A method for intelligent management and automatic report generation of a wind power construction task

Abstract

The application discloses a kind of wind power construction's operation task intelligent management and automatic report generation method, belong to engineering technical management field.This method is by deploying multi-source perception equipment in construction site, real-time collection personnel, equipment, environment and regional data, using 4G / 5G, LoRa and other multi-network fusion transmission and edge computing technology, constructs wind power construction site digital twin.Based on deep learning, random forest, LSTM neural network and other AI algorithms, construct intelligent task scheduling, risk prediction and early warning and safety quality monitoring subsystem, realize task automatic distribution, risk early 7-15 days warning, safety quality intelligent identification.Construction automatic report generation module, support progress, quality, safety, cost and other multidimensional report automatic generation.Through system integration realizes cross-platform data sharing, using reinforcement learning to continuously optimize system.The application can improve construction efficiency and safety quality identification accuracy, realize the change from experience-driven to data-driven for wind power construction.

Description

Technical Field

[0001] This invention relates to engineering technology management, and more particularly to a method for intelligent management and automatic report generation of wind power construction tasks. Background Technology

[0002] Wind power, as a crucial component of clean and renewable energy, plays a vital role in the global energy structure transformation. However, wind power projects are characterized by long construction periods, high technical requirements, large investment scales, and complex construction environments, posing significant challenges to project management. Traditional wind power construction management models rely primarily on manual experience and decentralized information systems, exhibiting the following prominent problems:

[0003] First, there is a serious problem of information silos. Wind power projects involve multiple stages, including design, procurement, construction, acceptance, and operation and maintenance. Data from different stages is stored in separate systems, such as CAD for design, Excel for construction, and SCADA for operation and maintenance. The lack of a unified platform for integration leads to data not being shared and low information transmission efficiency.

[0004] Secondly, progress control is difficult. Wind power projects are usually located in remote mountainous areas or coastal areas, and are severely affected by severe weather such as strong winds, heavy rain, and thunderstorms, with only 50%-60% of the year being effective construction days. At the same time, the projects involve hundreds of wind turbine installation sites and thousands of construction workers. The dispersed personnel and delayed information transmission lead to large deviations in plan execution and frequent delays.

[0005] Third, there are many potential quality risks. Wind power construction involves 17 types of specialized operations, including geological surveying, blasting, road construction, slope protection, concrete pouring, large-scale hoisting, cable trench excavation, optical cable splicing, and SCADA system commissioning. The procedures are complex, and there are many potential quality risks. There is a lack of unified quality inspection standards and closed-loop tracking mechanisms, making it difficult to accept concealed works and trace quality.

[0006] Fourth, high safety risks. Wind power construction involves high-altitude operations, heavy hoisting, and complex terrain, resulting in numerous safety hazards. Traditional manual monitoring methods have limitations such as delayed response and insufficient coverage, making it difficult to conduct 24-hour real-time monitoring of high-risk operations and resulting in weak early warning capabilities for safety accidents.

[0007] Fifth, cost control is difficult. Wind power projects have high construction costs, involving multiple cost elements such as equipment procurement, material consumption, labor costs, and equipment leasing. Traditional cost management relies on manual accounting, data updates are lagging, making it difficult to achieve refined control, and cost overruns are common.

[0008] Sixth, low collaboration efficiency. Wind power projects involve multiple stakeholders, including owners, design firms, construction companies, equipment suppliers, supervision firms, government departments, and power grid companies. This leads to high communication costs, decision-making delays, and issues such as unclear responsibilities and frequent changes.

[0009] Seventh, report generation efficiency is low. Wind power project management requires the regular generation of various reports, including progress reports, quality reports, safety reports, and cost reports. Traditional report generation relies on manual data processing, content writing, and formatting, which is time-consuming and labor-intensive, often requiring multiple people and several days to complete. It is also prone to errors, has low standardization, and makes it difficult to guarantee data accuracy.

[0010] With the development of technologies such as artificial intelligence, the Internet of Things, and big data, some wind power companies have begun to try to introduce intelligent methods for project management. For example, deploying smart sensors to monitor equipment status, applying BIM technology for 3D visualization, and using mobile apps for on-site management. However, existing intelligent solutions mostly focus on single links or single functions, lacking a systematic overall solution. They have failed to truly achieve intelligent management and automatic report generation throughout the entire construction process, and suffer from problems such as data fragmentation, functional dispersion, and low integration.

[0011] Therefore, there is an urgent need for a method that can achieve intelligent management of the entire wind power construction process, automatic data collection, intelligent risk early warning, and automatic report generation, in order to improve the efficiency, safety, and quality management level of wind power construction, reduce project management costs, and realize the transformation of wind power construction from "experience-driven" to "data-driven". Summary of the Invention

[0012] Purpose of the invention: The purpose of this invention is to provide a method for intelligent management and automatic report generation of wind power construction tasks. It achieves comprehensive perception of the construction site through Internet of Things technology, intelligent decision-making and risk warning through AI algorithms, and standardized and automated report generation through automatic report generation technology. This solves the problems of severe information silos, high dependence on manual labor, low report generation efficiency, and difficulty in guaranteeing quality in traditional wind power construction management.

[0013] Technical solution: A method for intelligent management and automatic report generation of wind power construction tasks, comprising the following steps:

[0014] S1: Construct an IoT data collection system for wind power construction, deploy multi-source sensing devices at the construction site, including smart safety helmets, positioning wristbands, torque sensors, vibration sensors, temperature sensors, anemometers, rain gauges, high-definition cameras, infrared detectors, and electronic fences, to collect real-time location data, behavior data, qualification data of construction personnel, operating parameter data of hoisting equipment, vibration and temperature data of mechanical equipment, environmental data, and area monitoring data.

[0015] S2: Through a multi-network fusion transmission scheme of 4G / 5G network, LoRa wireless communication, satellite communication and microwave transmission, combined with edge computing technology, the collected data is preprocessed and aggregated to the cloud platform to build a digital twin of the wind power construction site, realizing real-time mapping between physical space and digital space;

[0016] S3: Construct an AI-based intelligent decision analysis module, including an intelligent task scheduling subsystem, a risk prediction and early warning subsystem, and a safety and quality monitoring subsystem. The intelligent task scheduling subsystem uses deep learning models and random forest models for task allocation and resource optimization scheduling based on collected real-time data and historical project data. The risk prediction and early warning subsystem uses an LSTM neural network model to predict risks of project delays, equipment failures, and extreme weather, issuing warnings 7-15 days in advance. The safety and quality monitoring subsystem uses AI image recognition algorithms to analyze camera video streams, identifying safety hazards such as personnel not wearing safety helmets or safety belts, violations of hoisting operations, open flame operations, and lack of edge protection, as well as quality defects such as rebar spacing, formwork support, concrete appearance, and masonry quality, with an accuracy rate of 80%-95%.

[0017] S4: Construct an automatic report generation module, including a report template library, a data aggregation engine, an intelligent template engine, an intelligent analysis engine, and a report distribution system. The report template library has pre-set standardized templates for progress reports, quality reports, safety reports, cost reports, and comprehensive management reports, and supports custom template configurations. The data aggregation engine extracts structured data from the digital twin platform and the intelligent decision analysis module, including key indicators such as task completion rate, quality pass rate, safety incident rate, and cost deviation rate. The intelligent analysis engine performs statistical analysis, trend analysis, and comparative analysis on the aggregated data to generate data insights and suggestions. The intelligent template engine populates the analysis results into the templates to generate standardized report documents. The report distribution system pushes reports to relevant management personnel through multiple channels such as email, WeChat Work, DingTalk, and mobile APP.

[0018] S5: Construct a system integration and collaboration module, providing standard API interfaces for data connection with BIM systems, ERP systems, SCADA systems, financial systems and attendance systems, to achieve cross-system data sharing and collaborative work, and to streamline the information flow of design, procurement, construction and acceptance.

