Complex construction site construction equipment control system and method

Abstract

The invention provides a complex construction site construction equipment control system and method, and the system constructs a basic state semantic model of each type of equipment, and covers all perception and control instruction data; the comprehensive management and control platform dynamically extracts and issues one or more data packet formats from the basic model according to construction scenes, areas and equipment types, and fine adjustment of sensing information and instruction formats is achieved; the edge computing device is deployed at an equipment end or a cluster end and is responsible for dynamic mapping analysis, redundancy removal, priority screening and unified protocol packaging of a multi-heterogeneous protocol; the equipment end reports a null instruction sensing data packet, the comprehensive management and control platform generates an optimal control instruction, compresses recent sensing data and then issues the compressed recent sensing data in the same data packet format; the equipment end decides whether to execute the instruction or not, if not, problem expression is fed back, and a safe closed loop is formed; in a cluster scene, gathering fusion and instruction distribution of multi-level edge computing devices are supported, and efficient collaboration of heterogeneous clusters is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for construction sites, and in particular to a control system and method for construction equipment in complex construction sites. Background Technology

[0002] In complex construction sites, a large number of construction equipment (such as tower cranes, construction elevators, transportation equipment, and pumping equipment) face changing environments and collaborative requirements. Construction equipment typically uses specific communication protocols for data acquisition and control command issuance. Typical protocols include, but are not limited to, custom byte stream protocols, Modbus-RTU, CAN, RTSP, and NMEA0183. These protocols differ significantly in frame structure, encoding rules, transmission timing, and error checking, leading to the following problems: high gateway protocol conversion overhead, requiring the development of independent parsing modules for each protocol; fragmented data semantics, with different types of data reported separately, lacking stateful aggregation and correlation analysis; wasted bandwidth, with multiple protocol data packets carrying redundant fields such as device IDs and timestamps repeatedly; and poor real-time performance, with multi-protocol polling causing significant delays in critical control commands, posing security risks.

[0003] To address the aforementioned issues, existing technologies often employ multi-source sensing information fusion, unpacking and repackaging multiple data packets at the equipment or edge to achieve effective data fusion. However, this data fusion method is unsuitable for complex construction sites. In complex construction sites, the same equipment may exhibit significant differences in its operating mode, key sensing information types (such as vibration signals, movement speed, and positioning data), and information transmission methods (such as wired RS485, wireless CAN, or UDP stream) under different construction scenarios (e.g., pit operation, elevator transportation, inter-floor coordination, or formwork support). Furthermore, the collaboration methods between equipment clusters dynamically change with variations in scenario, equipment type, and quantity. This results in fixed data fusion methods failing to meet the specific needs of different construction scenarios, exhibiting a lack of flexibility.

[0004] Therefore, how to provide a control system and method for construction equipment in complex construction sites is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, this invention proposes a control system and method for construction equipment in complex construction sites. Through a dynamic data packet format publishing mechanism, cross-protocol semantic fusion at the edge, closed-loop command verification and execution, and cluster collaborative control, it achieves refined adaptation to complex construction scenarios, optimized fusion of multi-source data, and efficient and safe control.

[0006] The technical solution of a control system and method for complex construction equipment at a construction site according to the present invention is as follows:

[0007] A method for controlling construction equipment at complex construction sites includes the following steps:

[0008] Step S1, Construct the basic state semantic model of the equipment: Construct the state and command data of the construction equipment into a data packet format with multiple combinations, and construct a basic state semantic model for each type of construction equipment.

[0009] Step S2, deploy edge computing devices and integrated management and control platform: deploy edge computing devices at the construction equipment end or equipment cluster end. The edge computing devices are used for dynamic mapping and parsing of multiple heterogeneous protocols, as well as data packet processing, distribution and cluster aggregation; the integrated management and control platform manages the equipment cluster.

[0010] Step S3, Dynamically publish data packet format: The integrated management and control platform extracts the data packet format based on scenario requirements and dynamically publishes one or more data packet formats to the edge computing devices of the construction equipment or equipment cluster by region and / or equipment type; the data packet format is extracted from the basic state semantic model of the equipment;

[0011] Step S4, Equipment-side perception data collection and fusion reporting: The equipment-side drives perception collection based on the received data packet format, automatically interprets various heterogeneous raw protocol data, forms target semantic combinations, processes them, encapsulates the processed target semantic combinations according to a unified protocol format, and reports the perception data packets to the integrated management and control platform. Among them, the instruction data is empty.

