that the system can adapt to various abnormal situations. Data storage: Temporarily store the collected data and wait to be sent to the preprocessing module. This can be a storage mechanism such as cache, message queue, etc. to ensure the reliability and timeliness of the data. Metadata recording: Record the metadata information of the collected data, including the source of the data, timestamp, data format, etc. Metadata is very important for subsequent data analysis and management. Data transfer: The collected data is transferred to the pre-processing module. This can be done through message passing, API calls, etc. to ensure that the data can smoothly enter the subsequent processing stage.

[0037] Preprocessing module: processes data obtained from different sources, cleans, converts and standardizes them to ensure data consistency and quality. The processed data is sent to the data storage module, data mining module and visualization module;

[0038] Data cleaning: Detect and process missing values, outliers, and duplicate values ​​in the data. Cleaning operations help improve the accuracy and reliability of data. Data conversion: Convert data to meet the standards or requirements within the system. This may involve conversion of data types, conversion of units, etc., to ensure that the data format meets the unified standards of the system. Data standardization: Standardize data to have consistent scales and formats. Standardization helps ensure that data from different sources can be effectively compared and analyzed. Data integration: Integrate data from different data sources into a consistent data set. This may include operations such as merging tables and connecting data to facilitate comprehensive analysis. Time series processing: If the data contains time series information, perform time series processing, such as timestamp conversion, time zone adjustment, etc., to ensure the consistency of time data. Outlier processing: Perform outlier detection and processing to ensure that outliers do not have a negative impact on subsequent analysis and mining. Data quality assessment: Evaluate the quality of data, including accuracy, completeness, consistency, etc. By establishing quality standards, you can better understand the credibility of the data. Feature engineering: Perform feature engineering on the data, which may include feature extraction, dimensionality reduction, generation of new features, etc., to improve the performance of the data in subsequent analysis. Data segmentation: Split the data into appropriate training sets and test sets for use in machine learning and model training. Data storage: Store the preprocessed data in the data storage module for subsequent access and query. Data sending: Send the preprocessed data to the data mining module and visualization module for further analysis and presentation.

[0039] Data storage module: stores the cleaned and preprocessed data in a central or distributed database for subsequent access and query;

[0040] Select a database: Select a suitable database system based on system requirements and data characteristics. This may involve relational databases (such as MySQL, PostgreSQL), NoSQL databases (such as MongoDB, Cassandra), distributed databases (such as Hadoop, Couchbase), etc. Database design: Design the database schema, including the structure of the table, the creation of indexes, etc. The database design should meet the storage and query requirements of the data, and take into account the consistency and performance of the data. Data storage: Store the cleaned and pre-processed data in the database. This may include operations such as batch insertion and data loading to ensure the safe storage of data. Distributed storage: If a distributed database system is used, configure and manage distributed storage to ensure high availability, scalability and fault tolerance of the data. Data indexing: Create appropriate indexes to improve the performance of data queries. The choice of indexes should be optimized according to the query pattern and frequency. Transaction management: Configure the transaction management mechanism to ensure the consistency and integrity of the data. This is especially important, especially when complex data update and deletion operations are involved. Backup and recovery: Set up regular data backup and recovery plans to deal with unexpected data loss or system failures. Security measures: Implement database security measures, including access control, authentication, encryption, etc., to prevent unauthorized access and data leakage. Performance monitoring: Set up a performance monitoring mechanism to monitor database performance in real time, as well as identify and resolve potential performance bottlenecks. Version control: If necessary, consider implementing database version control to track and manage changes in database schemas. Query optimization: For common query patterns, perform query optimization to improve query response speed. Data archiving: For historical data, consider implementing a data archiving strategy to reduce database storage pressure.

