Green building construction equipment automatic collaborative control system

Abstract

The application is particularly an automatic collaborative control system based on green building construction equipment, and relates to the technical field of intelligent construction and green construction, comprising: a multi-source sensing and data acquisition module; a network transmission and edge computing module; a collaborative decision and intelligent scheduling module; an equipment execution and automatic control module; and a green energy efficiency and safety management and control module.In the application, a data flow conversion link is constructed, in which multi-source sensing and data acquisition, network transmission and edge computing, collaborative decision and intelligent scheduling, and equipment execution and automatic control are sequentially connected, and a green energy efficiency and safety management and control module is matched, so as to form a closed-loop management and control system covering the whole operation process of construction equipment and break the information island among construction equipment of different categories and different manufacturers.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction and green construction technology, and in particular to an automatic collaborative control system for green building construction equipment. Background Technology

[0002] With the continuous advancement of green building concepts and the rapid development of intelligent construction technologies, intelligent management and green upgrading of the construction process have become important directions for industry development. As a core production factor in construction, the operating efficiency, energy consumption level, and collaborative operation capabilities of construction equipment directly affect the project's construction progress, construction costs, and the effectiveness of green construction. Traditional construction equipment often adopts a model of independent single-machine management and manual scheduling and coordination. The degree of information exchange between equipment is limited, and waiting gaps easily occur during process transitions. Furthermore, energy consumption and environmental management largely rely on manual inspections and post-event statistics, making it difficult to achieve real-time linkage with the equipment operation process. This is no longer adequate to meet the construction management needs of high-standard green building projects.

[0003] Existing construction equipment management and control systems mostly focus on the realization of single-dimensional functions and have not yet formed a complete closed-loop management and control system of "perception-transmission-decision-execution-optimization". The integration depth of multi-device automatic collaboration capabilities and green construction constraints is insufficient, and there are breakpoints in data flow, making it difficult to simultaneously improve construction efficiency and achieve green goals. Summary of the Invention

[0004] The purpose of this invention is to propose an automatic collaborative control system for green building construction equipment in order to solve the above-mentioned problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An automatic collaborative control system for green building construction equipment includes: The multi-source sensing and data acquisition module is used to collect various parameters of construction equipment such as cranes, earthmoving and construction robots, construction space and process progress, and green construction environment. After protocol conversion, outlier removal and structured preprocessing of multi-source heterogeneous data, it outputs standardized sensing data. The network transmission and edge computing module receives standardized sensing data, performs local data aggregation, low-latency control calculations, and offline data caching at the edge, and transmits the aggregated global sensing data upward to the decision layer and forwards control commands downward to the execution end. The collaborative decision-making and intelligent scheduling module receives global perception data, generates a global collaborative scheduling scheme and device-level operation instructions based on the digital twin base and multi-objective optimization algorithm, and sends them to the network transmission and edge computing module. The equipment execution and automatic control module receives work instructions, drives various types of construction equipment to complete single-machine automated operations and cross-equipment process collaborative linkage, and transmits execution status and work feedback data back in real time; The green energy efficiency and safety management module acquires operational data from each module, completes energy efficiency and carbon emission accounting, environmental linkage control and safety risk prevention and control, and outputs optimization strategies to the collaborative decision-making and intelligent scheduling module to form a closed-loop management and control system for green construction.

[0006] Preferably, the multi-source sensing and data acquisition module includes an equipment status sensing unit, a construction space progress sensing unit, a green environment sensing unit, and a data preprocessing unit; The equipment status sensing unit collects data on the operating conditions, energy consumption, and fault symptoms of various construction equipment; the construction space progress sensing unit collects data on the spatial distribution, component location, and process completion; the green environment sensing unit collects environmental parameters such as dust, noise, hydrology, and meteorology; and the data preprocessing unit completes the standardization conversion of heterogeneous data and data quality verification.

[0007] Preferably, the network transmission and edge computing module adopts a layered hybrid networking architecture, matching the corresponding communication methods for terminal devices with different speed and latency requirements, and constructing a stable communication network covering the entire construction site; two levels of computing nodes are set up on the edge side, which respectively undertake lightweight control on the end side and complex computing tasks at the site level, and have the ability to be autonomous when the network is disconnected and to retain data locally.

[0008] Preferably, the collaborative decision-making and intelligent scheduling module has a built-in digital twin base, which integrates building information model and real-time perception data to construct a digital mirror of the construction site, supports simulation and deduction of the construction process and synchronous mapping of virtual and real states, and provides visual decision support for collaborative scheduling.

[0009] Preferably, the collaborative decision-making and intelligent scheduling module adopts a scheduling mechanism that combines static global planning and dynamic rolling optimization. It takes into account multiple constraints such as construction period, space, process and green construction, generates multi-equipment operation sequence and path planning, and can trigger local rescheduling according to unexpected situations on site.

[0010] Preferably, the device executes the native control system of various construction equipment that interfaces with the automatic control module, and can issue control commands such as start / stop, speed, trajectory and operation parameters to the device to realize unmanned autonomous operation at the single-machine level, and has built-in safety limit protection logic.

[0011] Preferably, the equipment execution and automatic control module supports process-level collaborative linkage between equipment of different categories and manufacturers. Through unified clock synchronization and spatial information sharing, it realizes the matching of action rhythms of multiple equipment and dynamic spatial avoidance, eliminating waiting gaps between processes.

[0012] Preferably, the green energy efficiency and safety management module includes an energy efficiency and carbon emission management unit, which can statistically analyze energy and resource consumption from multiple dimensions such as equipment, process, and region, calculate carbon emissions during the construction phase, identify inefficient operation scenarios and generate energy-saving optimization strategies to support green construction evaluation and benchmarking.

[0013] Preferably, the green energy efficiency and safety management module also includes an environmental linkage control unit, which establishes a linkage management mechanism between environmental parameters and equipment operation, and automatically adjusts the equipment operation status and environmental protection facility operation strategy according to changes in environmental parameters such as dust, noise, and hydrology, so as to achieve proactive pollution prevention and control.

