Dynamic self-adaptive access method for cluster construction equipment on construction site

Abstract

The invention provides a dynamic self-adaptive access method for cluster construction equipment on a construction site. The method comprises the following steps: 1, constructing a multi-network environment fingerprint map: scanning and collecting signal data in advance to establish a rasterized map; 2, issuing and sharing a fingerprint map: broadcasting to all equipment; 3, predicting the moving trajectory of the cluster equipment: predicting the trajectory in real time and constructing a network access path; 4, cluster equipment task QoS grading and bandwidth demand modeling: establishing a task priority and demand model; 5, evaluating the working state of the equipment and judging the switching opportunity: judging the switching feasibility according to the risk quantification; 6, performing dynamic network switching decision and multi-objective optimization: performing decision switching 30 seconds in advance by adopting an improved genetic algorithm and a multi-objective optimization function; and 7, real-time correction and failure response of the fingerprint map: incrementally updating the map, and stopping the machine in advance for waiting in a failure area. According to the method, the connection reliability, the safety and the intelligent operation level of the cluster equipment can be remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for construction sites, and in particular to a dynamic adaptive access method for clustered construction equipment at construction sites. Background Technology

[0002] Complex construction sites (such as high-rise buildings, tunnel projects, deep foundation pit excavation, and viaduct construction) often have varied terrain and dense obstacles, resulting in fragmented wireless network coverage with multiple regions and coexistence of multiple standards. Typical network types include 5G private networks, 4G-LTE public networks, Wi-Fi hotspots or local access points (APs), and IoT private networks (such as LoRa and NB-IoT). When clustered construction equipment (such as excavators, automated guided vehicles, and construction elevators) moves between different construction areas, they need to access available networks in real time to ensure reliable transmission of sensing data, remote control commands, and collaborative data between equipment.

[0003] However, existing technologies often employ single-network binding or simple threshold-triggered switching strategies, which cannot effectively address the dynamic complexity of the construction site network environment. This leads to frequent issues such as unstable connections, data transmission interruptions, or disconnections during equipment operation, severely impacting construction efficiency and safety. The construction site network environment itself is relatively stable but has dynamically supplied resources; network coverage, bandwidth availability, and stability change in real time with factors such as time, weather, and equipment density. Traditional fixed access strategies are ill-suited to this multi-network heterogeneous and dynamically interfering environment, failing to enable intelligent network selection and seamless switching for equipment. To ensure intelligent operation control of clustered construction equipment, especially the reliable transmission of safety-critical data, existing network access technologies are insufficient to meet practical needs.

[0004] Therefore, how to provide a dynamic adaptive access method for clustered construction equipment on 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 dynamic adaptive access method for construction equipment clusters at construction sites. By constructing and sharing a high-resolution multi-network environment fingerprint map, real-time trajectory prediction, task QoS classification, working status risk assessment, genetic algorithm multi-objective optimization decision-making, and incremental updates of the fingerprint map, predictive adaptive seamless access of equipment in fragmented multi-network environments is achieved.

[0006] The technical solution of the present invention for a dynamic adaptive access method for clustered construction equipment at a construction site is as follows:

[0007] A method for dynamic adaptive access of clustered construction equipment at a construction site includes the following steps:

[0008] Step S1, Construct a multi-network environment fingerprint map: In advance, a map scanning device is used to travel along the construction path to collect the device's location and multi-network signal data in real time;

[0009] Step S2, Fingerprint Map Publishing and Sharing: The completed multi-network environment fingerprint map is published to all construction equipment in the cluster via platform broadcast;

[0010] Step S3, Prediction of the operating trajectory of the cluster equipment: Based on the autonomous navigation function of the cluster equipment, predict the subsequent operating trajectory in real time;

[0011] Step S4, Cluster Equipment Task QoS Classification and Bandwidth Demand Modeling: Classify the equipment transmission tasks according to QoS and establish priority indicators, latency requirements and bandwidth demand models.