[0012] Step S5, Central side command generation and issuance: The integrated control platform receives the sensing data packet (which may include cluster summary data), fills it into the corresponding data packet format, and compresses the sensing data before encapsulating it together and sending it back to the equipment in the same data packet format;

[0013] Step S6, Equipment-side command verification and execution feedback: After receiving the data packet of the filling command, the equipment compares the latest sensing information with the compressed sensing data to determine whether to execute the command; if executed, it operates according to the command and repeats step S3 to report the new sensing data packet, forming a closed-loop control; if not executed, it generates a new sensing data packet and attaches a command problem expression (such as conflict reason code or description) and reports it. The integrated management and control platform regenerates and adjusts the command based on the feedback and issues it.

[0014] Step S7, Cluster Equipment Collaborative Processing: In a cluster scenario with multiple construction equipment, the cluster edge computing device aggregates and merges the data packets of the subordinate equipment and then executes step S3 to report to the integrated management and control platform; the integrated management and control platform generates a comprehensive optimization control command based on the aggregated data and executes step S4 to send it to the cluster edge computing device; the cluster edge computing device then distributes the comprehensive command to each subordinate construction equipment to achieve cluster closed-loop collaborative control.

[0015] Furthermore, the basic state semantic model in step S1 includes all data related to equipment perception and control commands.

[0016] Furthermore, the data includes the status and instruction data of the construction equipment, which are constructed into a data packet in the form of multiple combinations. The multiple combinations include equipment ID, timestamp, environmental perception related data, vibration related data, motion related data, and control instruction related data. The construction equipment includes tower cranes, construction elevators, transportation equipment, and pumping equipment.

[0017] Furthermore, the automatic data interpretation and processing in step S4 includes: data verification, interception of key instruction frames and key data frames, and embedding the key instruction frames and key data frames into the unified multi-element combination in bytecode form to form the target semantic combination; then, time alignment and unified timestamp processing are performed on the multi-protocol data packets with a fixed time window, redundant fields are removed, priority judgment and filtering are performed based on relevant data, and compression encoding is performed using variable length encoding and differential encoding. If the same data packet is found, only the number is transmitted.

[0018] Furthermore, the various heterogeneous original protocols include, but are not limited to: custom byte stream protocols (such as RS485 serial port private frame format), Modbus-RTU protocol, CAN 2.0B protocol, RTSP over UDP video stream protocol and NMEA0183 Beidou positioning protocol; the unified protocol format includes: 2-byte frame header, 1-byte version number, 4-byte device ID, 8-byte timestamp, variable-length payload field and 2-byte CRC16 checksum.

[0019] Furthermore, in step S3, the data packet format supports dynamic publishing and multiple versions coexisting. The instruction publishing format and sensing data reporting format can be flexibly adjusted according to the construction scenario (such as collaborative collision avoidance for tower crane groups, material distribution in pumping equipment clusters, etc.), achieving refined control over different areas, equipment types, and collaboration modes. The purpose of using multiple data packets is to accommodate the equipment control needs of multiple scenarios. For example, tower cranes are used for both ground material transport and intra-floor material transport, requiring different sensing information and data packet transmission frequencies.

[0020] Furthermore, in step S4, the data packet format is extracted from the basic state semantic model of the equipment. That is, by analyzing the construction scenario, equipment tasks, and cluster collaboration requirements, the required perception data is extracted to form the data packet format.

[0021] Furthermore, in step S5, the multi-source information fusion judgment is made by the integrated management and control platform based on the perception data of multiple equipment to make a comprehensive judgment and give the optimal control command and data fusion strategy.

[0022] Furthermore, the judgment logic in step S6 includes perceptual data consistency verification, security threshold check, and scenario adaptability verification to ensure the security and reliability of instruction execution.

[0023] This invention also provides a control system for construction equipment at complex construction sites, used in the aforementioned control method for construction equipment at complex construction sites. The system includes: an equipment basic state semantic model, multi-level edge computing devices, and a comprehensive management and control platform. The equipment basic state semantic model includes a tower crane model, a construction elevator model, and a horizontal transportation equipment model. Each piece of equipment is equipped with a single edge device, and a main edge computing device is deployed at the floor cluster level. The comprehensive management and control platform performs comprehensive discrimination and generates group commands.