[0041] Security module: responsible for ensuring the security of data during transmission and storage, and implementing privacy protection measures to comply with relevant regulations and standards;

[0042] Authentication: Implement strict authentication mechanisms to ensure that only authorized users can access systems and data. This can include passwords, multi-factor authentication, etc. Access control: Configure fine-grained access control to restrict data based on user roles and permission management. Ensure that users can only access the data they need. Data encryption: Use encryption technology during data transmission and storage to prevent unauthorized access. Including transport layer encryption (TLS / SSL) and data storage layer encryption. Network security: Use network security measures, including firewalls, intrusion detection systems (IDS), virtual private networks (VPN), etc. to protect the system from network attacks. Security audit: Record and audit system operations and access to monitor potential security threats and provide log data for subsequent investigations. Vulnerability management: Regularly perform system vulnerability scans and assessments, and promptly repair discovered vulnerabilities to ensure system security. Data backup and recovery: Implement regular data backup and recovery plans to prevent data loss or enable rapid recovery in the event of system failure. Privacy protection: For sensitive data, privacy protection measures such as data anonymization and desensitization are adopted to reduce the risk of personal information leakage. Compliance Check: Regularly check the compliance of the system to ensure compliance with relevant regulations and standards, such as GDPR, HIPAA, etc. Security Training: Conduct security training for system users and administrators to improve their awareness of security risks and ensure that they understand and follow security best practices. Emergency Response Plan: Develop an emergency response plan to respond to potential security incidents, including security vulnerabilities being exploited, data leaks, etc. Continuous Monitoring: Implement a continuous monitoring mechanism to promptly detect and respond to potential security threats to maintain the security of the system.

[0043] Standardization module: During data transmission, it defines the data format and communication protocol to ensure that data between different systems can be correctly interpreted and communicated;

[0044] Develop data format standards: Determine the data format used for transmission between systems, which may include JSON, XML, CSV, etc. Choosing a common data format can increase interoperability between systems. Field definition and naming specifications: Define the fields in the data format and develop consistent naming specifications. Ensure that different systems have consistent understandings of data fields to avoid ambiguity. Develop communication protocols: Define the communication protocols between systems, including the method of data transmission, protocol version, etc. RESTful API, SOAP, GraphQL, etc. are all common communication protocols. Data encoding and decoding specifications: Determine the encoding and decoding specifications used in the data transmission process to ensure that the data can be correctly interpreted and restored during the transmission process. Protocol documentation: Write detailed documents for the defined data format and communication protocol. The document should include the structure of the data format, the meaning of the fields, the usage of the protocol, etc., so that system developers can understand and follow these standards. Version management: Manage the versions of data formats and communication protocols to ensure that system updates and upgrades do not break compatibility with other systems. Using semantic versioning can better manage version changes. Error handling specifications: Develop error handling specifications and define the formats of error codes and error messages so that the cause of the problem can be clearly indicated in data transmission. Security considerations: Consider security when developing data formats and communication protocols, and ensure that data is properly encrypted and protected to prevent data leakage or tampering. Compatibility testing: Perform compatibility testing of data formats and communication protocols to ensure that different systems can correctly understand and process standardized data. Continuous evolution: Standardized modules should be able to continuously evolve to adapt to changes in systems and businesses, ensuring that standardized data transmission still meets actual needs.

[0045] Cross-domain communication module: provides communication mechanisms between different systems during data transmission, which may include message passing, remote calls or other communication methods;