[0019] S6: Build a continuous learning and optimization module to collect historical project data, expert experience and user feedback, and use reinforcement learning algorithms to continuously optimize the intelligent scheduling algorithm, risk prediction model and safety and quality identification algorithm to improve the system's intelligence level.

[0020] The method achieves comprehensive perception of wind power construction sites through IoT technology, intelligent decision-making and risk warning through AI algorithms, and standardized and automated report generation through automatic report generation technology. It realizes the transformation of wind power construction from "experience-driven" to "data-driven", improves the efficiency, safety and quality management level of wind power construction, and solves the problems of serious information silos, high dependence on manual labor, low report generation efficiency and difficulty in guaranteeing quality in traditional wind power construction management.

[0021] Furthermore, the multi-source sensing device deployment strategy in step S1 includes: having personnel wear smart safety helmets and positioning wristbands at the construction site, deploying torque sensors and high-definition cameras on hoisting equipment, setting up anemometers, rain gauges and slope displacement gauges in key construction areas, and installing infrared detectors and electronic fences at the area boundaries, forming a comprehensive sensing network for personnel monitoring, equipment monitoring, environmental monitoring and area monitoring.

[0022] Furthermore, the edge computing technology in step S2 includes deploying an industrial-grade edge computing gateway on-site to perform data cleaning, data filtering, feature extraction, and data compression on the collected raw data, filtering redundant information, reducing transmission pressure, and achieving low-latency transmission of critical data.

[0023] Furthermore, the intelligent task scheduling subsystem in step S3 uses a deep learning model to predict task completion time, uses a random forest model to perform optimal matching of personnel, tasks, and resources, and automatically generates shift and work assignment schemes to avoid personnel being idle or overworked.

[0024] Furthermore, the risk prediction and early warning subsystem in step S3 integrates weather forecast data, equipment operation data, construction progress data, and personnel qualification data to construct a multi-dimensional risk assessment matrix. When a risk of construction delay is predicted, it automatically proposes resource allocation suggestions, and when a safety risk is predicted, it automatically pushes protective measure suggestions.

[0025] Furthermore, the automatic report generation module in step S4 supports two modes: on-demand generation and scheduled generation. The report content covers construction progress, quality status, safety status, cost status, resource status, risk warnings and recommended measures. The report format supports multiple formats such as Word, PDF, Excel and HTML. The report data is updated synchronously with the real-time on-site data to achieve dynamic report generation.

[0026] Furthermore, the report template library in step S4 supports user-defined template configuration, including template structure, data fields, chart types, style formats, and output rules. Users can flexibly adjust the report template according to project characteristics and management needs to achieve personalized report customization.

[0027] Furthermore, the system integration and collaboration module in step S5 connects with external systems through RESTful API interfaces, WebSocket interfaces, and message queue middleware, supporting multiple interaction modes such as real-time data push, historical data query, data subscription, and event triggering, ensuring the timeliness and accuracy of data synchronization.

[0028] Furthermore, the method also includes a mobile application module, providing a mobile APP or mini-program, which allows construction personnel to report progress data, upload site photos, receive task instructions, and view early warning information. Management personnel can view the construction status, approve reports, and issue decision instructions in real time through the mobile terminal, realizing mobile construction management and collaboration.

[0029] Beneficial effects:

[0030] (1) Comprehensive perception and data-driven decision-making at wind power construction sites have been achieved. By deploying multi-source sensing devices, all-round real-time monitoring of personnel, equipment, environment and area has been realized, and a real-time mapping between physical space and digital space has been constructed, providing a data foundation for intelligent decision-making.

[0031] (2) Improved construction efficiency and resource utilization. Through the intelligent task scheduling subsystem, AI algorithms are used for task allocation and resource optimization scheduling, and shift and work assignment plans are automatically generated to avoid idle or overloaded personnel, which can improve construction efficiency by 20%-30%.

[0032] (3) Enhanced risk warning and safety management capabilities. Through the risk prediction and early warning subsystem, risks of project delays, equipment failures, and extreme weather can be warned 7-15 days in advance, allowing project managers sufficient time to respond. Through the safety and quality monitoring subsystem, 24-hour all-weather safety and quality monitoring can be achieved, with an accuracy rate of 80%-95%, significantly reducing the incidence of safety accidents.

[0033] (4) It achieves automated report generation and standardization. Through the automatic report generation module, the report generation work that traditionally required multiple people and several days to complete can be shortened to a few minutes. The reports are highly standardized and the data accuracy is guaranteed. It supports multi-dimensional reports, custom templates, and multiple output formats to meet the needs of different scenarios.

[0034] (5) It breaks down information silos and enables cross-system collaboration. Through the system integration and collaboration module, it connects with external systems such as BIM system, ERP system, SCADA system, financial system, and attendance system to achieve cross-system data sharing and collaborative work, and to connect the information flow of design, procurement, construction and acceptance.

[0035] (6) Support for continuous learning and system optimization. Through the continuous learning and optimization module, historical project data, expert experience and user feedback are collected, and reinforcement learning algorithms are used to continuously optimize the intelligent scheduling algorithm, risk prediction model and safety quality identification algorithm, thereby continuously improving the system's intelligence level.

[0036] (7) Provide mobile applications to improve collaboration efficiency. Through the mobile application module, construction personnel and management personnel can perform operations such as data reporting, task reception, status viewing, and decision approval via mobile devices, thereby realizing mobile construction management and collaboration, and improving management efficiency and decision response speed. Attached Figure Description

[0037] Figure 1 This is a diagram illustrating the overall architecture of the method of the present invention;

[0038] Figure 2 This is a system composition block diagram of the method of the present invention;

[0039] Figure 3 This is a flowchart of the intelligent decision analysis module of the method of the present invention;

[0040] Figure 4 This is a flowchart of the automatic report generation module of the method of the present invention. Detailed Implementation

[0041] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Example 1

[0043] like Figure 1 As shown in the figure, this embodiment provides a method for intelligent management and automatic report generation of wind power construction tasks. The overall architecture includes six layers: data perception layer, network transmission layer, platform support layer, core function layer, application service layer, and user access layer.

[0044] The data perception layer deploys multi-source sensing devices, including smart safety helmets, positioning wristbands, torque sensors, vibration sensors, temperature sensors, anemometers, rain gauges, high-definition cameras, infrared detectors, and electronic fences, to collect multi-dimensional data from the wind power construction site in real time.

[0045] The network transmission layer adopts a multi-network converged transmission scheme, including 4G / 5G networks, LoRa wireless communication, satellite communication and microwave transmission. It combines edge computing technology to preprocess the collected data to ensure the stability and real-time performance of data transmission.

[0046] The platform support layer is built on a cloud-native architecture and includes an IoT platform, a big data platform, an AI computing platform, and a digital twin platform, providing basic services such as data storage, data processing, algorithm computing, and digital twins for upper-layer applications.

[0047] The core functional layer is the core of this invention, including a data processing module, an intelligent decision analysis module, an automatic report generation module, a system integration and collaboration module, and a continuous learning and optimization module.

[0048] The application service layer provides application services such as schedule management, cost management, quality management, safety management, resource management, and report management.

[0049] The user access layer supports multiple access methods, including PC, mobile, and large screen.

[0050] like Figure 2 As shown, the system composition of the method of the present invention includes six core modules: a data processing module, an intelligent decision analysis module, an automatic report generation module, a system integration and collaboration module, a continuous learning and optimization module, and a mobile application module.

[0051] The data processing module is responsible for data collection, cleaning, storage, and querying, constructing a digital twin of the wind power construction site. The module accesses data from multi-source sensing devices through an IoT platform, uses ETL (Extract-Transform-Load) tools to extract, transform, and load the data, establishes a standardized data model, and supports real-time and historical data queries.