[0014] Preferably, the green energy efficiency and safety management module also has all-dimensional safety prevention and control and continuous optimization capabilities, which can realize equipment failure early warning, space collision avoidance and identification of unsafe human behavior, while accumulating operation data to iteratively optimize scheduling strategies and continuously improve system collaboration efficiency and green construction level.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention constructs a data flow link that sequentially connects multi-source sensing and data acquisition, network transmission and edge computing, collaborative decision-making and intelligent scheduling, and equipment execution and automatic control. Combined with a green energy efficiency and safety management module that links the entire domain, it forms a closed-loop management system covering the entire operation process of construction equipment, breaking down information silos between different types and manufacturers of construction equipment.

[0016] 2. This invention automatically breaks down work tasks based on the global construction schedule plan, matches suitable equipment resources with equipment capability attributes, and generates collaborative work schemes that conform to the process logic. This enables multi-equipment process-level linkage and cooperation, which helps reduce waiting gaps and equipment downtime during process connections. At the same time, relying on a dynamic scheduling mechanism, the work arrangements are adjusted in a timely manner according to changes in on-site working conditions, adapting to unexpected situations such as equipment status fluctuations and progress deviations, ensuring a smooth and orderly construction process. Attached Figure Description

[0017] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0018] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0019] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein. Example

[0020] Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0021] In this embodiment, it includes: Module 1: Multi-source Sensing and Data Acquisition Module This module serves as the system's overall data input terminal. Following a four-layer perception architecture of "equipment-space-environment-data," it achieves digital mapping of all construction elements. All collected data serves the dual goals of collaborative control and green construction, and is further subdivided into four core sub-units: 1.1 Construction Equipment All-Dimensional Status Sensing Unit Customized deployment of sensing terminals for different types of construction equipment enables comprehensive and high-frequency collection of operating conditions, energy consumption levels, and fault symptoms, forming the data foundation for equipment collaboration and energy efficiency optimization.

[0022] Status monitoring of lifting equipment: Torque sensors, lifting capacity transmitters, slewing absolute encoders, luffing distance sensors, tilt sensors, anemometers, and brake status monitoring terminals are deployed on tower cranes, construction hoists, crawler cranes, and other equipment to collect 12 types of safety and operating condition parameters in real time, including load size, operating radius, lifting height, slewing angle, operating speed, wind speed, and braking status. The sampling frequency is no less than 5Hz, with key safety parameters sampled at 20Hz, and the accuracy error ≤0.5%. Simultaneously, three-phase energy meters and power quality monitoring modules are installed to collect the energy consumption of the lifting, slewing, and luffing mechanisms, identifying inefficient operating conditions such as no-load and light-load conditions.

[0023] Earthmoving and Transportation Equipment Sensing: Engine ECU data reading terminals, fuel flow sensors, oil pressure sensors, oil temperature sensors, and GNSS high-precision positioning terminals are installed on fuel-powered equipment such as excavators, loaders, dump trucks, and concrete mixer trucks to collect parameters such as engine speed, instantaneous fuel consumption, cumulative fuel consumption, operating time, driving trajectory, and load. The positioning accuracy reaches sub-meter level, and the fuel consumption measurement accuracy is ≤1.5%, which can accurately calculate the fuel consumption and carbon emissions per unit volume of earthmoving and per unit transportation distance.

[0024] Sensing for construction robots and precision machining equipment: It interfaces with the internal motion controllers and servo systems of equipment such as concrete placing booms, vibratory compaction robots, rebar tying robots, and ground leveling robots to collect data such as motor torque, speed, power, working coordinates, working cycle time, and fault codes; at the same time, it is equipped with end effector force sensors and vision positioning modules to collect data such as working accuracy and material consumption, providing a basis for quality control and material loss optimization.

[0025] Comprehensive energy consumption metering: 0.5-level precision three-phase smart energy meters are installed in the main distribution box, zone distribution boxes, and individual equipment inlet terminals at the construction site to support peak-valley time-of-use metering; remote water meters with metering accuracy ≤2% are installed in the main water supply pipes, zone branch pipes, sprinkler systems, and rainwater harvesting systems; achieving four-level energy consumption metering of "main-zone-individual equipment-process", with automatic data upload, replacing manual meter reading and providing granular data support for energy efficiency analysis.

[0026] 1.2 Construction Space and Work Process Progress Sensing Unit By integrating four technologies—positioning, point cloud, vision, and model matching—it achieves spatial digitization and automated progress recognition at construction sites, solving the problems of untimely detection of spatial conflicts and progress delays in traditional construction.

[0027] High-precision positioning and sensing: UWB ultra-wideband positioning technology is adopted to deploy positioning base stations at the construction site with a base station spacing of ≤50m. Positioning tags are attached to mobile construction equipment, AGV transport vehicles, personnel, and key prefabricated components to achieve centimeter-level real-time positioning with a position refresh frequency of ≥10Hz. It supports functions such as electronic fences, trajectory playback, and proximity warning, providing spatial data for equipment collision prevention and personnel safety management.

[0028] 3D spatial point cloud perception: Multi-line lidar is deployed at high locations such as tower cranes and construction elevators, and combined with mobile handheld scanning terminals to periodically scan the work surface with point cloud density ≥100 points / ㎡; real-time 3D model of the construction site is generated by splicing the point cloud, automatically identifying the distribution of obstacles, the outline of constructed components, and the location and volume of material stacking, providing spatial basis for equipment path planning and engineering quantity calculation.

[0029] AI visual process recognition: High-definition AI smart cameras are deployed at the work surface, construction site entrances and exits, and material storage areas. Using target detection and instance segmentation algorithms, the cameras automatically identify the type of construction process, the number of workers, the amount of materials entering the site, and the amount of construction waste. They can automatically determine the completion status of processes such as rebar tying, formwork erection, and concrete pouring, replacing manual on-site statistics and increasing the progress data update frequency to the hourly level.

[0030] BIM Progress Comparison and Awareness: Automatically registers the site point cloud model, visual recognition data, and BIM design model using the ICP point cloud registration algorithm, achieving a registration accuracy of ≤5mm; automatically compares the geometric deviations between completed components and the design model, calculates the completion rate of sub-items, identifies deviations that are ahead or behind schedule, and achieves deviation identification accuracy at the component level, providing data input for progress scheduling.