[0012] Step S5, Equipment Working Status Assessment and Switching Timing Judgment: Based on the actual operation and working status of the equipment, dynamically assess the feasibility of network switching. Prioritize switching in low-risk operation or idle state, and restrict or prohibit switching in high-risk operation or walking state to ensure that switching does not affect the safety of critical operations; assess whether switching is allowed based on the network resource usage before and after switching.

[0013] Step S6, Dynamic Network Handover Decision and Multi-Objective Optimization: Based on trajectory prediction, current signal quality, and real-time service queue, an improved genetic algorithm is used to establish a decision model. The multi-objective optimization function is used as the fitness function to evaluate bandwidth, latency, handover cost, and resource load, and output the optimal network, handover timing, and bandwidth allocation ratio.

[0014] Step S7, Real-time correction and failure handling of fingerprint map: The signal quality is reported in real time as the equipment is running, and the fingerprint map of the multi-network environment is updated incrementally, only updating the changed areas; when the fingerprint map of the multi-network environment shows that the equipment is about to enter a network node failure or signal coverage blind spot, the equipment stops running in advance, sends the location and pending tasks to the platform, and continues to run after the network is restored.

[0015] Furthermore, in step S1, the fingerprint map records the signal strength, delay, packet loss rate, and bandwidth availability parameters of each grid point; the fingerprint map is stored using a rasterized data structure, supports incremental updates, and only records the difference data of the changing grids to reduce transmission overhead.

[0016] Furthermore, in step S3, the subsequent running trajectory is predicted in real time, and the trajectory is combined with the fingerprint map to determine the network areas and network resource conditions it passes through, and to construct a network access path.

[0017] Furthermore, in step S4, the QoS classification of the cluster equipment task includes emergency stop command, vibration command, travel command, video data, and radar data.

[0018] Furthermore, step S4 also includes setting a quantization label for each transmission task, including the highest priority emergency stop instruction, the delay requirement emergency stop instruction ≤ 50ms, and the bandwidth requirement video data ≥ 10Mbps.

[0019] Furthermore, in step S5, the equipment working status assessment includes a quantitative judgment of operational risk, including four types: idle state, traveling state, low-risk operational state, and high-risk operational state. Among them, the idle state allows network switching, while the traveling state prohibits network switching. The low-risk operational state refers to a state where the equipment does not require data transmission or only transmits a small amount of non-critical data, and network switching is allowed in the low-risk operational state. The high-risk operational state refers to a state where all data is transmitted or critical data is transmitted, and network switching is prohibited in the high-risk operational state.

[0020] Further, in step S6, the inputs to the improved genetic algorithm include current signal quality, equipment trajectory prediction, real-time service queue length, resource utilization before and after handover, and working status evaluation results. The multi-objective optimization function adopted is F = w1 R + w2 P - w3 D - w4L, where F is the comprehensive optimization objective, R is connection reliability, P is service priority satisfaction, D is handover delay, L is network load, and w1 to w4 are weights. Among them, R, connection reliability, is calculated based on the matching degree of current signal quality and trajectory prediction; the higher the value, the more stable the network connection and the more reliable the signal coverage. P, service priority satisfaction, is calculated based on whether the network meets the QoS requirements of each task in the service queue after handover; the higher the value, the more guaranteed the high-priority tasks are. D, handover delay, includes the interruption time of hard handover, data retransmission overhead, and computational resource consumption; the higher the value, the higher the network handover cost. L, network load, includes the current network load and resource occupancy after access; the higher the value, the more limited the network resources are.

[0021] Furthermore, the timing of the handover includes initiating the handover 30 seconds before the equipment is about to enter the network area to be switched.

[0022] Furthermore, the incremental update in step S7 specifically includes: the equipment reporting the grid signal data corresponding to the current location in real time and comparing it with the original data in the fingerprint map. When the signal strength deviation exceeds 10%, the delay deviation exceeds 20%, or the packet loss rate deviation exceeds 5%, an incremental update is triggered; only the difference data packet of the changed grid is generated and broadcast. The difference data packet includes the grid ID, the type of the changed parameter, and the difference between the old and new values. The receiving equipment only updates the corresponding part of the local fingerprint map, thereby reducing communication overhead and update latency.