[0052] The intelligent decision analysis module includes an intelligent task scheduling subsystem, a risk prediction and early warning subsystem, and a safety and quality monitoring subsystem. The intelligent task scheduling subsystem, based on collected real-time and historical project data, uses deep learning and random forest models for task allocation and resource optimization scheduling, automatically generating shift schedules and work assignment plans. The risk prediction and early warning subsystem uses an LSTM neural network model to predict risks of project delays, equipment failures, and extreme weather, issuing warnings 7-15 days in advance. The safety and quality monitoring subsystem uses AI image recognition algorithms to analyze camera video streams, identifying safety hazards such as personnel not wearing safety helmets or safety belts, violations of hoisting operations, open flame operations, and lack of edge protection, as well as quality defects such as rebar spacing, formwork support, concrete appearance, and masonry quality.

[0053] The automated report generation module includes a report template library, a data aggregation engine, an intelligent template engine, an intelligent analysis engine, and a report distribution system. The report template library has pre-set standardized templates for progress reports, quality reports, safety reports, cost reports, and comprehensive management reports, and supports custom template configurations. The data aggregation engine extracts structured data from the digital twin platform and the intelligent decision analysis module, including key indicators such as task completion rate, quality pass rate, safety incident rate, and cost deviation rate. The intelligent analysis engine performs statistical analysis, trend analysis, and comparative analysis on the aggregated data to generate data insights and recommendations. The intelligent template engine populates the templates with the analysis results to generate standardized report documents. The report distribution system pushes reports to relevant management personnel through multiple channels such as email, WeChat Work, DingTalk, and mobile apps.

[0054] The system integration and collaboration module provides standard API interfaces for data exchange with BIM, ERP, SCADA, financial, and attendance systems, enabling cross-system data sharing and collaborative work, and streamlining information flow across design, procurement, construction, and acceptance stages. The module connects with external systems via RESTful API interfaces, WebSocket interfaces, and message queue middleware, supporting various interaction modes such as real-time data push, historical data query, data subscription, and event triggering.

[0055] The continuous learning and optimization module collects historical project data, expert experience, and user feedback, and uses reinforcement learning algorithms to continuously optimize the intelligent scheduling algorithm, risk prediction model, and safety and quality identification algorithm, thereby improving the system's intelligence level. The module also builds a project knowledge base, recording project experience, problem cases, and solutions, supporting knowledge retrieval and reuse.

[0056] The mobile application module provides a mobile APP or mini-program, which allows construction workers to report progress data, upload site photos, receive task instructions and view early warning information. Management personnel can use the mobile app to view the construction status, approve reports and issue decision instructions in real time, thus realizing mobile construction management and collaboration.

[0057] like Figure 3 As shown, the workflow of the intelligent decision analysis module is as follows:

[0058] First, the data acquisition subsystem acquires real-time data from multi-source sensing devices, including personnel data, equipment data, environmental data, and regional data. Then, the data preprocessing subsystem cleans, filters, extracts features, and compresses the raw data, filtering out redundant information and reducing processing pressure. Next, the intelligent task scheduling subsystem, based on the preprocessed data and project plan data, uses a deep learning model to predict task completion times and a random forest model to achieve optimal matching of personnel, tasks, and resources, generating an intelligent scheduling plan. Simultaneously, the risk prediction and early warning subsystem integrates weather forecast data, equipment operation data, construction progress data, and personnel qualification data, using an LSTM neural network model to predict risks of project delays, equipment failures, and extreme weather, constructing a multi-dimensional risk assessment matrix. When a risk is predicted, it automatically generates early warning information and response suggestions. The safety and quality monitoring subsystem uses AI image recognition algorithms to analyze camera video streams, identifying safety hazards and quality defects with an accuracy rate of 80%-95%, and automatically pushes the results to management personnel. Finally, the decision support subsystem integrates the intelligent scheduling plan, risk early warning information, and safety and quality monitoring information to generate decision-making suggestions to assist management personnel in making decisions.

[0059] like Figure 4 As shown, the workflow of the automatic report generation module is as follows:

[0060] First, users submit a report generation request through the user interface, selecting parameters such as report type, report period, and report template. Then, the data aggregation engine extracts relevant structured data from the digital twin platform and intelligent decision analysis module based on the report type and period, including key indicators such as task completion rate, quality pass rate, safety incident rate, and cost deviation rate. Next, the intelligent analysis engine performs statistical analysis, trend analysis, and comparative analysis on the aggregated data, calculating year-on-year, month-on-month, and deviation indicators, identifying anomalies, and generating data insights and recommendations. The intelligent template engine populates the template with the analysis results, including title, summary, data tables, charts, conclusions, and recommendations, generating a standardized report document. After the report is generated, the report distribution system pushes the report to relevant managers through multiple channels such as email, WeChat Work, DingTalk, and mobile apps, according to the push method set by the user. After receiving the report, users can view the report content, download the report file, or edit it further through the user interface. Users can also save custom templates to the report template library for later reuse.

[0061] The data flow and information flow of the method of this invention are as follows:

[0062] The data stream originates from multi-source sensing devices, is transmitted through the network transport layer to the data processing module, and after data cleaning, transformation, and loading, is stored in the data platform. The intelligent decision analysis module acquires data from the data platform, performs intelligent analysis and decision-making, and generates intelligent scheduling schemes, risk warning information, and safety and quality monitoring information. The automatic report generation module acquires data from the data platform and the intelligent decision analysis module to generate standardized reports. The system integration and collaboration module exchanges data with external systems to achieve cross-system data sharing. The mobile application module interacts with each module to provide mobile services. The continuous learning and optimization module collects data generated by each module and user feedback to optimize models and accumulate knowledge.

[0063] Example 2

[0064] This embodiment uses a mountain wind power project as an example to illustrate the implementation process of the present invention.

[0065] The wind power project is located in a mountainous area with complex terrain. It will install 50 wind turbine units with a single unit capacity of 3MW, for a total installed capacity of 150MW, and the construction period is 18 months.

[0066] First, multi-source sensing equipment was deployed at the construction site. Smart safety helmets and positioning wristbands were equipped for 150 construction workers to monitor their location, behavior, and status in real time. Torque sensors and high-definition cameras were deployed on 10 cranes to monitor lifting operation parameters and on-site video in real time. Anemometers, rain gauges, and slope displacement gauges were installed at 50 wind turbine foundation locations to monitor environmental data in real time. Infrared detectors and electronic fences were installed at the boundaries of the construction area to monitor area safety in real time.

[0067] Secondly, a cloud platform and digital twin are constructed. Data from sensing devices are aggregated to the cloud platform via 4G / 5G networks and LoRa wireless communication, and a digital twin of the wind power project is built on the cloud platform to achieve real-time mapping between physical and digital spaces.

[0068] Next, the intelligent decision analysis module was deployed. The intelligent task scheduling subsystem automatically generates daily shift and work assignment plans based on the qualifications, skills, attendance, and construction task requirements of 150 construction workers, using deep learning and random forest models to avoid idle or overworked personnel. The risk prediction and early warning subsystem integrates weather forecast data, equipment operation data, and construction progress data, using an LSTM neural network model to predict the risks of project delays and equipment failures, issuing warnings 15 days in advance. The safety and quality monitoring subsystem uses AI image recognition algorithms to analyze camera video streams, automatically identifying safety hazards such as personnel not wearing safety helmets or safety belts, and violations of hoisting regulations, as well as quality defects such as rebar spacing and concrete appearance, achieving an accuracy rate of over 90%.