[0031] 1.3 Multi-parameter sensing unit for green construction environment Strictly adhering to the indicator requirements of the "Green Construction Evaluation Standard" GB / T50640, we will construct a comprehensive environmental monitoring network to achieve continuous and automatic collection of environmental data, providing a basis for judgment in environmental linkage control.

[0032] Atmospheric and dust monitoring: Environmental monitoring stations are set up in earthwork operation areas, construction site entrances and exits, material storage areas, and office and living areas, with a minimum of one station per 5,000 square meters; PM2.5, PM10, and TSP (total suspended particulate matter) are collected simultaneously, and CO and NOx concentrations are also monitored; data is collected once per minute with a monitoring accuracy of ±10%, and seamless integration with the local housing and construction department's dust monitoring platform is supported.

[0033] Noise monitoring: Noise monitoring terminals are deployed at the construction site boundary and near residential areas. The frequency weighting complies with the requirements of GB3096 standard. The measurement range is 30~130dB and the accuracy is ±1.5dB. Continuous monitoring is carried out in two time periods, day and night. The equivalent continuous A-weighted sound level is automatically calculated, and the time period of noise exceeding the standard and the corresponding construction equipment are identified, providing accurate location for noise control.

[0034] Hydrological and water resource monitoring: Install flow, level, and water quality sensors at municipal water supply inlets, rainwater harvesting ponds, sprinkler systems, construction dewatering wells, and sewage discharge outlets; monitor water consumption, recycled water consumption, drainage volume, rainwater collection volume, as well as indicators such as sewage pH value and suspended solids concentration, calculate water resource recycling rate, and support water conservation and sewage compliance management.

[0035] Meteorological parameter sensing: Small weather stations are deployed in open areas of the construction site to collect parameters such as wind speed, wind direction, temperature, humidity, rainfall, and atmospheric pressure. Among them, wind speed monitoring is used for automatic shutdown determination of tower cranes and high-altitude operations in windy weather, and rainfall is used for rainwater recycling scheduling and sprinkler water consumption optimization.

[0036] 1.4 Multi-source heterogeneous data standardization preprocessing unit It solves the problem of data interoperability between devices from different manufacturers, using different protocols, and of different types, and provides unified, high-quality, standardized data for upper-layer systems.

[0037] Protocol Conversion and Interface Adaptation: Deploy an industrial protocol conversion gateway that supports more than 12 mainstream industrial protocols such as Modbus-RTU / TCP, OPCUA, MQTT, CANopen, Profinet, and HTTP; uniformly convert the private protocols of different devices into standard JSON format to achieve data interoperability between heterogeneous devices without the need for customized development for individual devices.

[0038] Data cleaning and outlier handling: The front-end sensing nodes perform data filtering on-site, using Kalman filtering and moving average filtering algorithms to remove sensor noise; outliers are automatically identified and removed using the 3σ criterion and threshold determination method; and linear interpolation and historical mean methods are used to fill in temporarily missing data to ensure data continuity and effectiveness.

[0039] Spatiotemporal alignment and structured encapsulation: Based on a unified clock source, all sensing data is timestamped and aligned, with a time synchronization error of ≤10ms; structured encapsulation is performed according to the six-tuple format of "device unique ID-timestamp-parameter type-value-spatial coordinate-data quality level" to establish a unified data model and eliminate data format differences.

[0040] Data quality assessment: Automatically rates the quality of each data item, classifying it into four levels: excellent, acceptable, abnormal, and missing; statistically analyzes data completeness, accuracy, and real-time performance; low-quality data is not allowed to enter the upper-level decision-making system, ensuring the reliability of collaborative control.

[0041] Module 2, Network Transmission and Edge Computing Module: This module is a crucial hub connecting the perception layer and the decision-making layer. It adopts an architecture of "layered networking + edge computing power + end-to-end security," ensuring reliable transmission of massive amounts of data while achieving low-latency control at the field level. It serves as the infrastructure for the stable operation of the automatic collaborative control system and is further divided into three core parts: 2.1 Global Layered Hybrid Communication Network Architecture In response to the complex environment, diverse equipment types, and varying service latency requirements at construction sites, a layered networking scheme is adopted to achieve full coverage both above and below ground, indoors and outdoors, with communication resources matched to different services as needed.

[0042] Terminal sensing layer low-power wide area network: For low-speed, low-power terminals such as environmental sensors, water and electricity meters, and valve controllers, LoRaWAN wide area low-power communication technology is adopted; the coverage radius of a single gateway is ≥1.5km, supporting concurrent access of thousands of terminals, and the battery life of the terminals can reach 3~5 years; it is suitable for sensing nodes with small data volume and dispersed deployment, reducing wiring costs and construction difficulty.

[0043] High-speed industrial network at the field access layer: For high-bandwidth, low-latency services such as mobile robots, high-definition video, and real-time equipment control, a hybrid coverage of "5G private network + WiFi 6" is adopted; the 5G private network adopts slicing technology to ensure that control services have dedicated bandwidth, with end-to-end transmission latency ≤10ms and packet loss rate ≤0.01%; WiFi 6 adopts high-density AP deployment, supports concurrent access of multiple devices, and the access capacity of a single AP is ≥200 units, meeting the needs of a large number of devices on the work surface to connect to the network at the same time.

[0044] Core backhaul layer fiber optic backbone network: Fixed nodes such as the core computer room, tower crane control room, and batching plant control room at the construction site are connected by industrial fiber optic Ethernet to form a backbone ring network; bandwidth ≥1000Mbps, supports ring network redundancy protection, automatic switching for single point faults, and switching time ≤50ms; ensuring the stability and reliability of massive data backhaul to the cloud.

[0045] Enhanced communication in underground / enclosed spaces: For areas with signal obstruction such as basements, tunnels, and utility tunnels, a 5G signal distribution system combined with leaky cables is used to enhance coverage; combined with local forwarding by edge gateways, stable network connectivity for underground construction equipment and reliable issuance of control commands are ensured, solving the problem of underground signal blind spots in traditional solutions.

[0046] 2.2 Distributed Edge Computing Power System By bringing computing power to the construction site and building a two-tier computing power system of "edge gateway-edge server", local business can be processed locally, significantly reducing the computing power pressure on the cloud and improving control response speed.