[0024] Compared with the prior art, the control system and method for complex construction equipment at construction sites according to the present invention have the following advantages:

[0025] This invention discloses a control system and method for construction equipment in complex construction sites. Through a dynamic data packet format publishing mechanism, cross-protocol semantic fusion at the edge, closed-loop command verification and execution, and cluster collaborative control, it addresses the data fusion method for each piece of equipment in the equipment cluster operation mode. Based on the operation mode of the equipment cluster, it makes a comprehensive judgment and provides an optimized multi-source information fusion method. This enables refined adaptation to complex construction scenarios, optimized fusion of multi-source data, and efficient and safe control. It supports multi-level edge computing layers and realizes distributed closed-loop control and comprehensive optimization of multiple equipment in large-scale construction sites. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a method for controlling construction equipment at a complex construction site, as described in one embodiment of the present invention. Detailed Implementation

[0027] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, further illustrates the control system and method for complex construction site equipment of the present invention. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0028] refer to Figure 1 This invention provides a detailed description of the control method for complex construction equipment at construction sites.

[0029] Please continue to refer to this. Figure 1 A complex construction site equipment control system includes an equipment basic state semantic model, multi-level edge computing devices, and an integrated management and control platform. The equipment basic state semantic model includes a tower crane model, a construction elevator model, and a horizontal transportation equipment model. Each piece of equipment is equipped with a single edge device, and a main edge computing device is deployed at the floor cluster end. The integrated management and control platform performs comprehensive discrimination and generates group commands.

[0030] Specifically, this system addresses the heterogeneous protocol differences, dynamic scene changes, and cluster collaboration requirements faced by construction equipment (such as tower cranes, construction elevators, horizontal transport equipment, and pumping equipment) in complex construction sites. It proposes a highly dynamic, adaptable, real-time, and reliable control scheme. The system: constructs a basic state semantic model for each type of equipment, covering all sensing and control command data; the integrated management platform dynamically extracts and publishes one or more data packet formats (supporting multiple versions) from the basic model based on the construction scenario, region, and equipment type, enabling fine-tuning of sensing information and command formats; edge computing devices are deployed at the equipment or cluster ends, responsible for handling multiple heterogeneous protocols (custom byte streams, Modbus-RTU, CAN, etc.). This invention employs dynamic mapping parsing (using protocols such as 2.0B, RTSP, and NMEA0183), 10ms-level time window alignment, redundancy removal, priority filtering, variable-length / differential compression, and unified protocol encapsulation. Equipment reports empty command sensing data packets, and the integrated management platform performs multi-source information fusion and judgment based on single-machine or cluster aggregated data to generate optimized control commands. These commands are then compressed from recent sensing data and distributed in the same data packet format. The equipment compares the latest sensing data with the compressed sensing data, performs consistency, security threshold, and scenario adaptability verification, and decides whether to execute the command. If not executed, a problem expression is reported, forming a security closed loop. In cluster scenarios, it supports multi-level edge computing device aggregation and command distribution, enabling efficient collaboration among heterogeneous clusters such as tower cranes, construction elevators, and horizontal transport equipment (e.g., collision avoidance and path optimization for material transport within floors). This invention effectively solves the problems of high overhead, semantic fragmentation, bandwidth waste, poor real-time performance, and insufficient flexibility of fixed fusion modes in traditional protocols. It significantly improves the intelligence level, transmission efficiency, collaborative capabilities, and security of equipment control in complex construction sites, and is suitable for dynamic and variable scenarios such as high-rise buildings and large-scale infrastructure projects.

[0031] Please continue to refer to this. Figure 1 A method for controlling construction equipment at complex construction sites, comprising the following steps:

[0032] Step S1, Construct the basic state semantic model of the equipment: Construct the state and command data of the construction equipment into a data packet format with multiple combinations, and construct a basic state semantic model for each type of construction equipment.

[0033] Step S2, deploy edge computing devices and integrated management and control platform: deploy edge computing devices at the construction equipment end or equipment cluster end. The edge computing devices are used for dynamic mapping and parsing of multiple heterogeneous protocols, as well as data packet processing, distribution and cluster aggregation; the integrated management and control platform manages the equipment cluster.