[0069] Next, an automated report generation module was deployed. The report template library includes pre-set standardized templates for progress reports, quality reports, safety reports, cost reports, and comprehensive management reports. Every Monday morning, the system automatically extracts data from the previous week from the data platform and intelligent decision analysis module, performs statistical analysis, and generates a weekly report, which is then pushed to project managers, technical leads, safety managers, and other management personnel via email and WeChat. At the end of each month, the system automatically generates a monthly report, which is pushed to the client and supervision unit via DingTalk. The report content includes key indicators such as task completion rate, quality pass rate, safety incident rate, and cost deviation rate, as well as trend analysis and improvement suggestions. The report generation time has been reduced from 3 people and 3 days using traditional manual methods to 5 minutes, and the standardization of reports has been significantly improved.

[0070] Simultaneously, a system integration and collaboration module is deployed. Data is integrated with the project's BIM, ERP, SCADA, financial, and attendance systems via standard API interfaces, enabling cross-system data sharing and collaborative work. Design drawings, 3D models, and on-site construction data in the BIM system are synchronized in real time, supporting clash detection and virtual acceptance. Equipment procurement, material inventory, and labor cost data in the ERP system are compared in real time with on-site consumption data, enabling dynamic cost control. Equipment operation data in the SCADA system is correlated with on-site construction data, supporting equipment health assessment and predictive maintenance.

[0071] In addition, a mobile application module will be deployed. A mobile app will be developed, allowing construction workers to report daily progress, upload site photos, receive task instructions, and view early warning information. Management personnel can use the app to view construction status, approve reports, and issue decision-making instructions in real time. The mobile app supports offline functionality, storing data locally when there is no network connection and automatically syncing once the network is restored.

[0072] Finally, a continuous learning and optimization module is deployed. This module collects all data, expert experience, and user feedback generated during project implementation and uses reinforcement learning algorithms to continuously optimize the intelligent scheduling algorithm, risk prediction model, and safety and quality identification algorithm. A project knowledge base is built to record project experience, problem cases, solutions, etc., supporting knowledge retrieval and reuse, and providing a reference for subsequent projects.

[0073] Example 3

[0074] This embodiment takes a certain offshore wind power project as an example to specifically illustrate the implementation process of the present invention in the offshore wind power scenario.

[0075] The offshore wind power project is located in the coastal waters of Guangdong Province. It will install 20 wind turbine units with a single unit capacity of 6MW, with a total installed capacity of 120MW and a construction period of 24 months.

[0076] Offshore wind power projects are characterized by short construction windows, complex marine environments, and high safety risks. This invention addresses these unique characteristics by making targeted optimizations and adjustments.

[0077] First, multi-source sensing equipment is deployed on offshore wind power construction platforms and auxiliary vessels. Fifty offshore construction workers are equipped with smart life jackets integrating BeiDou positioning and water-fall detection functions to monitor their location and safety status in real time. Torque sensors and high-definition cameras are deployed on two offshore hoisting machines to monitor hoisting operation parameters in real time. Infrared cameras, radar monitoring systems, and marine environmental monitoring stations are installed on the offshore platform to monitor personnel activity range and marine environmental parameters, including tides, wind speed, wave height, and ocean waves, in real time.

[0078] Secondly, considering the characteristics of the maritime network environment, network transmission solutions were optimized. A combination of satellite communication and microwave transmission was adopted to ensure stable data transmission in offshore areas. Edge computing gateways were deployed on the offshore platform to preprocess and compress the collected data, reducing the amount of data transmitted and achieving low-latency transmission of critical data.

[0079] Then, the intelligent decision analysis module was optimized to meet the specific needs of offshore wind power. The risk prediction and early warning subsystem focuses on marine meteorological risks, integrating meteorological warning information such as typhoons, rainstorms, and high waves, and using AI algorithms to predict the optimal lifting window period, reducing unnecessary waiting time. The safety and quality monitoring subsystem addresses the specific risks of offshore operations, identifying violations such as personnel not wearing life-saving equipment, unauthorized entry into lifting restricted areas, and fatigued work, with an accuracy rate of over 95%.

[0080] Next, considering the numerous participants and complex communication and coordination involved in offshore wind power projects, the automatic report generation module was optimized. The report template library was expanded to include specialized offshore wind power report templates, such as marine environmental monitoring reports, offshore construction window reports, and vessel scheduling reports. The report generation frequency was adjusted to daily reporting, synchronizing the offshore construction status with the shore-based command center and the project owner in real time.

[0081] Meanwhile, considering the complex supply chain of offshore wind power projects, the system integration and collaboration modules have been optimized. Deep integration with offshore logistics systems, vessel scheduling systems, and equipment manufacturing systems enables end-to-end supply chain visibility. Data such as vessel position information, equipment delivery times, and weather forecasts are obtained through API interfaces to optimize construction organization and resource allocation.

[0082] Furthermore, considering the dispersed nature of personnel and the high management difficulty of offshore wind power projects, the mobile application module has been optimized. The mobile app supports video calls and remote collaboration, allowing onshore engineers to provide real-time guidance for offshore operations and resolve unexpected technical challenges. The app also supports one-click alarm and emergency command functions, enabling rapid activation of emergency response procedures in emergency situations.

[0083] Example 4

[0084] The automatic report generation module comprises five subsystems: a report template library, a data aggregation engine, an intelligent template engine, an intelligent analysis engine, and a report distribution system.

[0085] The report template library adopts a template + data design concept, separating the report's structure, style, and data content. The library includes a variety of pre-built standard report templates, including:

[0086] Progress report template: Includes project overview, comparison of planned and actual progress, completion status of key milestones, progress deviation analysis, resource input status, project schedule risk warning and improvement suggestions, etc.

[0087] Quality report template: Includes an overview of quality inspection, pass rate statistics, distribution of quality problems, classification of quality problems, closed-loop status of quality problems, quality risk warning and improvement measures, etc.

[0088] Safety report template: Includes safety overview, safety accident statistics, safety violation statistics, safety hazard investigation status, safety risk point identification, safety early warning information and improvement measures, etc.

[0089] Cost report template: Includes cost overview, comparison of budget and actual costs, cost deviation analysis, cost composition analysis, cost risk warning and cost control suggestions.

[0090] Comprehensive Management Report Template: Provides an overall health assessment and comprehensive recommendations for the project by integrating data from multiple dimensions such as schedule, quality, safety, cost, and resources.

[0091] Each report template consists of the following elements:

[0092] Template basic information: template name, template type, applicable scenarios, creator, creation time, etc.;

[0093] Template structure: The chapter structure of the report, including title, abstract, main body, conclusion, etc.;

[0094] Data field definition: The name, type, data source, calculation rules, etc. of each data field;

[0095] Chart definition: chart type (line chart, bar chart, pie chart, etc.), data binding, style configuration, etc.;

[0096] Style configuration: style parameters such as font, color, margin, and background;

[0097] Output rules: output format (Word, PDF, Excel, HTML, etc.), output path, push method, etc.

[0098] The report template library supports user-defined template configurations. Users can create and modify templates through a drag-and-drop interface, and the system automatically parses the template configurations and generates corresponding reports.

[0099] The data aggregation engine is responsible for extracting the data required for the report from the data platform and intelligent decision analysis module. The data aggregation engine uses a configurable approach, automatically generating data query statements based on the data fields defined in the report template, and extracting data from the corresponding data sources. The data aggregation engine supports the following data sources:

[0100] Digital twin platform: Extracts real-time and historical data, including personnel data, equipment data, environmental data, and regional data.

[0101] Intelligent task scheduling subsystem: extracts task data, shift data, resource data, etc.;

[0102] Risk prediction and early warning subsystem: extracts risk early warning data, risk assessment data, etc.;

[0103] Safety and quality monitoring subsystem: Extracts data on safety hazards, quality defects, etc.

[0104] External systems: Extract data from BIM systems, ERP systems, SCADA systems, financial systems, etc. via API interfaces.

[0105] The data aggregation engine supports two data update modes: incremental update and full update. Incremental update mode only extracts data that has changed since the last update, reducing data transfer volume. Full update mode extracts all data, ensuring data integrity. The data aggregation engine also supports a data caching mechanism, caching frequently accessed data in memory to improve query efficiency.

