Ship construction data intelligent aggregation and automatic response system based on enterprise WeChat

Abstract

The invention discloses a ship construction data intelligent aggregation and automatic response system based on enterprise WeChat. The system comprises an enterprise WeChat front-end user interaction module, a demand analysis and intelligent matching engine module, a multi-source data fusion platform module and an automatic analysis execution module. According to the intelligent aggregation and automatic response system for the ship construction data, complete intranet operation is achieved, no external server is needed, and the risk of data leakage does not exist; all codes are autonomous and controllable, all modules are self-researched and self-controlled, no third-party dependence exists, and rapid expansion can be achieved according to actual production; the communication cost is obviously reduced, and the field efficiency is greatly improved; deep fusion and standardized management of multi-system data are realized, and aperture consistency is ensured; pB-level data processing is supported, high performance and high reliability are achieved, it is guaranteed that the stability can be fed back in real time in any operation scene (hot, cold, wind and rain) through a database and a modular execution frame, and the overall response speed is increased.

Description

Technical Field

[0001] This invention relates to the fields of enterprise digitalization, automation and information system integration technology, and in particular to a shipbuilding data intelligent aggregation and automatic response system based on WeChat for Enterprise. Background Technology

[0002] In large manufacturing enterprises, on-site personnel, especially workers, process engineers, planners, quality inspectors, and warehouse staff, need to frequently travel between multiple locations to obtain information, such as material arrival status, process progress, drawing versions, inventory information, and project data, under extreme conditions of sweltering heat during the hottest days of summer and freezing cold during the coldest days of winter. Traditional information flow methods suffer from the following prominent problems: severe data silos, lack of effective connections between systems, and multiple business systems and data sources within the enterprise, such as Excel. The lack of unified interfaces and standards between various systems, such as ledgers, on-site records, departmental internal databases, RPA data capture records, and file servers, creates typical data silos. Data across work areas and positions cannot be automatically linked. Non-standard data formats make information retrieval difficult and prone to errors. Inconsistent formats and field definitions across different departments lead to query difficulties, lack of traceability, and even information conflicts, significantly impacting on-site work efficiency. Much information relies on manual communication, resulting in high and unstable time costs. Workers often need to travel to multiple departments or work areas to confirm production data. Process engineers, planners, and warehouse personnel also need to constantly answer repetitive questions, causing significant communication costs. During busy production seasons or extreme weather, this "running around for information" work pattern is both physically demanding and affects production efficiency. Existing tools have limited capabilities and cannot support high concurrency and standardization requirements. Excel performance degrades significantly with millions of rows of data, RPA is unstable in complex scenarios, and data security is limited. External SaaS... The service fails to meet intranet security requirements, information response is untimely, it lacks automated response capabilities, and it lacks a unified data middleware layer to support enterprise-wide automation. Summary of the Invention

[0003] The purpose of this invention is to provide a smart aggregation and automatic response system for ship construction data based on WeChat for Enterprise, which is based on an intranet, has fully autonomous and controllable code, and can achieve real-time automatic response.

[0004] The technical solution adopted by this invention to achieve the above objectives is: a shipbuilding data intelligent aggregation and automatic response system based on WeChat for Enterprise, comprising: The Enterprise WeChat front-end user interaction module is used to receive user requests and automatically identify user identity, permissions and behaviors, including text input, shortcut buttons and menus, user information integration, data collection and feedback management; The requirement parsing and intelligent matching engine module automatically parses user input into calling module analysis Process, structured parameters Parameter, and / or extended parameters. Through keyword matching, rule trees, and field mapping mechanisms, it realizes the conversion of natural language into structured parameters, parameters into corresponding Python modules, and modules into automatic execution paths. The multi-source data fusion platform module supports various intranet data sources in the backend, including enterprise databases, Excel ledgers, RPA crawling results, OCR image recognition results, network shared folders, and specialized business systems. The automated analysis and execution module uses a Python modular execution engine to perform data filtering and logical operations, cross-table and cross-system correlation analysis, file generation and processing, action execution, compliance checks and boundary verification. The Enterprise WeChat automatic feedback module automatically returns results via Enterprise WeChat, including text, files, table screenshots, charts, landing prompts, or next step guidance, which can be viewed directly on your mobile phone on-site.

[0005] The multi-source data fusion platform module completes all data processing within the intranet. The processing steps include data acquisition, field cleaning, format standardization, rule-based preprocessing, and multi-system association. All data flows in a closed loop within the intranet.

[0006] The actions executed in the automatic analysis and execution module include copying, moving, OCR image recognition, and RPA execution.