[0047] Lightweight computing on the edge gateway side: Deploy industrial edge gateways in each partition and each type of device cluster, with lightweight computing power; responsible for data acquisition, protocol conversion, simple logic control and data preprocessing of devices in the region; can independently complete low-complexity control tasks such as single device start-up and shutdown, threshold alarm, and simple linkage, with a response latency of ≤20ms, without relying on upper-layer systems.

[0048] Edge server on-site aggregation computing: Industrial-grade edge servers are deployed in the central computer room of the construction site to carry out complex on-site computing tasks, including high real-time tasks such as multi-device collision avoidance computing, local path planning, environmental linkage control, equipment fault diagnosis, and video AI analysis; the control response latency is ≤50ms, which is far lower than the response speed of hundreds of milliseconds in the cloud, meeting the requirements of construction safety and real-time collaboration.

[0049] Edge-cloud collaborative scheduling mechanism: Establish an edge-cloud computing power collaborative mechanism, with control-type services with high real-time requirements being processed on the edge side, and non-real-time services such as global optimization, big data analysis, and report statistics being uploaded to the cloud for processing; the edge side periodically synchronizes aggregated data to the cloud, and the cloud sends optimized scheduling strategies and algorithm models to the edge side to achieve optimal allocation of computing resources.

[0050] Network outage autonomy and data caching: Edge nodes have complete network outage autonomy capabilities. When the network connection with the cloud is interrupted, they can independently maintain basic control of on-site equipment, data collection and local storage; local storage capacity ≥1TB, supporting at least 30 days of full data caching; automatic resume transmission after network recovery, ensuring no data loss and no business interruption, thus guaranteeing construction continuity.

[0051] 2.3 Industrial-grade end-to-end security protection system Throughout the entire chain from terminal, transmission, boundary, to application, we strictly adhere to industrial control system security standards to ensure the security of construction data and equipment control.

[0052] Terminal identity security: All access devices use a unique hardware identity identifier and are authenticated based on a whitelist mechanism; only authorized devices can access the network, and unauthorized terminals are automatically blocked; the device firmware adopts an encrypted storage and security upgrade mechanism to prevent malicious tampering with the device control program.

[0053] Transmission link security: All data transmission uses TLS 1.3 end-to-end encryption, and critical control commands are signed and verified using national cryptographic algorithms; this prevents data from being eavesdropped on, tampered with, or replayed during transmission, ensuring the authenticity and integrity of control commands.

[0054] Boundary access security: Deploy industrial firewalls and intrusion detection systems between the field control network, office network, and the Internet, using deep packet inspection technology to identify and block industrial protocol attacks; strictly control access permissions, opening only necessary ports and services, isolating the control network from external networks, and reducing the attack surface.

[0055] Operation audit and traceability: Establish a complete operation log audit system. All equipment control commands, parameter modifications, mode switching, and manual operations are recorded in their entirety. The logs include information such as the operator, operation time, operation content, and operation results. They are tamper-proof and have a retention period of ≥6 months, meeting the requirements for safety incident traceability and compliance audit.

[0056] Module 3, Collaborative Decision-Making and Intelligent Scheduling Module: This module is the system's "intelligent brain," using digital twins as a visualization carrier and multi-objective optimization algorithms as its core. It deeply embeds green construction constraints into the scheduling logic, achieving globally optimal coordination among multiple devices and processes. It is further refined into four core units: 3.1 Digital Twin Base at the Construction Site By integrating BIM, GIS, point cloud, and real-time sensing data, a 1:1 high-precision digital mirror of the construction site is constructed, realizing "virtual-real synchronization, simulation and deduction, and visual interaction," providing a support platform for collaborative decision-making.

[0057] Multi-source model lightweight fusion: Import BIM design models (supporting mainstream formats such as IFC and RVT), use lightweight technology to simplify geometry and compress textures, retain component geometric information, attribute information, and process parameters, and achieve a model lightweighting rate of ≥80%; integrate GIS geographic information, terrain data and on-site point cloud models to form a full-domain 3D model that includes geographic environment, building entities and construction equipment, with spatial geometric accuracy reaching the centimeter level.

[0058] Synchronous dynamic mapping between virtual and real: Establish a unified spatiotemporal benchmark and dynamically bind real-time sensing data with the 3D model; real-time updates of the twin model are driven by data such as equipment location, operating status, environmental parameters, and construction progress, with an overall refresh rate of 1~10Hz and a refresh rate of ≥10Hz for key control areas; and achieve state synchronization, action synchronization, and event synchronization between the physical construction site and the digital twin scene.

[0059] Construction process simulation: The built-in construction simulation engine supports pre- and post-construction simulations of construction plans, equipment operation paths, and scheduling plans; it can simulate the construction period, energy consumption, carbon emissions, and spatial conflicts under different construction sequences and equipment combinations, and quantitatively evaluate the advantages and disadvantages of the plans; it can identify progress bottlenecks, collision risks, and energy consumption peaks in advance, optimize the plans before construction, and reduce on-site rework and resource waste.

[0060] 3D visualization interactive engine: Supports interactive operations such as 3D scene roaming, zooming, sectioning, and component attribute querying; can intuitively display the real-time location, operating parameters, alarm information of all equipment, as well as construction progress, environmental quality, energy consumption and carbon emission data; supports automatic alarm event location and pop-up prompts, assisting managers to quickly grasp the overall operating conditions and improve decision-making efficiency.

[0061] 3.2 Global Task Decomposition and Intelligent Resource Matching Based on the overall construction schedule, automatically completing process breakdown, equipment matching, and initial plan generation is a fundamental step in collaborative scheduling.

[0062] WBS process atomization decomposition: Import the project WBS schedule plan and automatically decompose the sub-projects into sub-projects, processes, and atomic tasks; clarify the operation location, workload, process requirements, schedule nodes, pre- and post-dependencies, and quality standards of each atomic task; the decomposition granularity meets the scheduling requirements of a single machine for a single operation, and realizes the executability and traceability of tasks.

[0063] Equipment resource capability profile construction: Create a capability profile for each construction equipment, including dimensions such as equipment type, rated operating efficiency, rated power, energy consumption level, emission level, applicable procedures, maintenance status, location information, and historical fault records; dynamically update the equipment availability status, and automatically remove faulty or under maintenance equipment from the scheduling resource pool.