[0106] The intelligent analytics engine performs statistical, trend, and comparative analyses on aggregated data to generate data insights and recommendations. The intelligent analytics engine provides the following analytical functions:

[0107] Statistical analysis: Calculate statistical indicators such as sum, mean, maximum, minimum, median, and standard deviation;

[0108] Trend analysis: Analyze the time series changes of data to identify trends such as upward, downward, and stable.

[0109] Comparative analysis: Conduct year-on-year analysis, month-on-month analysis, and comparison between planned and actual results to identify deviations;

[0110] Anomaly detection: Statistical and machine learning methods are used to identify outliers in the data;

[0111] Predictive analytics: Predicting future trends and risks based on historical data;

[0112] Correlation analysis: Analyze the relationships between different indicators and identify influencing factors.

[0113] The intelligent analysis engine employs configurable analysis rules, which users can define through a visual interface. The system automatically executes the analysis and generates results, including data tables, charts, and text descriptions.

[0114] The intelligent template engine populates the report template with the analysis results generated by the intelligent analysis engine, producing a standardized report document. The workflow of the intelligent template engine is as follows:

[0115] Parse the report template configuration to obtain the template structure, data fields, chart definitions, and style configuration;

[0116] Call the data aggregation engine to extract the corresponding data based on the data field definitions;

[0117] The intelligent analysis engine is invoked to analyze the extracted data and generate analysis results;

[0118] Generate corresponding charts based on the chart definition, including line charts, bar charts, pie charts, etc.

[0119] Based on the template structure and style configuration, assemble the data, charts, text descriptions, and other content into a report document;

[0120] According to the output rules, the report document will be output in the specified format, including Word, PDF, Excel, HTML, etc.

[0121] The intelligent template engine supports dynamic content generation, which can dynamically generate text descriptions based on data and analysis results. For example, it can automatically generate a suggestion such as "5 days behind schedule, it is recommended to increase the number of construction teams" based on schedule deviations. The intelligent template engine also supports conditional rendering, which can dynamically show or hide parts of the content based on data values ​​or conditions.

[0122] The report distribution system is responsible for pushing the generated reports to relevant management personnel. The report distribution system supports the following push methods:

[0123] Email push: Send the report as an email attachment or in the email body to the specified email address;

[0124] Enterprise WeChat push: Push the report to enterprise WeChat groups or individuals through the enterprise WeChat application interface;

[0125] DingTalk Push: Push reports to DingTalk groups or individuals via the DingTalk application interface;

[0126] Mobile App Push: Push the report to the mobile app via push service;

[0127] File system push: Saves the report file to the specified file system path.

[0128] The report distribution system supports two modes: on-demand generation and scheduled generation. On-demand generation is triggered by the user and is suitable for ad-hoc reporting needs. Scheduled generation automatically generates reports according to a preset cycle (daily, weekly, monthly, etc.) and is suitable for regular reporting needs. The system also supports configured push rules, allowing reports to be automatically pushed to appropriate personnel based on report type, content, and recipient role.

[0129] The automatic report generation module enables standardized and automated report generation, reducing the traditional report generation process that required multiple people and days to complete to just a few minutes. This significantly improves report standardization, ensures data accuracy, and substantially enhances the efficiency of wind power construction management.

[0130] Example 5

[0131] The intelligent decision analysis module includes an intelligent task scheduling subsystem, a risk prediction and early warning subsystem, and a safety and quality monitoring subsystem.

[0132] The intelligent task scheduling subsystem is responsible for task allocation and resource optimization scheduling, automatically generating shift schedules and work assignment plans. The workflow of the intelligent task scheduling subsystem is as follows:

[0133] Data Acquisition: Collect basic data such as task data, personnel data, equipment data, and environmental data;

[0134] Task data includes: task name, task type, task quantity, execution date, construction area, and prerequisite dependencies.

[0135] Personnel data: Personnel ID, name, skills, qualifications, attendance, historical performance, etc.;

[0136] Device data: Device ID, name, type, status, location, performance parameters, etc.;

[0137] Environmental data: weather conditions, wind speed, temperature, visibility, etc.

[0138] Data preprocessing: Performing data cleaning, data standardization, feature extraction, and other processing on the collected raw data;

[0139] Data cleaning: handling missing values ​​and outliers, filling in or deleting invalid data;

[0140] Data standardization: Converting data from different sources and in different formats into a unified format;

[0141] Feature extraction: Extract features such as task complexity, personnel skill matching degree, and equipment utilization rate.

[0142] Task priority calculation: The priority of each task is calculated by comprehensively considering factors such as urgency, importance, and dependencies.

[0143] Intelligent scheduling algorithm: It uses a deep learning model to predict task completion time and a random forest model to perform optimal matching of personnel, tasks and resources.

[0144] Deep learning model: Employing an LSTM (Long Short-Term Memory) neural network model, the model takes task features, personnel features, equipment features, and environmental features as input and outputs a predicted task completion time. Trained using historical project data, the model can accurately predict task completion times under various conditions.

[0145] Random Forest Model: Input personnel characteristics, equipment characteristics, and task characteristics; output a personnel-task-resource matching score. The model is trained using historical scheduling and performance data and can intelligently recommend the optimal combination of personnel, equipment, and tasks.

[0146] Scheduling and assignment plan generation: Automatically generate scheduling and assignment plans based on task priority, task completion time prediction, and resource matching score;

[0147] Shift scheduling plan: Daily staff attendance arrangements, including staff members, attendance time, and work tasks;

[0148] Work assignment plan: personnel allocation, equipment allocation, and time arrangement for each task.

[0149] Scheme evaluation and optimization: The generated scheduling and assignment schemes are evaluated, and indicators such as resource utilization, personnel load balance, and task completion timeliness are calculated. Genetic algorithms or simulated annealing algorithms are used for optimization and iteration to generate the optimal scheme.

[0150] Solution Implementation and Feedback: The optimized solution is distributed to a mobile app, where construction personnel receive task instructions. Task execution is tracked in real time, and feedback data is collected for continuous model optimization.

[0151] The intelligent task scheduling subsystem achieves intelligent scheduling through AI algorithms. Compared with the traditional manual scheduling method, it can improve construction efficiency by 20%-30%, reduce personnel idle rate by 15%-25%, and improve task completion timeliness by 10%-15%.

[0152] The risk prediction and early warning subsystem is responsible for predicting risks of project delays, equipment failures, and extreme weather, and issuing early warnings. The workflow of the risk prediction and early warning subsystem is as follows:

[0153] Data collection: Collect multi-dimensional data including project data, weather data, equipment data, and personnel data;

[0154] Project data: schedule data, cost data, quality data, change data, etc.;

[0155] Weather data: weather forecast data, historical meteorological data, real-time meteorological data, etc.;

[0156] Equipment data: equipment operation data, equipment maintenance data, equipment fault data, etc.;

[0157] Personnel data: personnel qualification data, personnel performance data, personnel attendance data, etc.

[0158] Feature engineering: extracting and selecting features from the collected raw data to construct a feature set for risk prediction;

[0159] Schedule characteristics: deviation between planned and actual schedule, critical path delays, resource availability, etc.

[0160] Cost characteristics: deviation between budgeted and actual costs, cost trends, resource price fluctuations, etc.

[0161] Quality characteristics: quality pass rate, number of quality problems, and rectification status;

[0162] Weather characteristics: weather forecast data, historical meteorological data, frequency of extreme weather events, etc.;

[0163] Equipment characteristics: equipment operating status, equipment health, equipment maintenance records, etc.;

[0164] Personnel characteristics: personnel qualifications, personnel experience, personnel training, etc.