[0034] Step S3, Dynamically publish data packet format: The integrated management and control platform extracts the data packet format based on scenario requirements and dynamically publishes one or more data packet formats to the edge computing devices of the construction equipment or equipment cluster by region and / or equipment type; the data packet format is extracted from the basic state semantic model of the equipment;

[0035] Step S4, Equipment-side perception data collection and fusion reporting: The equipment-side drives perception collection based on the received data packet format, automatically interprets various heterogeneous raw protocol data, forms target semantic combinations, processes them, encapsulates the processed target semantic combinations according to a unified protocol format, and reports the perception data packets to the integrated management and control platform. Among them, the instruction data is empty.

[0036] Step S5, Central side command generation and issuance: The integrated control platform receives the sensing data packet (which may include cluster summary data), fills it into the corresponding data packet format, and compresses the sensing data before encapsulating it together and sending it back to the equipment in the same data packet format;

[0037] Step S6, Equipment-side command verification and execution feedback: After receiving the data packet of the filling command, the equipment compares the latest sensing information with the compressed sensing data to determine whether to execute the command; if executed, it operates according to the command and repeats step S3 to report the new sensing data packet, forming a closed-loop control; if not executed, it generates a new sensing data packet and attaches a command problem expression (such as conflict reason code or description) and reports it. The integrated management and control platform regenerates and adjusts the command based on the feedback and issues it.

[0038] Step S7, Cluster Equipment Collaborative Processing: In a cluster scenario with multiple construction equipment, the cluster edge computing device aggregates and merges the data packets of the subordinate equipment and then executes step S3 to report to the integrated management and control platform; the integrated management and control platform generates a comprehensive optimization control command based on the aggregated data and executes step S4 to send it to the cluster edge computing device; the cluster edge computing device then distributes the comprehensive command to each subordinate construction equipment to achieve cluster closed-loop collaborative control.

[0039] In this embodiment, more preferably, the basic state semantic model in step S1 includes all data of equipment perception and control commands.

[0040] In this embodiment, more preferably, the data includes the status and instruction data of the construction equipment, which are constructed into a data packet in the form of multiple combinations. The multiple combinations include equipment ID, timestamp, environmental perception related data, vibration related data, motion related data, and control instruction related data. The construction equipment includes tower cranes, construction elevators, transportation equipment, and pumping equipment.

[0041] In this embodiment, more preferably, the automatic data interpretation and processing in step S4 includes: data verification, interception of key instruction frames and key data frames, and embedding the key instruction frames and key data frames into the unified multi-element combination in bytecode form to form a target semantic combination; then, time alignment and unified timestamp processing are performed on the multi-protocol data packets with a fixed time window, redundant fields are removed, priority judgment and filtering are performed based on relevant data, and compression encoding is performed using variable length encoding and differential encoding. If the same data packet is found, only the number is transmitted.

[0042] In this embodiment, more preferably, the various heterogeneous original protocols include, but are not limited to: custom byte stream protocols (such as RS485 serial port private frame format), Modbus-RTU protocol, CAN 2.0B protocol, RTSP over UDP video stream protocol and NMEA0183 Beidou positioning protocol; the unified protocol format includes: 2-byte frame header, 1-byte version number, 4-byte device ID, 8-byte timestamp, variable-length payload field and 2-byte CRC16 checksum.

[0043] In this embodiment, more preferably, the data packet format in step S3 supports dynamic publishing and multiple versions coexisting. The instruction publishing format and sensing data reporting format can be flexibly adjusted according to the construction scenario (such as collaborative collision avoidance for tower crane groups, material distribution in pumping equipment clusters, etc.) to achieve refined control over different areas, equipment types, and collaboration modes. The purpose of using multiple data packets is to accommodate the equipment control needs of multiple scenarios. For example, tower cranes are used for both ground material transport and intra-floor material transport, requiring different data packet sensing information and data packet transmission frequencies.

[0044] In this embodiment, more preferably, the data packet format in step S4 is extracted from the basic state semantic model of the equipment. That is, by analyzing the construction scenario, equipment tasks and cluster collaboration requirements, the required perception data is extracted to form the data packet format.

[0045] In this embodiment, more preferably, the multi-source information fusion judgment in step S5 is made by the integrated management and control platform based on the perception data of multiple equipment to make a comprehensive judgment and give the optimal control command and data fusion strategy.