[0064] Initial task allocation under multiple constraints: Taking into account four major categories of constraints, namely schedule constraints, space constraints, process constraints, and green constraints, an intelligent matching algorithm is used to match the optimal equipment combination for each task; an initial construction plan is automatically generated, including the operation sequence, operation path, and operation parameters of each piece of equipment, to ensure smooth connection of processes and no logical conflicts.

[0065] Green target weight configuration: Supports custom configuration of weights for multiple targets such as construction period, cost, energy consumption, carbon emissions, and environmental protection; In green building construction scenarios, the weight ratio of energy consumption and carbon emissions is increased by default, and energy consumption per unit of project and carbon emissions per unit of output value are taken as core optimization targets to ensure the implementation of green construction targets from the source of planning.

[0066] 3.3 Multi-device dynamic collaborative scheduling engine The dual-layer scheduling mechanism of "static global planning + dynamic rolling optimization" balances plan stability and on-site adaptability, and is the core of realizing automatic collaboration among multiple devices.

[0067] Static global optimization scheduling: Before construction or during daily non-operational periods, intelligent optimization algorithms such as genetic algorithm, particle swarm optimization algorithm, and ant colony algorithm are used for global static scheduling. The global optimal work plan is solved with multiple objective functions, such as the shortest construction period, the lowest total energy consumption, the lowest carbon emissions, and the lowest equipment conflicts. The work schedule, travel path, and work parameters of each piece of equipment are output to achieve optimal global resource allocation.

[0068] Rolling time-domain dynamic rescheduling: A rolling time-domain optimization mechanism is adopted during construction, and the rolling window duration is configurable (usually 15~60 minutes). When triggering conditions such as equipment failure, schedule deviation exceeding the threshold, sudden weather changes, design changes, or environmental exceedances occur, local rescheduling is automatically triggered. The plan is updated within minutes, and only the affected equipment and processes are adjusted to ensure the stability of the overall plan while adapting to dynamic changes on site.

[0069] Typical scenario collaborative algorithm: A dedicated collaborative algorithm is customized for high-frequency construction scenarios. Multi-tower crane collaboration: Based on three-dimensional spatial position and motion trajectory, it realizes collision avoidance collaboration of multiple tower cranes, automatically plans the rotation path and allocates the work area to avoid cross-operation conflicts; Coordinated concrete pouring: Achieve full-chain cycle matching between batching plant, transport tanker trucks, on-site placing boom, and vibrating equipment, supply materials on demand, and reduce tanker truck waiting time and concrete waste; Coordinated hoisting of prefabricated components: This enables seamless coordination between transport vehicles, tower cranes, and installation robots, allowing components to be hoisted and positioned immediately upon arrival on site, reducing on-site stacking and secondary handling.

[0070] Green constraint embedding mechanism: Deeply embedding green construction rules into the scheduling algorithm logic: Peak-valley electricity pricing constraints: High-energy-consuming equipment is prioritized for operation during off-peak and flat periods, while the load is automatically reduced or non-essential operations are suspended during peak periods, thereby reducing electricity costs and peak pressure on the power grid; Environmental constraints: Automatically suspend earthwork, crushing and other dust-generating processes during windy weather; automatically suspend high-noise equipment operation at night; Energy efficiency constraints: Prioritize the use of low-energy-consumption and low-emission equipment; optimize equipment routes to reduce idle driving and ineffective operation time; avoid prolonged idle standby of equipment.

[0071] 3.4 BIM-Driven Precision Work Instruction Generation By establishing a data link between the design and construction phases, BIM model data can directly drive equipment operations, reducing errors from manual secondary data entry and improving construction accuracy and material utilization.

[0072] BIM Model Data Parsing and Extraction: Automatically parses geometric and non-geometric information such as component geometric dimensions, spatial coordinates, material properties, process standards, and quantities in the BIM model; extracts key construction data such as the outline of the pouring area, reinforcement layout parameters, and component installation points, eliminating the need for manual on-site layout and parameter entry.

[0073] Spatial coordinate system transformation: Establish the transformation relationship between the BIM design coordinate system, the construction site coordinate system, and the equipment local coordinate system; through the coordinate transformation matrix, automatically convert the BIM design coordinates into operating coordinates that the equipment can recognize, with a transformation accuracy of ≤3mm, ensuring that the equipment operating position is consistent with the design.

[0074] Equipment-level operation parameter mapping: Automatically map BIM component parameters to equipment operation parameters and motion trajectories; for example, convert the BIM outline of wall casting into the operation path, material flow rate, and casting height of the concrete placing machine; convert the BIM parameters of steel reinforcement components into the cutting length and bending angle of the steel reinforcement processing robot; realize "construction according to the formwork", improve construction accuracy, and reduce material waste and rework.

[0075] Construction deviation reverse closed loop: After the on-site construction is completed, the actual construction data scanned by point cloud is sent back to the BIM model to automatically compare the deviation between the design and the actual situation; the deviation data is synchronously fed back to the scheduling system to provide a basis for the parameter adjustment of subsequent processes, forming a closed loop iteration of "design-construction-feedback-optimization".

[0076] Module 4, Equipment Execution and Automatic Control Module: This module serves as the execution terminal for collaborative decision-making, responsible for translating scheduling instructions into actual operational actions of the equipment. It ensures collaborative accuracy through precise control and closed-loop feedback, directly determining the system's automation level and control reliability. It is further divided into four core components: 4.1 Automated Closed-Loop Control of Single-Machine Equipment Developing independent single-machine automatic control loops for different types of construction equipment to achieve unmanned autonomous operation is the foundation for multi-equipment collaboration.

[0077] Automatic control of lifting equipment: It connects to the tower crane PLC control system to realize automatic hook positioning, automatic slewing and obstacle avoidance, automatic luffing and speed regulation, and smooth lifting of load; it adopts fuzzy PID control algorithm, combined with hook anti-sway control technology, so that the hook positioning accuracy is ≤5cm and the swing amplitude is controlled within ±10cm; it has built-in multiple protection logics such as overload, over-torque, over-limit, and over-wind speed, and automatically restricts and alarms for illegal operation.