[0165] Risk prediction model: A multi-dimensional risk prediction model is constructed using an LSTM neural network model;

[0166] Project delay risk prediction model: Input schedule characteristics, weather characteristics, equipment characteristics, and personnel characteristics, output project delay probability and delay days prediction;

[0167] Equipment failure risk prediction model: Input equipment characteristics, operating characteristics, and maintenance characteristics; Output equipment failure probability and failure time prediction.

[0168] Extreme weather risk prediction model: Input historical meteorological data and weather forecast data, output the probability of extreme weather occurrence and prediction of occurrence time.

[0169] Risk assessment: Construct a multi-dimensional risk assessment matrix, comprehensively considering risks such as project delays, equipment failures, extreme weather, and other risk factors, to calculate the overall risk level and key risk points of the project.

[0170] Early warning rule configuration: Configure early warning thresholds and rules for different risk levels;

[0171] High risk: Issue a red alert immediately and activate the emergency response mechanism;

[0172] Medium risk: An orange alert has been issued, and monitoring and prevention measures have been strengthened;

[0173] Low risk: A yellow alert has been issued; continued monitoring and tracking are underway.

[0174] Early warning information generation: Based on risk prediction results and early warning rules, early warning information is generated, including risk type, risk level, occurrence time, scope of impact, and response suggestions.

[0175] Warning information push: Warning information is pushed to relevant personnel through multiple channels, including email, WeChat Work, DingTalk, and mobile APP, to ensure that warning information is delivered in a timely manner.

[0176] Early warning tracking and closed-loop management: Track the processing of early warning information, record response measures and processing results, and form a closed-loop management of early warning processing.

[0177] The risk prediction and early warning subsystem uses AI algorithms to predict risks. Compared with traditional manual risk assessment methods, it can provide early warnings of project delay risks and equipment failure risks 7-15 days in advance, with an accuracy rate of 85%-90%. This allows project managers sufficient time to respond and significantly reduces the losses caused by risks.

[0178] The safety and quality monitoring subsystem is responsible for intelligent identification of safety hazards and quality defects, achieving 24 / 7 safety and quality monitoring. The workflow of the safety and quality monitoring subsystem is as follows:

[0179] Video data acquisition: Real-time video stream data is acquired through high-definition cameras deployed at the construction site, supporting 4K high-definition image quality and a frame rate of up to 25 frames per second;

[0180] Image preprocessing: Preprocessing the acquired raw video images, including image noise reduction, image enhancement, image cropping, image scaling, etc., to improve image quality;

[0181] Object detection: The YOLO (You Only Look Once) object detection algorithm is used to detect objects in video frames and identify people and objects, including people, safety helmets, safety belts, steel bars, formwork, concrete, etc.

[0182] Behavior recognition: The 3D-CNN (3D Convolutional Neural Networks) behavior recognition algorithm is used to analyze continuous video frames and identify personnel behavior, including whether they are wearing safety helmets, whether they are fastening seat belts, whether they have entered dangerous areas, and whether they have violated regulations.

[0183] Defect identification: Image segmentation and image classification algorithms are used to identify quality defects, including whether the spacing of the reinforcing bars is up to standard, whether the formwork support is up to standard, and whether there are cracks in the concrete appearance.

[0184] Risk level assessment: The risk level is assessed based on the identification results. The risk level is divided into three levels: high risk, medium risk, and low risk.

[0185] High-risk activities include working at heights without wearing safety belts and unauthorized entry into lifting restricted areas, which must be stopped immediately.

[0186] Medium risk: Not wearing a safety helmet, lack of edge protection, etc., requires reminders and rectification;

[0187] Low risk: Minor violations that require attention and correction.

[0188] Alarm Trigger: Trigger the corresponding alarm based on the risk level;

[0189] High risk: Immediately triggers audible and visual alarms, and simultaneously pushes alarm information to the administrator's mobile phone;

[0190] Medium risk: Alert information will be pushed to the mobile devices of administrators to remind them to pay attention and handle the situation;

[0191] Low risk: Record alarm information and incorporate it into daily management.

[0192] Alarm push notifications: Alarm information is pushed to relevant personnel through multiple channels, including mobile apps, WeChat Work, DingTalk, and email.

[0193] Alarm handling: After receiving alarm information, managers can view alarm details, retrieve on-site video, issue rectification instructions, and track rectification progress, thus achieving closed-loop management of alarm handling.

[0194] Model optimization: Collect alarm confirmation results and rectification feedback data to continuously optimize and train the model and improve recognition accuracy.

[0195] Example 6

[0196] The system integration and collaboration module is responsible for data interface with external systems, enabling cross-system data sharing and collaborative work, and connecting the information flow of all stages of design, procurement, construction, and acceptance.

[0197] The system integration and collaboration module adopts a microservice architecture, comprising five parts: API gateway, interface adapter, data synchronization engine, message middleware, and collaboration workbench.

[0198] The API gateway serves as the unified entry point for system integration and collaboration modules. It is responsible for receiving requests from external systems and performing tasks such as routing, load balancing, authentication, permission verification, and traffic control. The API gateway supports multiple interface protocols, including RESTful APIs, WebSockets, and GraphQL, allowing for flexible integration with different types of systems.

[0199] An interface adapter is responsible for converting the interfaces and data formats of external systems into a unified format that the internal system can process. For different external systems, the interface adapter provides corresponding adapter implementations:

[0200] BIM System Adapter: Connects to BIM software such as Revit, Bentley, and ArchiCAD, supports importing and exporting BIM model files in formats such as IFC and RVT, and enables real-time synchronization of design drawings and 3D models with on-site construction data.

[0201] ERP System Adapter: Connects to ERP systems such as SAP, Oracle, Kingdee, and Yonyou, supporting real-time integration of purchase orders, inventory data, cost data, and financial data to achieve dynamic control over procurement, inventory, cost, and finance.

[0202] SCADA system adapter: Interacts with various SCADA (Supervisory Control And Data Acquisition) systems, supports real-time acquisition of equipment operation data, equipment status data, fault alarm data, etc., and enables equipment health assessment and predictive maintenance;

[0203] Financial System Adapter: Connects to various financial software, supports real-time synchronization of budget data, cost data, expense data, invoice data, etc., and realizes the association between financial data and engineering data;

[0204] Attendance System Adapter: Connects to various attendance systems, supports real-time collection of personnel attendance data, overtime data, leave data, etc., and enables personnel work hour statistics and performance evaluation;

[0205] Other system adapters: Supports integration with other business systems such as CRM systems, HR systems, and contract management systems to achieve data sharing and business collaboration;

[0206] The data synchronization engine is responsible for synchronizing data between internal and external systems, supporting both bidirectional and unidirectional synchronization modes. In bidirectional synchronization mode, data flows between the internal and external systems, maintaining data consistency. In unidirectional synchronization mode, data flows only from the external system to the internal system, or only from the internal system to the external system. The data synchronization engine supports incremental and full synchronization strategies. Incremental synchronization only synchronizes changed data, while full synchronization synchronizes all data. The data synchronization engine also supports conflict detection and resolution strategies. When the same data is modified simultaneously in multiple systems, conflicts are automatically detected and resolved according to preset rules.

[0207] Message middleware is responsible for implementing asynchronous communication and event-driven architecture between systems. It supports various message passing modes, including publish / subscribe and point-to-point. When an event occurs in one system (such as task completion, quality failure, or cost overrun), the event is published through the message middleware, and other systems subscribed to that event can receive timely notifications and respond. Message middleware supports advanced features such as message persistence, message retries, and message routing to ensure reliable message delivery.

[0208] The collaborative workbench provides a unified work platform that integrates the functions and data of various external systems, supporting users to complete cross-system collaborative operations within a single interface. The collaborative workbench includes the following functions:

[0209] Data dashboard: Integrates key data metrics from various systems, providing multi-dimensional data display and analysis;

[0210] Workflow: Supports cross-system workflow configuration and execution, such as design change workflow, quality rectification workflow, cost approval workflow, etc.