[0023] Compared with existing technologies, the dynamic adaptive access method for construction site cluster equipment of the present invention has the following advantages:

[0024] The present invention discloses a dynamic adaptive access method for clustered construction equipment at construction sites. This method can make decisions on the optimal network and switching timing about 30 seconds before the equipment enters the signal attenuation zone, dynamically control the switching feasibility based on the operational risk, avoid interruption of critical data transmission under high-risk conditions, maintain real-time accuracy of the map through incremental updates, and stop the equipment in advance to avoid risks in network failure zones, thereby significantly improving the connection reliability, security and intelligent operation level of the clustered equipment. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a method for dynamic adaptive access of clustered construction equipment at a construction site, according to one embodiment of the present invention. Detailed Implementation

[0026] The following detailed description of the dynamic adaptive access method for construction site cluster equipment of the present invention, in conjunction with the accompanying drawings and specific embodiments, provides further insight into the advantages and features of the present invention. It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions, intended only to facilitate and clarify the illustration of the embodiments of the present invention.

[0027] Example 1

[0028] refer to Figure 1 This invention provides a detailed description of the dynamic adaptive access method for construction site cluster equipment.

[0029] Please continue to refer to this. Figure 1 A dynamic adaptive access system for clustered construction equipment at a construction site 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 end. The comprehensive management and control platform performs comprehensive discrimination and generates group instructions.

[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 dynamic adaptive access method for clustered construction equipment at a construction site includes the following steps:

[0032] A method for dynamic adaptive access of clustered construction equipment at a construction site includes the following steps:

[0033] Step S1, Construct a multi-network environment fingerprint map: A map scanning device is used to travel along the construction path to collect real-time data on the device's location (GPS) and multi-network signal data, including RSSI. 5G RSSI WiFi RSSI LoRa Based on latency and packet loss rate, a multi-network environment fingerprint map with a resolution of 1m×1m×0.5m is established;

[0034] Step S2, Fingerprint Map Publishing and Sharing: The completed multi-network environment fingerprint map is published to all construction equipment in the cluster via platform broadcast;

[0035] Step S3, Prediction of the operating trajectory of the cluster equipment: Based on the autonomous navigation function of the cluster equipment, predict the subsequent operating trajectory in real time;

[0036] Step S4, Cluster Equipment Task QoS Classification and Bandwidth Demand Modeling: Classify the equipment transmission tasks according to QoS and establish priority indicators, latency requirements and bandwidth demand models.

[0037] Step S5, Equipment Working Status Assessment and Switching Timing Judgment: Based on the actual operation and working status of the equipment, dynamically assess the feasibility of network switching. Prioritize switching in low-risk operation or idle state, and restrict or prohibit switching in high-risk operation or walking state to ensure that switching does not affect the safety of critical operations; assess whether switching is allowed based on the network resource usage before and after switching.

[0038] Step S6, Dynamic Network Handover Decision and Multi-Objective Optimization: Based on trajectory prediction, current signal quality, and real-time service queue, an improved genetic algorithm is used to establish a decision model. The multi-objective optimization function is used as the fitness function to evaluate bandwidth, latency, handover cost, and resource load, and output the optimal network, handover timing, and bandwidth allocation ratio. The handover is initiated 30 seconds before the equipment is about to enter the network area to be handed over.

[0039] Step S7, Real-time correction and failure handling of fingerprint map: The signal quality is reported in real time as the equipment is running, and the fingerprint map of the multi-network environment is updated incrementally, only updating the changed areas; when the fingerprint map of the multi-network environment shows that the equipment is about to enter a network node failure or signal coverage blind spot, the equipment stops running in advance, sends the location and pending tasks to the platform, and continues to run after the network is restored.

[0040] In this embodiment, more preferably, in step S1, the fingerprint map records the signal strength (RSSI), latency, packet loss rate, and bandwidth availability parameters for each grid point; the fingerprint map is stored using a rasterized data structure, supports incremental updates, and only records the difference data of the changing grids to reduce transmission overhead.

[0041] In this embodiment, more preferably, in step S3, the subsequent running trajectory is predicted in real time, the trajectory is combined with the fingerprint map, the network areas and network resources it passes through are determined, and a network access path is constructed.