[0046] Communication protocol selection: Select an appropriate communication protocol, such as HTTP / HTTPS, MQTT, AMQP, etc. The choice of protocol should take into account the communication requirements and performance requirements between systems. Message format definition: Define the message format transmitted between systems, including message headers, message bodies, and possible attachments. The message format should comply with standards to ensure that different systems can correctly parse and process messages. Message queue or middleware selection: If the messaging mode is adopted, select a suitable message queue or middleware, such as RabbitMQ, Apache Kafka, ActiveMQ, etc., to ensure reliable message delivery and processing. Remote call protocol selection: If the remote call mode is adopted, select an appropriate remote call protocol, such as RESTful API, SOAP, gRPC, etc. The choice of protocol should take into account the integration requirements and performance characteristics between systems. Interface definition: Define clear interfaces for communication between systems, including API endpoints, request and response formats, etc. The interface definition should comply with standards to facilitate system integration. Asynchronous communication mechanism: Consider adopting an asynchronous communication mechanism to improve the responsiveness and processing capabilities of the system. Message queues and asynchronous event-driven modes are common implementation methods. Security considerations: Consider security in the communication module to ensure that data is properly encrypted and authenticated during transmission. Using HTTPS or other secure protocols can ensure the confidentiality of communication. Error handling mechanism: Define a clear error handling mechanism, including standardization of error codes and error messages. Communication between systems should be able to clearly convey error conditions and provide sufficient information for debugging and repair. Logging: Record logs of communication modules, including sent and received messages, for troubleshooting and performance optimization. Performance optimization: Optimize the performance of the communication module, considering mechanisms such as message compression, connection pooling, and load balancing to improve the efficiency and stability of communication. Version management: If the communication interface may change, consider implementing version management to ensure that different systems are compatible with old versions when upgraded. Continuous monitoring: Implement a continuous monitoring mechanism to monitor the performance, availability, and security of the communication module to promptly discover and resolve potential problems.

[0047] Data mining module: Analyze and mine the processed data to extract valuable information and patterns, and send the data analysis results to the automated decision-making module;

[0048] Obtain data parameters and network parameters during data transmission. Data parameters include data integrity and data volume deviation. Network parameters include network traffic deviation and network security warning frequency.

[0049] The data integrity, data volume deviation, network traffic deviation and network security warning frequency are calculated comprehensively to obtain the data coefficient, which is expressed as follows: Wherein, is the data coefficient, swz, spc, wlp, ajs are data integrity, data volume deviation, network traffic deviation and network security warning frequency, respectively, α, β, γ, δ are the proportional coefficients of data integrity, data volume deviation, network traffic deviation and network security warning frequency, respectively, and α, β, γ, δ are all greater than 0.

[0050] User interface module: provides a user interface to enable users to easily access and query the integrated data, including data visualization and reporting functions;

[0051] User needs analysis: Identify user needs and expectations, and understand their expectations of the system interface so that an interface that meets user expectations can be designed. User interface design: Design an intuitive and easy-to-use user interface, including layout, navigation, color, font, and other aspects. Use user experience (UX) and user interface (UI) design best practices. Interaction design: Determine how users interact with the system, including the design of elements such as buttons, forms, filters, and searches. Optimize the interaction process between users and the system. Data visualization: Visualize the integrated data and display the data in the form of charts, graphs, maps, etc. Select appropriate data visualization tools and libraries to present the data intuitively. Report generation: Provide report generation functions so that users can generate customized reports and select different data dimensions and metrics as needed. Navigation and search: Design an effective navigation structure so that users can quickly find the information they need. Provide search functions that support full-text search and filter search. Responsive design: Ensure that the user interface can run normally on different devices, adopt responsive design principles, and adapt to different screen sizes and resolutions. Permission management: Implement a permission management mechanism to ensure that users can only access data and functions for which they have permissions. Provide role management and fine-grained permission control. Multi-language support: If the system may be used in a multi-language environment, consider providing multi-language support to adapt to the language needs of different regions and users. User feedback: Provide user feedback mechanisms, such as user surveys, feedback forms, etc., to understand user satisfaction with the interface and suggestions for improvement. Performance optimization: Optimize the performance of the user interface to ensure fast page loading speed and short response time to improve user experience. Continuous improvement: Continuously improve the user interface based on user feedback, system usage and new requirements to ensure that it is consistent with business goals and user expectations.