[0211] Document Management: Integrates documents from various systems, providing unified document management and retrieval functions;

[0212] Message Center: Integrates messages and notifications from various systems, providing unified message viewing and processing functions;

[0213] Collaborative editing: Supports multiple users editing documents online simultaneously, such as construction plans and technical briefings;

[0214] Video conferencing: Integrates video conferencing functionality, supporting remote communication and collaboration.

[0215] The system integration and collaboration module, through standardized interfaces and a flexible architecture, achieves deep integration with external systems, breaks down information silos, enables cross-system data sharing and collaborative work, and significantly improves management efficiency and decision-making level.

[0216] Example 7

[0217] The continuous learning and optimization module is responsible for collecting historical project data, expert experience, and user feedback. It uses reinforcement learning algorithms to continuously optimize the intelligent scheduling algorithm, risk prediction model, and safety and quality identification algorithm, thereby improving the system's intelligence level.

[0218] The continuous learning and optimization module consists of five parts: data acquisition, knowledge base, model training, model evaluation, and model deployment.

[0219] The data acquisition module is responsible for collecting various types of data for model training and optimization, including:

[0220] Historical project data: Collect basic project data, schedule data, cost data, quality data, safety data, etc. from completed wind power projects to build a project dataset;

[0221] Real-time operational data: Real-time collection of operational data from the digital twin platform and intelligent decision analysis module, including personnel data, equipment data, environmental data, task data, risk data, safety and quality data, to construct an operational dataset;

[0222] Expert experience data: Collect the experience and knowledge of project management experts, technical experts, and security experts, including rules of thumb, decision-making cases, solutions, etc., to build an expert dataset;

[0223] User feedback data: Collect user feedback and evaluations on system functions, report quality, scheduling schemes, early warning information, etc., and build a feedback dataset.

[0224] The data acquisition module employs a combination of automated collection and manual input. Automated collection periodically extracts data from various systems and modules through data pipelines. Manual input allows experts and users to fill in their experiences and feedback through a user-friendly interface. The data acquisition module also supports data processing functions such as data cleaning, data annotation, and data augmentation to improve data quality.

[0225] The knowledge base module is responsible for storing and managing knowledge, including explicit and tacit knowledge. Explicit knowledge refers to knowledge that can be clearly expressed and recorded, such as standards, technical solutions, and operation manuals. Tacit knowledge refers to experiential knowledge that is difficult to express explicitly but exists in the minds of experts, such as intuitive judgments and rules of thumb. The knowledge base module includes:

[0226] Rule knowledge base: Stores various rule knowledge, including management rules, technical rules, security rules, quality rules, etc., and uses a rule engine to realize rule reasoning and execution;

[0227] Case Knowledge Base: Stores various types of case knowledge, including successful cases, failed cases, typical problems, solutions, etc., and uses case reasoning methods to achieve case retrieval and reuse;

[0228] Ontology Knowledge Base: Constructs an ontology model for the wind power construction field, describing domain concepts, concept attributes, and relationships between concepts, and supporting structured representation and intelligent retrieval of knowledge;

[0229] Data knowledge base: Stores various types of data, including standard data, reference data, statistical data, etc., and supports data querying and visualization;

[0230] Model knowledge base: Stores various machine learning models and deep learning models, including model parameters, model versions, model performance, etc., and supports model version management and performance tracking.

[0231] The knowledge base module supports the construction of knowledge graphs, representing structured knowledge in the form of a graph. Nodes in the knowledge graph represent entities (such as personnel, equipment, tasks, risks, etc.), and edges represent relationships between entities (such as personnel-task relationships, equipment-risk relationships, etc.). Knowledge graphs support knowledge reasoning and knowledge discovery, and can uncover implicit knowledge relationships.

[0232] The model training module is responsible for training and optimizing various machine learning models and deep learning models, including:

[0233] Intelligent scheduling model: It adopts deep learning model and random forest model, takes task characteristics, personnel characteristics, equipment characteristics and environmental characteristics as input, and outputs task completion time prediction results and resource matching degree score, which are used to optimize task scheduling and resource allocation;

[0234] Risk prediction model: The model adopts an LSTM neural network model. It takes schedule characteristics, cost characteristics, quality characteristics, weather characteristics, equipment characteristics, and personnel characteristics as input, and outputs the prediction results of project delay risk, equipment failure risk, and extreme weather risk for risk warning and prevention.

[0235] Safety and quality identification model: It adopts target detection algorithm, behavior recognition algorithm, and image classification algorithm. It takes video image data as input and outputs the identification results of safety hazards and quality defects for safety and quality monitoring.

[0236] Image generation model: Generative adversarial networks (GANs) or other generative models are used to generate simulated images of construction scenes to expand the training dataset and simulate construction scenes;

[0237] Reinforcement learning models: Reinforcement learning algorithms such as Q-Learning, DQN (Double Q-Network), and PPO (Proximal Policy Optimization) are used to train agents to make optimal decisions, such as optimal scheduling strategies, optimal resource allocation strategies, and optimal risk response strategies.

[0238] The model training module supports multiple training modes, including offline training, online training, incremental training, and transfer learning. Offline training uses historical data to train the model offline. Online training uses real-time data to update the model online. Incremental training uses newly added data to incrementally update the model, avoiding retraining on all data. Transfer learning transfers models trained in other domains or projects to the current domain or project, accelerating model training.

[0239] The model evaluation module is responsible for evaluating model performance, including metrics such as accuracy, precision, recall, F1 score, and AUC. It supports methods such as cross-validation and A / B testing to objectively assess model performance. The module also supports model interpretability analysis, explaining the model's decision-making process and enhancing its credibility.

[0240] The model deployment module is responsible for deploying the trained model to the production environment, providing model services. It supports multiple deployment methods, including local deployment, cloud deployment, and edge deployment. The module also supports model version management, canary releases, and model rollback functions to ensure the security and stability of model deployment.

[0241] The continuous learning and optimization module collects data and feedback, and uses reinforcement learning algorithms to continuously optimize various models, enabling the system to learn and evolve, improving its intelligence and adaptability. As the system is used for longer periods, the model's prediction accuracy and decision-making level will continuously improve, and the scheduling schemes, risk warnings, and safety and quality monitoring provided by the system will become increasingly precise and intelligent, truly achieving "the more you use it, the smarter it gets."

[0242] Example 8

[0243] The mobile application module is responsible for providing mobile application services. Construction workers and managers can use mobile devices to perform operations such as data reporting, task reception, status viewing, and decision approval, thereby realizing mobile construction management and collaboration.

[0244] The mobile application module employs a combination of native and hybrid development approaches to develop applications for both iOS and Android platforms. The mobile application includes the following main functional modules:

[0245] User authentication module: Supports multiple login methods such as mobile phone number login, account and password login, QR code login, and single sign-on. It also supports biometric login (fingerprint login and face login) and provides secure identity authentication and permission management.

[0246] Workbench Module: Provides a personalized workbench for users with different roles, displaying to-do items, message notifications, data dashboards, quick access and other information, allowing users to understand their current work status at a glance;

[0247] Task Management Module: Construction workers can view assigned tasks, including task name, task description, construction location, construction time, precautions, etc. They can accept, start, pause, and complete tasks, and report task progress and problems. Managers can view the status of all tasks, and can assign and adjust tasks.