[0046] In this embodiment, more preferably, the judgment logic in step S6 includes perception data consistency verification, security threshold check and scenario adaptability verification to ensure the security and reliability of instruction execution.

[0047] Example 1: Single Equipment Control Scenario

[0048] Taking tower cranes as an example, in the scenario of operating inside a foundation pit,

[0049] Step S1: Construct a basic state semantic model, including device ID, timestamp, vibration data (RS485 protocol), motion data (Modbus-RTU), control commands (CAN 2.0B), and environmental perception (NMEA0183 positioning).

[0050] Step S2: Deploy edge computing devices, such as ARM processor-based embedded boards that support RTOS real-time operating systems.

[0051] Step S3: Deploy the integrated management and control platform and use a cloud server to implement management functions.

[0052] In step S4, the integrated management and control platform extracts the data packet format according to the foundation pit scenario, emphasizing vibration and positioning data, and publishes it to the edge devices.

[0053] Step S5: The edge device collects heterogeneous data, performs verification, embedding, time alignment (10ms window), redundancy removal, priority judgment (vibration anomaly priority), compression (variable length + differential coding), encapsulates a unified protocol (frame header 0x55 0xAA, version 0x01, DevID 4B, Timestamp 8B, variable length payload, CRC16), and reports a perception packet (instruction empty).

[0054] Step S6: The control platform analyzes the reported data, integrates and judges (such as vibration + positioning to detect risks), generates instructions (such as deceleration), compresses the sensing data, fills the data packet and sends it out.

[0055] In step S7, the edge device compares the latest sensing and compressed data. If they are consistent and within the safety threshold, it executes (e.g., sends a command via CAN). Otherwise, it reports a problem (e.g., "vibration exceeds threshold" code 0x01) and the platform adjusts the command.

[0056] This embodiment enables real-time safety control of a single tower crane.

[0057] Example 2: Cluster Equipment Collaboration Scenario

[0058] In this embodiment, taking the collaborative material transportation within a high-rise building construction site as an example, a heterogeneous equipment cluster is involved: a tower crane (responsible for vertically lifting materials to designated floors), construction elevators (assisting in the vertical transportation of personnel / materials), and horizontal transportation equipment (such as electric flatbed carts or AGV transport vehicles, responsible for horizontal transport within the floors). In this scenario, the three types of equipment need to work closely together: the tower crane lifts materials to the floor unloading area, the construction elevator simultaneously positions itself to assist in unloading, and the horizontal transportation equipment promptly receives materials and transports them to the construction site to avoid material accumulation, path conflicts, or safety hazards (such as overloading or collisions). The cluster size is assumed to be 1 tower crane, 2 construction elevators, and 4 horizontal transportation equipment.

[0059] Step S1: Build a basic state semantic model for each type of equipment.

[0060] Tower crane model: includes device ID, timestamp, boom position / angle (Modbus-RTU), lifting weight / vibration (RS485 custom protocol), positioning (NMEA0183), and control commands (CAN 2.0B, such as hoisting / slewing).

[0061] Construction elevator model: including floor location, door status, load, speed (Modbus-RTU), vibration / safety door status (custom protocol).

[0062] Horizontal transport equipment model: including path location (BeiDou positioning), speed / direction (Modbus-RTU), load sensor, obstacle detection (RTSP video stream), and control command (CAN).

[0063] Step S2: Deploy multi-level edge computing devices at the cluster end.

[0064] Deploy standalone edge devices (embedded boards, such as ARM processors that support RTOS) on each device.

[0065] Deploy main edge computing devices (high-performance industrial computers, such as the NVIDIA Jetson series) at the floor cluster end to be responsible for aggregating sub-equipment data; if there are multiple floors, regional main edge devices can be further deployed to achieve multi-level hierarchy.

[0066] Step S3: Deploy a comprehensive management and control platform, using a cloud / edge hybrid server to achieve global management and a visual interface.

[0067] Step S4: Dynamically publish cluster-specific data packet format.

[0068] Based on the material transportation scenario within the floor (high-frequency collaboration, emphasizing real-time positioning and load), the integrated management and control platform extracts key fields from the basic models of each piece of equipment: for tower cranes, it extracts the boom position / lifting weight; for construction elevators, it extracts the floor position / load; and for horizontal transportation equipment, it extracts the path position / obstacle detection.