[0078] Automatic control of earthmoving and concrete equipment: Earthmoving equipment such as excavators and graders, combined with 3D design elevation and GNSS positioning, can achieve automatic leveling and excavation operations with an operational elevation accuracy of ≤3cm; Concrete placing booms, combined with BIM pouring outlines, automatically plan their travel paths and placement points, match pouring flow and vibration rhythm, and achieve unmanned continuous pouring, reducing material waste from manual operation.

[0079] Autonomous operation control of construction robots: Vibration robots, rebar tying robots, ground leveling robots, etc., adopt a control architecture of "visual positioning + motion control + force control feedback"; automatically identify the work area, plan the optimal work path, and control the work depth, force and speed; the work coverage rate reaches 100%, avoiding missed work and over-work, and the construction quality consistency is far higher than that of manual work.

[0080] Automation Retrofit for Outdated Equipment: For outdated construction equipment without native automation interfaces, low-cost automation retrofits can be achieved by adding external control modules, servo actuators, and sensor kits; remote start / stop, parameter adjustment, and status monitoring are supported, and the equipment can be integrated into a unified collaborative management and control system to extend equipment lifespan and reduce system deployment costs.

[0081] 4.2 Cross-equipment and process-level collaborative linkage The core of "automatic collaboration" is to achieve time synchronization, spatial coordination, and material flow matching between equipment of different categories and manufacturers, eliminate waiting time between processes, and improve overall construction efficiency.

[0082] Precise clock synchronization system: Adopting the PTP precise time protocol, it provides a unified clock reference for all equipment and control systems, with a network-wide time synchronization error of ≤1ms; ensuring the timing consistency of multiple equipment actions, achieving precise coordination of process connections, and avoiding coordination errors caused by clock deviations.

[0083] Collaborative Spatial Conflict Avoidance: All devices share position, attitude, and motion trajectory data in real time, and the edge side continuously performs three-dimensional spatial conflict detection; when the safe distance between two devices approaches the warning threshold, avoidance actions such as deceleration, detour, and pause are automatically triggered; a graded warning mechanism is adopted, which is divided into three levels: prompt, deceleration, and shutdown, taking into account both work efficiency and construction safety.

[0084] Material flow rhythm matching: A rhythm coordination mechanism is built for the entire construction material chain; for example, in the steel bar processing scenario, processing robots, transfer AGVs, and binding robots share production capacity, location, and task status data in real time, and transfer is carried out immediately after processing is completed and binding is carried out immediately after placement, with zero gap between processes; in the concrete pouring scenario, the discharge rhythm of the mixing plant and the frequency of tank truck transportation are dynamically adjusted according to the on-site pouring speed to achieve on-demand material supply and reduce waiting waste.

[0085] Cross-vendor equipment interoperability adaptation: Based on the OPCUA industrial interoperability standard, a unified equipment information model and service interface are built; different manufacturers and different types of equipment can achieve data interaction and collaborative control without customized docking; solving the problem of traditional construction equipment manufacturers operating independently and unable to work together.

[0086] 4.3 Hierarchical Closed-Loop Correction of Execution Effect Establish a complete control closed loop of "instruction issuance - execution feedback - deviation comparison - dynamic correction" to ensure construction quality and control accuracy, and achieve adaptive adjustment of the control process.

[0087] Real-time feedback of execution status: During equipment execution, data such as work location, workload, operating parameters, and fault status are fed back in real time; the feedback frequency is matched with the control frequency, and the feedback frequency of key control parameters is ≥5Hz, ensuring that the control system can keep abreast of the execution status.

[0088] Deviation grading rules: The execution results are compared with the design values ​​and process standard values, and divided into three levels according to the magnitude of the deviation: Level 1 deviation (within the allowable range): If the deviation is less than the allowable process error, the system will automatically fine-tune the parameters to correct it; Level 2 Deviation (Warning Range): When the deviation approaches the allowable threshold, the system will automatically issue an alarm, prompting attention, and at the same time increase the correction efforts; Level 3 Deviation (Out of Limit): When the deviation exceeds the safety or process threshold, the system automatically suspends operation and notifies management personnel to handle the situation on-site.

[0089] Parameter adaptive correction: For first- and second-level deviations, adaptive PID, model predictive control and other algorithms are used to automatically adjust the equipment operating parameters; for example, the pump speed is automatically adjusted for deviations in pouring flow rate, and the motion trajectory is automatically corrected for deviations in position; without manual intervention, the deviations are automatically controlled within the allowable range to ensure stable construction quality.

[0090] Abnormal operating conditions are handled in a tiered manner: For abnormal operating conditions such as equipment failure, sudden environmental changes, and personnel intrusion, the corresponding handling procedures are automatically triggered; for minor abnormalities, the work plan is automatically adjusted, and for serious abnormalities, the machine is automatically shut down and an alarm is issued; all abnormal events are automatically recorded to form a closed loop for handling.

[0091] 4.4 Tiered Human-Machine Collaboration and Emergency Takeover While maintaining full automation, multiple levels of manual intervention channels are retained to balance automation efficiency and construction safety, meeting the on-site management needs of engineering construction.

[0092] A three-tiered control authority system is established, with three control modes: fully automatic, remote manual, and on-site manual, with priority increasing in that order. During normal operation, the fully automatic mode is used, and the equipment operates autonomously according to the dispatch instructions. In complex working conditions, the remote manual mode can be switched, allowing operators to control the equipment remotely. For equipment maintenance and emergencies, the on-site manual mode is used, allowing on-site personnel to directly control the equipment.

[0093] Remote control cockpit: Equipped with a remote control center, combining digital twin scenarios and multiple real-time videos to achieve remote control of equipment; control latency ≤100ms, operation feel consistent with on-site; supports single equipment takeover and multi-equipment monitoring, reducing on-site personnel and improving safety in hazardous working conditions.

[0094] Tiered emergency shutdown mechanism: Three levels of emergency shutdown permissions are set: single machine emergency shutdown, area emergency shutdown, and global emergency shutdown; on-site personnel, remote operators, and management personnel can all trigger the corresponding level of emergency shutdown; the emergency shutdown command has the highest priority, directly reaching the equipment controller, with a response time of ≤10ms, ensuring construction safety under extreme conditions.