[0042] In this embodiment, more preferably, in step S4, the QoS classification of the cluster equipment task includes emergency stop command, vibration command, travel command, video data, and radar data.

[0043] In this embodiment, more preferably, step S4 further includes setting a quantization label for each transmission task, including the highest priority emergency stop instruction, the delay requirement emergency stop instruction ≤ 50ms, and the bandwidth requirement video data ≥ 10Mbps.

[0044] In this embodiment, more preferably, in step S5, the equipment working status assessment includes a quantitative judgment of operational risk, including four types: idle state, traveling state, low-risk operational state, and high-risk operational state. Among them, the idle state allows network switching, while the traveling state prohibits network switching. The low-risk operational state refers to the state where the equipment does not require data transmission or only a small amount of non-critical data transmission (such as waiting for loading), and network switching is allowed in the low-risk operational state. The high-risk operational state refers to the state where all data transmission or critical data transmission is performed (such as excavation or hoisting), and network switching is prohibited in the high-risk operational state.

[0045] In this embodiment, more preferably, in step S6, the inputs to the improved genetic algorithm include the current signal quality, equipment trajectory prediction, real-time service queue length, resource utilization before and after handover, and working status evaluation results; the multi-objective optimization function adopted is F = w1 R + w2 P - w3 D - w4L, where F is the comprehensive optimization objective, R is the connection reliability, P is the service priority satisfaction, D is the handover delay, L is the network load, and w1 to w4 are weights; where R is the connection reliability calculated based on the current signal quality and trajectory prediction matching degree, the higher the value, the more stable the network connection and the more reliable the signal coverage; P is the service priority satisfaction calculated based on whether the network meets the QoS requirements of each task in the service queue after handover, the higher the value, the more guaranteed the high-priority tasks are; D is the handover delay including the hard handover interruption time, data retransmission overhead, and computational resource consumption, the higher the value, the higher the network handover cost; L is the network load including the current network load and the resource occupancy after access, the higher the value, the more limited the network resources are.

[0046] In this embodiment, more preferably, the incremental update in step S7 specifically includes: the equipment reporting the grid signal data corresponding to the current location in real time and comparing it with the original data in the fingerprint map. When the signal strength deviation exceeds 10%, the delay deviation exceeds 20%, or the packet loss rate deviation exceeds 5%, the incremental update is triggered; only the difference data packet of the changed grid is generated and broadcast. The difference data packet includes the grid ID, the type of the changed parameter, and the difference between the old and new values. The receiving equipment only updates the corresponding part of the local fingerprint map, thereby reducing communication overhead and update latency.

[0047] Example 2

[0048] Taking a deep foundation pit construction project as an example, the following is a detailed explanation:

[0049] The on-site network includes a 5G private network, Wi-Fi hotspots, and a LoRa private network. Multiple excavators and automated guided vehicles are operating in clusters, requiring emergency stop command latency of ≤50ms and video transmission of ≥10Mbps.

[0050] 1. Fingerprint map construction and publishing: Use a map scanning device to travel along the pit path, collect network signal parameters, build a 1m×1m×0.5m fingerprint map, and then broadcast it to all equipment.

[0051] 2. Trajectory Prediction and QoS Classification: The subsequent trajectory of the transport vehicle is predicted to pass through Wi-Fi blind spots; Task Classification: Emergency stop instructions have the highest priority, and video data has high bandwidth requirements.

[0052] 3. Work status assessment: When the vehicle is waiting to load soil (low-risk operation), switching is allowed; when the excavator is digging (high-risk operation), switching is prohibited.

[0053] 4. Switching Decision and Optimization: Before the vehicle enters the blind spot, the genetic algorithm takes the trajectory, signal, queue and working status as input, calculates the multi-objective function F, and outputs the switch to LoRa network 30 seconds in advance and allocates an appropriate bandwidth ratio.