[0069] Add collaboration fields (such as cluster relative positioning, material ID tracking, and estimated arrival time).

[0070] Publish multiple versions of data packets: high-frequency packets (reporting location / velocity every 100ms for real-time collision avoidance) and low-frequency packets (reporting vibration / load every 1s for status monitoring). Support for multiple versions coexisting to adapt to different transportation stages (such as high-frequency positioning during unloading).

[0071] Step S5: Sub-equipment perception data collection, fusion and reporting, and main edge aggregation.

[0072] The sub-equipment edge devices are driven by the release format for data acquisition: parsing heterogeneous protocols (e.g., tower crane CAN control frames, horizontal transport RTSP video frames to extract obstacles), embedding bytecode, 10ms window time alignment, redundancy removal (unified timestamp), priority judgment (obstacle detection takes priority), and compression (differential encoding of location data, historical number reuse of static state).

[0073] The sub-equipment reports the perception packet (instruction empty) to the main edge device.

[0074] The main edge devices aggregate and merge data: calculate group risks (such as the probability of tower crane boom intersecting with horizontal transport paths, and the approach distance of horizontal vehicles when construction elevator doors open), aggregate statistics (total material flow, bottlenecks), and generate cluster-level perception packages for unified reporting to the integrated management and control platform. The fusion process reduces the amount of data uploaded by approximately 60% and lowers bandwidth usage.

[0075] Step S6: Platform comprehensive judgment and group instruction generation.

[0076] The integrated management and control platform receives cluster summary packets and performs multi-source fusion: combining the tower crane's lifting weight, elevator floors, and transport vehicle locations, it determines the optimal path (such as avoiding congested areas) and synchronization timing (such as delaying the tower crane's unloading for 5 seconds after the elevator arrives).

[0077] Generate comprehensive instructions: e.g., tower crane "decelerates and rotates to the unloading area", construction elevator "stops at the designated floor and opens the door", multiple horizontal transport equipment "path planning adjustment, priority receiving vehicle ID001 approaches the unloading area, other vehicles wait".

[0078] Compress recent sensing data, populate it into the cluster data packet format, and send it to the main edge device.

[0079] Step S7: After verification at the main edge, distribute, execute, and provide feedback.

[0080] The main edge receives the instruction packet, first performs overall verification (e.g., checks the group security threshold, such as the total load not exceeding the floor limit), and then distributes it to the sub-equipment.

[0081] The sub-equipment edge compares the latest local perception with the compressed perception sent down (consistency check, such as position deviation <1m, safety threshold not exceeded, scene adaptation such as no temporary obstacles on the floor). If they are consistent, it is executed (e.g., CAN sends motion command); if they conflict (e.g., sudden obstacle), it is not executed and the problem expression is reported (code 0x02 "path blockage", with a summary of the new perception video frame attached).

[0082] Step S8: Multi-level hierarchical distributed optimization.

[0083] If the cluster spans multiple floors, the regional main edge aggregates data from multiple floor main edges to further optimize the global material flow (e.g., prioritizing supply to higher floors).

[0084] Supports dynamic adjustments: If transportation demand changes (such as low frequency at night), the platform will re-release the low-frequency data packet format.

[0085] The above description is merely a description of preferred embodiments of the present invention and is not intended to limit the scope of the present invention in any way. Any changes or modifications made by those skilled in the art based on the above disclosure shall fall within the protection scope of the claims.