[0052] Automated decision-making module: Based on the results of data analysis, it realizes automated decision-making and optimization to improve the intelligence and efficiency of the system;

[0053] After obtaining the data coefficient, the larger the data coefficient is, the more it indicates that there is no abnormality in the data transmission process. Therefore, the obtained data coefficient is compared with the preset abnormality threshold. The abnormality threshold is used to distinguish whether there is an abnormality in the data transmission process. If the data coefficient is greater than or equal to the abnormality threshold, it is analyzed that there is no abnormality in the data transmission process. If the data coefficient is less than the abnormality threshold, it is analyzed that there is an abnormality in the data transmission process, and the data transmission is automatically stopped.

[0054] During the data transmission process, the data transmission data coefficient is obtained in real time, and the period when the data coefficient is less than the abnormal threshold is recorded as the abnormal warning period, and the period when the data coefficient is greater than or equal to the abnormal threshold is recorded as the data transmission period;

[0055] The data fluctuation coefficient is obtained by integrating the abnormal warning period and the data transmission period. The function expression is:

[0056]

[0057] ; In the formula, zsx is the data fluctuation coefficient, S(t) is the change in real-time data transmission rate, [tx, ty] is the data transmission period, and [ti, tj] is the abnormal warning period;

[0058] The data fluctuation coefficient obtained at a fixed time is mapped onto a line graph to form a plurality of data points, the line graph is obtained after connecting the plurality of data points on the line graph, and the line graph is sent to an administrator.

[0059] The present application obtains the data transmission data coefficient in real time during the data transmission process, and records the time period when the data coefficient is less than the abnormal threshold as the abnormal warning period, and records the time period when the data coefficient is greater than or equal to the abnormal threshold as the data transmission period, and obtains the data fluctuation coefficient after performing integral calculations on the abnormal warning period and the data transmission period, maps the data fluctuation coefficient obtained at regular intervals to a line graph to form multiple data points, obtains the line graph after connecting the multiple data points on the line graph, and sends the line graph to the administrator, so as to facilitate the administrator to understand the status of data transmission and make corresponding decisions.

[0060] This application processes data obtained from different sources through a preprocessing module, and performs cleaning, conversion and standardization. The security module ensures the security of data during transmission and storage, and implements privacy protection measures. The standardization module defines the data format and communication protocol during data transmission, so that data between different systems can be interpreted and communicated. The cross-domain communication module provides a communication mechanism between different systems during data transmission, including message transmission and remote calls. The data mining module analyzes and mines the processed data to extract valuable information and patterns. The automated decision-making module realizes automated decision-making and optimization based on the results of data analysis. This network system proposes a data network system for cross-domain data interconnection and intercommunication to perform cross-domain data intercommunication, thereby improving the efficiency of business processes.

[0061] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0062] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0063] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A data network system for cross-domain data interconnection and intercommunication, characterized by: It includes data acquisition module, preprocessing module, data storage module, security module, standardization module, cross-domain communication module, data mining module, user interface module and automated decision-making module; Data acquisition module: responsible for collecting data from different sources, including sensors, databases, API data, and processing multiple data formats and sources; Preprocessing module: processes data obtained from different sources and performs cleaning, transformation and standardization; Data storage module: stores the cleaned and preprocessed data in a central or distributed database; Security module: ensures the security of data during transmission and storage, and implements privacy protection measures; Standardization module: During data transmission, it defines the data format and communication protocol to enable data to be interpreted and communicated between different systems; Cross-domain communication module: provides communication mechanisms between different systems during data transmission, including message passing and remote calls; Data mining module: analyzes and mines the processed data to extract valuable information and patterns; User interface module: provides a user interface that enables users to easily access and query the integrated data, including data visualization and reporting; Automated decision-making module: realizes automated decision-making and optimization based on the results of data analysis.

2. A data network system for cross-domain data interconnection and intercommunication according to claim 1, characterized in that: The data mining module obtains data parameters and network parameters during data transmission, wherein the data parameters include data integrity and data volume deviation, and the network parameters include network flow deviation and network security warning frequency; The data integrity, data volume deviation, network traffic deviation and network security warning frequency are calculated comprehensively to obtain the data coefficient, which is expressed as follows: Wherein, is the data coefficient, swz, spc, wlp, ajs are the data integrity, data volume deviation, network traffic deviation and network security warning frequency respectively, α, β, γ, δ are the proportional coefficients of data integrity, data volume deviation, network traffic deviation and network security warning frequency respectively, and α, β, γ, δ are all greater than 0.