[0095] Automatic switching logic for abnormal operating conditions: When the system detects abnormalities such as equipment failure, sensor failure, or network interruption, it automatically switches from fully automatic mode to safe mode; if necessary, it switches to manual takeover mode, and at the same time issues audible and visual alarms and pushes information to prevent accidents from occurring.

[0096] Module 5: Green Energy Efficiency and Safety Management Module This module is the core implementation vehicle for green building construction attributes, focusing on three major goals: energy conservation and carbon reduction, pollution prevention and control, and construction safety. It integrates green constraints throughout the entire collaborative control process and achieves continuous optimization through data review, and is refined into four core units: 5.1 Refined Management of Energy Efficiency and Carbon Emissions Making energy consumption and carbon emissions measurable, analyzable, and optimizable during the construction phase, and supporting green building and green construction site certifications, is the core of green construction.

[0097] Multi-dimensional energy consumption metering and statistics: Energy consumption statistics are performed according to four dimensions: equipment, process, region, and time period, and daily, weekly, monthly, and quarterly energy consumption reports are automatically generated; total energy consumption, energy consumption per unit of project, energy consumption ratio of each equipment, and energy consumption distribution during peak and off-peak periods are displayed in real time; high-energy-consuming links and equipment are accurately identified, providing targeted targets for energy-saving optimization.

[0098] Equipment energy efficiency baseline and waste identification: Based on historical operating data, establish energy efficiency baselines for individual equipment and single-process; automatically identify energy waste scenarios such as no-load operation, over-engineered equipment, and inefficient operation; for example, tower cranes idle for a long time and pump trucks operate at low flow and high pressure; automatically issue optimization suggestions, or directly adjust the equipment operating status through collaborative scheduling to reduce ineffective energy consumption.

[0099] Carbon emission accounting during the construction phase: Strictly following the "Standard for Calculation of Carbon Emissions in Buildings" GB / T51366, based on energy consumption data, material consumption data, and transportation data, combined with the carbon emission factors issued by the state, the system automatically calculates carbon emissions for Scope I and Scope II during the construction phase; it generates carbon ledgers by sub-item of the project and by time dimension, and the accuracy of the calculation meets the requirements for carbon emission verification.

[0100] Green building evaluation benchmark support: Built-in indicator system of "Green Building Evaluation Standard" GB / T50378 and "Green Construction Evaluation Standard" GB / T50640; automatically matches scoring items such as energy saving, water saving, material saving, and environmental protection, automatically calculates the completion status of indicators, and generates self-evaluation reports; supports green building star rating certification and green construction site application for projects.

[0101] 5.2 Green Construction Environment Hierarchical Linkage Control Establish an automatic linkage mechanism between environmental parameters and equipment operation to achieve proactive pollution control, meet environmental compliance requirements, and reduce environmental pollution.

[0102] Dust pollution is controlled in a tiered manner: three levels of dust concentration thresholds are set, each corresponding to a different linkage strategy. Level 1 Threshold (Warning Value): Automatically activates the fencing spray and road watering equipment to carry out dust suppression operations; Level 2 threshold (exceeding the standard): Start all spray and mist cannon equipment, and at the same time suspend earthwork excavation, material crushing and other dust-generating processes; Level 3 Threshold (Severe Exceeding Standard): All outdoor construction work is suspended, dust suppression measures are strengthened, and the situation is reported to the monitoring platform. Once the concentration drops to a safe level, work will automatically resume in a graded manner without manual intervention.

[0103] Noise pollution time-based management: Strictly enforce daytime and nighttime noise emission standards; automatically prohibit high-noise equipment operations such as pile driving and crushing during nighttime hours; automatically adjust the operation sequence of high-noise equipment to avoid sensitive periods when daytime noise exceeds the standard; combine noise source tracing analysis to accurately locate and control equipment exceeding the standard, thereby reducing complaints from residents.

[0104] Water resource recycling and joint control: Based on rainfall and soil moisture data, the amount and frequency of water used for dust suppression and greening irrigation are automatically adjusted; the level of rainwater harvesting tanks is automatically controlled, and the harvested rainwater is given priority for dust suppression, greening, and vehicle washing; construction precipitation is given priority for recycling and reuse, thereby improving the water resource recycling rate and reducing the consumption of municipal tap water.

[0105] Construction waste resource management: AI visual identification of construction waste types and quantities, automatic statistics of construction waste generation and resource utilization rate; optimization of construction waste transportation and classification processing paths, increasing the recycling rate of recyclable materials; combined with BIM material quantity calculation, calculating material loss rate, and reducing construction waste generation through precise construction.

[0106] 5.3 Proactive Prevention and Control of Construction Safety in All Dimensions It covers three scenarios: equipment safety, space safety, and personnel safety, and builds a proactive safety protection system that includes "early warning, in-process prevention and control, and post-event traceability".

[0107] Predictive maintenance and fault warning: Based on equipment operation data, fault diagnosis algorithms and life prediction models are used to identify fault signs such as bearing wear, motor overheating, and hydraulic system abnormalities in advance; maintenance reminders are generated in advance to achieve predictive maintenance, reduce downtime due to sudden equipment failures, avoid safety accidents caused by operating equipment with defects, and extend the service life of equipment.

[0108] 3D spatial collision avoidance protection: Construct a 3D electronic map and electronic fence for the construction site, and automatically mark dangerous areas and restricted areas; when equipment approaches a dangerous area or when equipment approaches a safe distance, it automatically triggers graded warnings and deceleration and shutdown actions; achieves collision avoidance in all scenarios of dynamic-static and dynamic-dynamic, covering three types of collision scenarios: equipment-to-equipment, equipment-to-fixed structure, and equipment-to-personnel.

[0109] Intelligent control of unsafe behaviors: Through AI visual analysis, it automatically identifies unsafe behaviors such as not wearing a safety helmet, not wearing a safety belt, entering a dangerous area without permission, and performing hot work without permission; upon identification, it immediately triggers an audible and visual alarm, records the event information, and pushes it to the management personnel; it realizes real-time correction and closed-loop management of unsafe behaviors, reducing the risk of human-caused safety accidents.

[0110] Hazardous area electronic fence management: Supports customizable 3D electronic fences, allowing for different access permissions such as prohibited entry, restricted entry, and authorized entry; combines UWB positioning and AI vision to monitor personnel and equipment entering hazardous areas in real time; automatic alarm and shutdown of relevant equipment in case of unauthorized entry, ensuring safe operation in hazardous areas.