Claims

1. A method for controlling construction equipment at complex construction sites, characterized in that, Includes the following steps: Step S1, Construct the basic state semantic model of the equipment: Construct the state and command data of the construction equipment into a data packet format with multiple combinations, and construct a basic state semantic model for each type of construction equipment. Step S2, deploy edge computing devices and integrated management and control platform: deploy edge computing devices at the construction equipment end or equipment cluster end. The edge computing devices are used for dynamic mapping and parsing of multiple heterogeneous protocols, as well as data packet processing, distribution and cluster aggregation. The equipment cluster is managed by a comprehensive control platform; Step S3, Dynamically publish data packet format: The integrated management and control platform extracts the data packet format based on scenario requirements and dynamically publishes one or more data packet formats to the edge computing devices of the construction equipment or equipment cluster by region and / or equipment type; the data packet format is extracted from the basic state semantic model of the equipment; Step S4, Equipment-side perception data collection and fusion reporting: The equipment-side drives perception collection based on the received data packet format, automatically interprets various heterogeneous raw protocol data, forms target semantic combinations, processes them, encapsulates the processed target semantic combinations according to a unified protocol format, and reports the perception data packets to the integrated management and control platform. Among them, the instruction data is empty. Step S5, Central side command generation and issuance: The integrated control platform receives the sensing data packets, fills them into the corresponding data packet format, compresses the sensing data, encapsulates them together, and sends them back to the equipment in the same data packet format. Step S6, Equipment-side command verification and execution feedback: After receiving the data packet of the filling command, the equipment compares the latest sensing information with the compressed sensing data to determine whether to execute the command; if executed, it operates according to the command and repeats step S3 to report the new sensing data packet, forming a closed-loop control; if not executed, it generates a new sensing data packet and reports the command problem expression, and the integrated management and control platform regenerates and adjusts the command based on the feedback and issues it. Step S7, Cluster Equipment Collaborative Processing: In a cluster scenario with multiple construction equipment, the cluster edge computing device aggregates and merges the data packets of the subordinate equipment and then executes step S3 to report to the integrated management and control platform; the integrated management and control platform generates a comprehensive optimization control command based on the aggregated data and executes step S4 to send it to the cluster edge computing device; the cluster edge computing device then distributes the comprehensive command to each subordinate construction equipment to achieve cluster closed-loop collaborative control.

2. The method according to claim 1, characterized in that, The basic state semantic model in step S1 includes all data related to equipment perception and control commands.

3. The method according to claim 2, characterized in that, The data includes the status and instruction data of the construction equipment, which are constructed into a multi-element combination data packet. The multi-element combination includes equipment ID, timestamp, environmental perception related data, vibration related data, motion related data, and control instruction related data. The construction equipment includes tower cranes, construction elevators, transportation equipment, and pumping equipment.

4. The method according to claim 1, characterized in that, The automatic data interpretation and processing in step S4 includes: data verification, interception of key instruction frames and key data frames, and embedding the key instruction frames and key data frames into the unified multi-element combination in bytecode form to form the target semantic combination; then, time alignment and unified timestamp processing are performed on the multi-protocol data packets with a fixed time window, redundant fields are removed, priority judgment and filtering are performed based on relevant data, and compression encoding is performed using variable length encoding and differential encoding. If the same data packet is found, only the number is transmitted.

5. The method according to claim 1, characterized in that, The various heterogeneous original protocols include, but are not limited to: custom byte stream protocol, Modbus-RTU protocol, CAN 2.0B protocol, RTSP over UDP video stream protocol and NMEA0183 Beidou positioning protocol; the unified protocol format includes: 2-byte frame header, 1-byte version number, 4-byte device ID, 8-byte timestamp, variable-length payload field and 2-byte CRC16 checksum.

6. The method according to claim 1, characterized in that, In step S3, the data packet format supports dynamic publishing and multiple versions coexisting. The instruction publishing format and the perception data reporting format can be flexibly adjusted according to the construction scenario to achieve refined control over different areas, equipment types, and collaboration modes.

7. The method according to claim 1, characterized in that, In step S4, the data packet format is extracted from the basic state semantic model of the equipment. That is, by analyzing the construction scenario, equipment tasks and cluster collaboration requirements, the required perception data is extracted to form the data packet format.

8. The method according to claim 1, characterized in that, In step S5, the multi-source information fusion judgment is made by the integrated control platform based on the perception data of multiple equipment to make a comprehensive judgment and give the optimal control command and data fusion strategy.

9. The method according to claim 1, characterized in that, The judgment logic in step S6 includes perceptual data consistency verification, security threshold check, and scenario adaptability verification to ensure the security and reliability of instruction execution.

10. A control system for construction equipment at a complex construction site, characterized in that, The method for controlling construction equipment at complex construction sites according to any one of claims 1 to 9 includes: an equipment basic state semantic model, multi-level edge computing devices, and an integrated management and control platform. The equipment basic state semantic model includes a tower crane model, a construction elevator model, and a horizontal transportation equipment model. A single edge device is deployed on each piece of equipment, and a main edge computing device is deployed at the floor cluster end. The integrated management and control platform performs comprehensive discrimination and generates group instructions.

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