[0248] Data reporting module: Construction personnel can report various types of data via mobile devices, including progress data, quality data, safety data, etc. They can also upload on-site photos and videos, support offline reporting, store data locally when the network is poor, and automatically synchronize when the network is restored;

[0249] Early warning push module: Receives push notifications for risk warnings and safety and quality alarms, including risk type, risk level, occurrence time, scope of impact, and response suggestions, and supports multiple reminder methods such as sound reminder, vibration reminder, and pop-up reminder;

[0250] On-site monitoring module: It can view real-time video monitoring of the construction site, supports simultaneous viewing of multiple video streams, supports PTZ control (direction control, focus control), and supports video playback and screenshots;

[0251] Report viewing module: Allows users to view various reports, including progress reports, quality reports, safety reports, cost reports, etc., supporting online viewing and downloading, as well as secondary editing and annotation of reports;

[0252] Collaborative Communication Module: Supports instant messaging communication, one-on-one chat, group chat, sending text, voice, pictures, and files, supports video conferencing, and enables remote collaboration and expert guidance;

[0253] Map navigation module: Based on GIS map, it displays the layout of the construction site, personnel location, equipment location, construction area, etc., and allows users to view personnel and equipment tracks, as well as perform route planning and navigation;

[0254] Knowledge Base Module: Provides online access to standards, technical solutions, operation manuals, training videos, and other knowledge, supporting full-text search and categorized browsing.

[0255] The mobile application module adopts a front-end / back-end separation architecture. The front-end is developed using the React Native or Flutter framework, while the back-end provides RESTful API interfaces. The mobile application module uses an MVC (Model-View-Controller) architecture, divided into a model layer, a view layer, and a controller layer. The model layer is responsible for data storage and management, the view layer is responsible for UI display, and the controller layer is responsible for handling business logic.

[0256] The mobile application module adopts an offline-first design philosophy, supporting both offline use and online synchronization. In offline mode, the mobile app can be used in environments without a network connection, with data stored in a local database. In online synchronization mode, the mobile app automatically synchronizes data with the server when a network connection is available, ensuring data consistency.

[0257] The mobile application module uses push notification technology, leveraging third-party push services such as JPush, Getui, and Xiaomi Push to deliver real-time messages. Push notifications support rich media content, displaying images, videos, buttons, and other rich information.

[0258] The mobile application module employs data encryption and security hardening technologies to ensure data and application security. Data transmission uses the HTTPS encryption protocol, and sensitive data is stored using the AES encryption algorithm. Application hardening utilizes technologies such as code obfuscation, decompilation protection, and anti-tampering to prevent the application from being cracked and tampered with.

[0259] The mobile application module provides mobile application services, enabling construction and management personnel to work anytime, anywhere, breaking the limitations of time and space and improving management and collaboration efficiency. Construction personnel can report data and receive tasks in a timely manner, while managers can monitor the site situation in real time and make decisions, truly realizing the vision of "wind power in your pocket".

[0260] This invention, through the deep integration of IoT, AI, and big data technologies, constructs a systematic intelligent management and automatic report generation solution for wind power construction. It realizes the transformation of wind power construction from "experience-driven" to "data-driven," providing strong support for the efficient, safe, and high-quality construction of wind power projects. This invention is applicable not only to onshore wind power projects but also to offshore wind power projects, possessing broad applicability and significant potential for widespread adoption.

[0261] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for intelligent management and automatic report generation of wind power construction tasks, characterized in that, Includes the following steps: S1: Construct an IoT data collection system for wind power construction, deploy multi-source sensing devices at the construction site, including smart safety helmets, positioning wristbands, torque sensors, vibration sensors, temperature sensors, anemometers, rain gauges, high-definition cameras, infrared detectors, and electronic fences, to collect real-time location data, behavior data, qualification data of construction personnel, operating parameter data of hoisting equipment, vibration and temperature data of mechanical equipment, environmental data, and area monitoring data. S2: Through a multi-network fusion transmission scheme of 4G / 5G network, LoRa wireless communication, satellite communication and microwave transmission, combined with edge computing technology, the collected data is preprocessed and aggregated to the cloud platform to build a digital twin of the wind power construction site, realizing real-time mapping between physical space and digital space; S3: Construct an intelligent decision analysis module based on AI algorithms, including an intelligent task scheduling subsystem, a risk prediction and early warning subsystem, and a safety and quality monitoring subsystem. The intelligent task scheduling subsystem uses deep learning models and random forest models for task allocation and resource optimization scheduling based on collected real-time data and historical project data. The risk prediction and early warning subsystem uses an LSTM neural network model to predict the risks of project delays, equipment failures, and extreme weather, issuing early warnings 7-15 days in advance. The safety and quality monitoring subsystem uses AI image recognition algorithms to analyze camera video streams and identify safety hazards and quality defects. S4: Construct an automatic report generation module, including a report template library, a data aggregation engine, an intelligent template engine, an intelligent analysis engine, and a report distribution system. The report template library has pre-set standardized templates for progress reports, quality reports, safety reports, cost reports, and comprehensive management reports, and supports custom template configurations. The data aggregation engine extracts structured data from the digital twin platform and the intelligent decision analysis module. The intelligent analysis engine performs statistical analysis, trend analysis, and comparative analysis on the aggregated data to generate data insights and suggestions. The intelligent template engine populates the analysis results into the template to generate a standardized report document; the report distribution system pushes the report to relevant management personnel through multiple channels. S5: Construct a system integration and collaboration module, providing standard API interfaces for data connection with BIM systems, ERP systems, SCADA systems, financial systems and attendance systems, to achieve cross-system data sharing and collaborative work, and to streamline the information flow of design, procurement, construction and acceptance. S6: Build a continuous learning and optimization module to collect historical project data, expert experience and user feedback, and use reinforcement learning algorithms to continuously optimize the intelligent scheduling algorithm, risk prediction model and safety and quality identification algorithm to improve the system's intelligence level.

2. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The multi-source sensing device deployment strategy in step S1 includes: having personnel wear smart safety helmets and positioning wristbands at the construction site, deploying torque sensors and high-definition cameras on hoisting equipment, setting up anemometers, rain gauges and slope displacement gauges in key construction areas, and installing infrared detectors and electronic fences at the boundaries of the area, forming a comprehensive sensing network for personnel monitoring, equipment monitoring, environmental monitoring and area monitoring.

3. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The edge computing technology in step S2 includes deploying an industrial-grade edge computing gateway on-site to perform data cleaning, data filtering, feature extraction, and data compression on the collected raw data, filtering redundant information, reducing transmission pressure, and achieving low-latency transmission of key data.

4. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The intelligent task scheduling subsystem in step S3 uses a deep learning model to predict task completion time, and a random forest model to perform optimal matching of personnel, tasks, and resources, automatically generating shift and work assignment schemes to avoid personnel being idle or overworked.

5. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The risk prediction and early warning subsystem in step S3 integrates weather forecast data, equipment operation data, construction progress data, and personnel qualification data to construct a multi-dimensional risk assessment matrix. When a risk of construction delay is predicted, it automatically proposes resource allocation suggestions, and when a safety risk is predicted, it automatically pushes protective measure suggestions.

6. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The automatic report generation module in step S4 supports two modes: on-demand generation and scheduled generation. The report content covers construction progress, quality status, safety status, cost status, resource status, risk warnings and recommended measures. The report data is updated synchronously with the real-time on-site data to achieve dynamic report generation.

7. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The report template library in step S4 supports user-defined template configuration, including template structure, data fields, chart types, style formats and output rules. Users can flexibly adjust the report template according to project characteristics and management needs to achieve personalized report customization.

8. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The system integration and collaboration module in step S5 connects with external systems through RESTful API interfaces, WebSocket interfaces, and message queue middleware, supporting multiple interaction modes such as real-time data push, historical data query, data subscription, and event triggering.

9. The method for intelligent management and automatic report generation of wind power construction tasks according to claim 1, characterized in that, The method also includes a mobile application module, providing a mobile APP or mini-program. Construction personnel can report progress data, upload site photos, receive task instructions, and view early warning information through the mobile terminal. Management personnel can view the construction status, approve reports, and issue decision instructions in real time through the mobile terminal, realizing mobile construction management and collaboration.

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