[0111] 5.4 Performance Evaluation and Continuous Optimization Mechanism By reviewing data and accumulating experience, we can continuously iterate and optimize system algorithms and management strategies to gradually improve the level of green construction and collaborative efficiency.

[0112] Green Construction Comprehensive Performance Index System: Establish a comprehensive performance index system covering construction period, quality, safety, energy conservation, water conservation, material conservation, environmental protection, and carbon emissions; quantitatively display the level of green construction and intelligent construction of projects, support horizontal comparison of multiple projects and multiple sections, and provide data support for management decisions.

[0113] Multi-dimensional data analysis and reporting: Automatically generates various reports such as green construction monthly reports, equipment energy efficiency analysis reports, collaborative efficiency reports, safety management reports, and carbon emission ledgers; supports custom report templates and statistical dimensions, and automatically pushes them to relevant management personnel; replaces manual statistical analysis and improves management efficiency.

[0114] The scheduling algorithm is self-iteratively optimized: the optimal scheduling strategies and control parameters under different working conditions and scenarios are accumulated to form an algorithm knowledge base; through reinforcement learning, data mining and other technologies, the scheduling algorithm and control model are continuously iterated and optimized; the longer the system runs, the better the collaborative efficiency and green optimization effect, realizing self-learning and self-optimization.

[0115] Optimal process knowledge accumulation: The efficient, low-consumption, and high-quality construction collaboration model is solidified into a standard process library, forming a corporate knowledge asset; it can be reused in subsequent similar projects, quickly replicating successful experiences, continuously reducing energy consumption and carbon emissions per unit of project, and improving the overall level of green construction of the enterprise.

[0116] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An automatic collaborative control system for green building construction equipment, characterized in that, include: The multi-source sensing and data acquisition module is used to collect various parameters such as the operating status of various construction equipment, construction space and process progress, and green construction environment in the whole domain, including cranes, earthmoving and construction robots. After protocol conversion, outlier removal and structured preprocessing of multi-source heterogeneous data, it outputs standardized sensing data. The network transmission and edge computing module receives standardized sensing data, performs local data aggregation, low-latency control calculations, and offline data caching at the edge, and transmits the aggregated global sensing data upward to the decision layer and forwards control commands downward to the execution end. The collaborative decision-making and intelligent scheduling module receives global perception data, generates a global collaborative scheduling scheme and device-level operation instructions based on the digital twin base and multi-objective optimization algorithm, and sends them to the network transmission and edge computing module. The equipment execution and automatic control module receives work instructions, drives various types of construction equipment to complete single-machine automated operations and cross-equipment process collaborative linkage, and transmits execution status and work feedback data back in real time. The green energy efficiency and safety management module acquires operational data from each module, completes energy efficiency and carbon emission accounting, environmental linkage control and safety risk prevention and control, and outputs optimization strategies to the collaborative decision-making and intelligent scheduling module to form a closed-loop management and control system for green construction.

2. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The multi-source sensing and data acquisition module includes an equipment status sensing unit, a construction space progress sensing unit, a green environment sensing unit, and a data preprocessing unit; The equipment status sensing unit collects data on the operating conditions, energy consumption, and fault symptoms of various construction equipment; the construction space progress sensing unit collects data on the spatial distribution, component location, and process completion; the green environment sensing unit collects environmental parameters such as dust, noise, hydrology, and meteorology; and the data preprocessing unit completes the standardization conversion of heterogeneous data and data quality verification.

3. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The network transmission and edge computing module adopts a layered hybrid networking architecture, matching the corresponding communication methods to terminal devices with different speed and latency requirements, and building a stable communication network covering the entire construction site. Two levels of computing nodes are set up on the edge side, which respectively undertake lightweight control on the end side and complex computing tasks at the site level, and have the ability to be autonomous when the network is down and to retain data locally.

4. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The collaborative decision-making and intelligent scheduling module has a built-in digital twin base, which integrates building information model and real-time perception data to build a digital mirror of the construction site. It supports the simulation and deduction of the construction process and the synchronous mapping of virtual and real states, providing visual decision support for collaborative scheduling.

5. The automatic collaborative control system for green building construction equipment according to claim 4, characterized in that, The collaborative decision-making and intelligent scheduling module adopts a scheduling mechanism that combines static global planning with dynamic rolling optimization. It takes into account multiple constraints such as schedule, space, process and green construction to generate multi-equipment operation sequence and path planning, and can trigger local rescheduling according to unexpected situations on site.

6. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The equipment execution and automatic control module interfaces with the native control system of various construction equipment, and can issue control commands such as start / stop, speed, trajectory and operation parameters to the equipment to realize unmanned autonomous operation at the single machine level, and has built-in safety limit protection logic.

7. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The equipment execution and automatic control module supports process-level collaborative linkage between equipment of different categories and manufacturers. Through unified clock synchronization and spatial information sharing, it realizes the matching of action rhythms of multiple equipment and dynamic spatial avoidance, eliminating waiting gaps between processes.

8. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The green energy efficiency and safety management module includes an energy efficiency and carbon emission management unit, which can statistically analyze energy and resource consumption from multiple dimensions such as equipment, process, and region, calculate carbon emissions during the construction phase, identify inefficient operation scenarios and generate energy-saving optimization strategies, and support green construction evaluation and benchmarking.

9. The automatic collaborative control system for green building construction equipment according to claim 8, characterized in that, The green energy efficiency and safety management module also includes an environmental linkage control unit, which establishes a linkage management mechanism between environmental parameters and equipment operation. Based on changes in environmental parameters such as dust, noise, and hydrology, it automatically adjusts the operating status of equipment and the operation strategy of environmental protection facilities to achieve proactive pollution prevention and control.

10. The automatic collaborative control system for green building construction equipment according to claim 1, characterized in that, The green energy efficiency and safety management module also has comprehensive safety control and continuous optimization capabilities, enabling equipment fault early warning, space collision avoidance and identification of unsafe personnel behavior. At the same time, it accumulates operational data to iteratively optimize scheduling strategies and continuously improve system collaboration efficiency and green construction level.

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