[0007] This invention presents a smart aggregation and automatic response system for shipbuilding data based on WeChat for Enterprise. Operating entirely within an intranet, it ensures data security and compliance, requires no external servers, and eliminates the risk of data leakage. The entire code is independently controllable, allowing for long-term sustainable evolution. All modules are self-developed and self-controlled, with no third-party dependencies, and can be rapidly expanded according to actual production needs. It significantly reduces communication costs and greatly improves on-site efficiency, freeing workers and functional personnel from the hassle of searching for information; they can obtain accurate information in real time through simple fingertip operations. It achieves deep integration and standardized management of data from multiple systems, with a unified data middleware layer enabling automatic data correlation across work areas and departments, ensuring consistency. It supports petabyte-level data processing, offering high performance and high reliability. Stability is guaranteed through a database and modular execution framework, outperforming traditional tools like Excel. Its enterprise-level response capabilities provide anytime, anywhere, and automated feedback in any operational scenario (hot, cold, rainy), improving overall response speed. Attached Figure Description

[0008] Figure 1 This is a front-end response service topology diagram of a ship construction data intelligent aggregation and automatic response system based on WeChat for Enterprise, according to the present invention.

[0009] Figure 2 This is a backend data service topology diagram of a ship construction data intelligent aggregation and automatic response system based on WeChat for Enterprise, according to the present invention. Detailed Implementation

[0010] like Figure 1 and Figure 2 As shown, the intelligent aggregation and automatic response system for shipbuilding data based on WeChat Work includes: a WeChat Work front-end user interaction module, used to receive user requests, automatically identify user identity, permissions, and behaviors, including text input (natural language commands), shortcut buttons and menus, user information integration, data collection and feedback management, enabling direct intranet connection to the WeChat Work API to ensure data does not leave the internal network, automatically identifying user roles, supporting differentiated responses, supporting mobile fingertip operation in both hot and cold environments, and readily available for frontline workers; a requirement parsing and intelligent matching engine module, which automatically parses user input into calling module analysis processes, structured parameters, and / or extended parameters, using keyword matching, rule trees, and field mapping mechanisms to convert natural language into structured parameters, parameters into corresponding Python modules, and modules into automatic execution paths; and a multi-source data fusion platform module, with backend support for various intranet data sources, including enterprise databases (SQL / SQLite), Excel... The system processes all data within the intranet, including ledgers, RPA scraping results, OCR image recognition results, network shared folders, and specialized business systems (process, materials, planning, etc.). The steps involve data acquisition, field cleaning, format standardization, rule-based preprocessing, and multi-system correlation. All data flows in a closed loop within the intranet, ensuring it doesn't leave the firewall. The automated analysis and execution module (fully self-developed) uses a modular Python execution engine for data filtering and logical operations, cross-table and cross-system correlation analysis, file generation and processing, action execution, compliance checks, and boundary verification. Actions include copying, moving, OCR, and RPA. The system includes a WeChat Work automatic feedback module, where results are automatically returned via WeChat Work, including text, files, table screenshots, charts, landing prompts, or next-step guidance. Workers on-site, such as on deck, in warehouse, engine room, outfitting area, etc., can directly view the results on their mobile phones. This invention is based on a WeChat Work-based intelligent aggregation and automatic response system for shipbuilding data. It constructs a unified data entry point on WeChat, a self-controllable multi-source data fusion platform on the backend, and an automatic analysis and feedback engine deployed entirely on the intranet, achieving secure, unified, efficient, and automated flow of internal information. The system is deployed entirely on the intranet, eliminating the risk of transmission over the external network. All code, data structures, and modules are self-developed, with no third-party black-box dependencies. It serves frontline workers, eliminating the need for travel and multi-department communication. They can obtain accurate and real-time production information at their fingertips, enabling workers to request information from the system instead of asking others, greatly saving communication time and improving overall production and collaboration efficiency.

Claims

1. A shipbuilding data intelligent aggregation and automatic response system based on WeChat for Enterprise, characterized in that, include: The Enterprise WeChat front-end user interaction module is used to receive user requests and automatically identify user identity, permissions and behaviors, including text input, shortcut buttons and menus, user information integration, data collection and feedback management; The requirement parsing and intelligent matching engine module automatically parses user input into calling module analysis Process, structured parameters Parameter, and / or extended parameters. Through keyword matching, rule trees, and field mapping mechanisms, it realizes the conversion of natural language into structured parameters, parameters into corresponding Python modules, and modules into automatic execution paths. The multi-source data fusion platform module supports various intranet data sources in the backend, including enterprise databases, Excel ledgers, RPA crawling results, OCR image recognition results, network shared folders, and specialized business systems. The automated analysis and execution module uses a Python modular execution engine to perform data filtering and logical operations, cross-table and cross-system correlation analysis, file generation and processing, action execution, compliance checks and boundary verification. The Enterprise WeChat automatic feedback module automatically returns results via Enterprise WeChat, including text, files, table screenshots, charts, landing prompts, or next step guidance, which can be viewed directly on your mobile phone on-site.

2. The intelligent aggregation and automatic response system for shipbuilding data based on WeChat for Enterprise as described in claim 1, characterized in that: The multi-source data fusion platform module completes all data processing within the intranet. The processing steps include data acquisition, field cleaning, format standardization, rule-based preprocessing, and multi-system association. All data flows in a closed loop within the intranet.

3. The intelligent aggregation and automatic response system for shipbuilding data based on WeChat for Enterprise as described in claim 1, characterized in that: The actions executed in the automatic analysis and execution module include copying, moving, OCR image recognition, and RPA execution.

Images