3. A data network system for cross-domain data interconnection and intercommunication according to claim 2, characterized in that: After the automated decision module obtains the data coefficient, the larger the data coefficient is, the more abnormal the data transmission process is. The obtained data coefficient is compared with a preset abnormal threshold value, and the abnormal threshold value is used to distinguish whether there is an abnormality in the data transmission process. If the data coefficient is greater than or equal to the abnormal threshold value, it is analyzed that there is no abnormality in the data transmission process. If the data coefficient is less than the abnormal threshold value, it is analyzed that there is an abnormality in the data transmission process, and the data transmission is automatically stopped. During the data transmission process, the data transmission data coefficient is obtained in real time, and the period when the data coefficient is less than the abnormal threshold is recorded as the abnormal warning period, and the period when the data coefficient is greater than or equal to the abnormal threshold is recorded as the data transmission period; The data fluctuation coefficient is obtained by integrating the abnormal warning period and the data transmission period. The function expression is: ； Where zsx is the data fluctuation coefficient, S(t) is the change in real-time data transmission rate, [tx, ty] is the data transmission period, and [ti, tj] is the abnormal warning period; The data fluctuation coefficient obtained at a fixed time is mapped onto a line graph to form a plurality of data points, the line graph is obtained after connecting the plurality of data points on the line graph, and the line graph is sent to an administrator.

4. A data network system for cross-domain data interconnection and intercommunication according to claim 3, characterized in that: The preprocessing module detects and processes missing values, abnormal values ​​and duplicate values ​​in the data, converts the data to adapt to the standards or requirements within the system, including data type conversion, unit conversion, standardizes the data to make it have a consistent scale and format, and integrates data from different data sources into a consistent data set, including merging tables and connecting data operations. If the data contains time series information, it processes the time series, including timestamp conversion, time zone adjustment, and row outlier detection and processing.

5. A data network system for cross-domain data interconnection and intercommunication according to claim 4, characterized in that: The security module implements an identity authentication mechanism so that only authorized users can access the system and data, including passwords and multi-factor authentication, configures fine-grained access control, restricts data based on user roles and permission management, uses encryption technology during data transmission and storage, including transport layer encryption and data storage layer encryption, adopts network security measures, including firewalls, intrusion detection systems, and virtual private networks, records and audits system operations and accesses, monitors potential security threats, and provides log data for subsequent investigations, regularly performs system vulnerability scans and assessments, fixes discovered vulnerabilities, and implements regular data backup and recovery plans.

6. A data network system for cross-domain data interconnection and intercommunication according to claim 5, characterized in that: The standardization module is used for data formats transmitted between systems, including JSON, XML, and CSV, defines fields in the data format, and formulates consistent naming conventions, defines communication protocols between systems, including the data transmission method, protocol version, and encoding and decoding specifications used in the data transmission process, and writes documents for the defined data formats and communication protocols. The documents contain the structure of the data format, the meaning of the fields, the usage of the protocol, and manage the versions of the data format and communication protocol.

7. A data network system for cross-domain data interconnection and intercommunication according to claim 6, characterized in that: The cross-domain communication module defines an interface for communication between systems, including API endpoints, request and response formats, uses an asynchronous communication mechanism for communication, encrypts and authenticates data using HTTPS during transmission, handles error data based on an error handling mechanism, including standardization of error codes and error messages, and records logs of the communication module, including sent and received messages.

8. A data network system for cross-domain data interconnection and intercommunication according to claim 7, characterized in that: The user interface module visualizes the integrated data through data visualization tools, displays the data in the form of charts, graphs, and maps, and provides report generation functions, allowing users to generate customized reports and select different data dimensions and metrics as needed.

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