[0054] 5. Map Correction and Failure Response: If a vehicle detects that a Wi-Fi hotspot has failed during operation, it reports the data, triggering an incremental update and broadcasting only the difference packet; if another vehicle is predicted to enter the failed area, it stops in advance, reports its location and task to the platform, and waits for recovery before continuing.

[0055] Using this method, the equipment disconnection rate is reduced to near zero, the success rate of critical command transmission is over 99.9%, and construction safety and efficiency are significantly improved.

[0056] 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 dynamic adaptive access method for clustered construction equipment at a construction site, characterized in that, Includes the following steps: Step S1, Construct a multi-network environment fingerprint map: Use equipment to collect equipment location and multi-network signal data in real time along the construction path; Step S2, Fingerprint Map Publishing and Sharing: The completed multi-network environment fingerprint map is published to all construction equipment in the cluster via platform broadcast; Step S3, Prediction of the operating trajectory of the cluster equipment: Based on the autonomous navigation function of the cluster equipment, predict the subsequent operating trajectory in real time; Step S4, Cluster Equipment Task QoS Classification and Bandwidth Demand Modeling: Classify the equipment transmission tasks according to QoS and establish priority indicators, latency requirements and bandwidth demand models. Step S5, Equipment Working Status Assessment and Switching Timing Determination: Based on the actual operation and working status of the equipment, dynamically assess the feasibility of network switching; assess whether switching is allowed based on network resource usage before and after switching. Step S6, Dynamic Network Handover Decision and Multi-Objective Optimization: Based on trajectory prediction, current signal quality, and real-time service queue, an improved genetic algorithm is used to establish a decision model. The multi-objective optimization function is used as the fitness function to evaluate bandwidth, latency, handover cost, and resource load, and output the optimal network, handover timing, and bandwidth allocation ratio. Step S7, Real-time correction and failure handling of fingerprint map: Report signal quality in real time as the equipment is running, and perform incremental updates to the fingerprint map in multi-network environments, updating only the changed areas; When the fingerprint map of a multi-network environment indicates that the equipment is about to enter a network node failure or signal coverage blind spot, the equipment will stop operating in advance, send its location and pending tasks to the platform, and wait for the network to be restored before resuming operation.

2. The method according to claim 1, characterized in that, In step S1, the fingerprint map records the signal strength, delay, packet loss rate, and bandwidth availability parameters of each grid point. The fingerprint map is stored using a rasterized data structure, supports incremental updates, and only records the difference data of the changing grids to reduce transmission overhead.

3. The method according to claim 2, characterized in that, In step S3, the subsequent running trajectory is predicted in real time, and the trajectory is combined with the fingerprint map to determine the network areas and network resources it passes through, and to construct a network access path.

4. The method according to claim 1, characterized in that, In step S4, the QoS classification of the cluster equipment task includes emergency stop command, vibration command, travel command, video data, and radar data.

5. The method according to claim 1, characterized in that, Step S4 also includes setting a quantization label for each transmission task, including the highest priority emergency stop instruction, the delay requirement emergency stop instruction ≤ 50ms, and the bandwidth requirement video data ≥ 10Mbps.

6. The method according to claim 1, characterized in that, In step S5, the equipment working status assessment includes a quantitative judgment of operational risk, including four types: idle state, running state, low-risk operational state, and high-risk operational state. Among them, the idle state allows network switching, while the running state prohibits network switching. The low-risk operational state refers to the state where the equipment does not require data transmission or only transmits a small amount of non-critical data, and network switching is allowed in the low-risk operational state. The high-risk operational state refers to the state where all data is transmitted or critical data is transmitted, and network switching is prohibited in the high-risk operational state.

7. The method according to claim 1, characterized in that, In step S6, the inputs to the improved genetic algorithm include current signal quality, equipment trajectory prediction, real-time service queue length, resource utilization before and after handover, and working status evaluation results. The multi-objective optimization function used is F = w1R + w2P - w3D - w4L, where F is the comprehensive optimization objective, R is the connection reliability, and P is the service priority satisfaction. D represents the handover delay, L represents the network load, and w1 to w4 represent the weights.

8. The method according to claim 7, characterized in that, In step S6, R (connection reliability) is calculated based on the current signal quality and trajectory prediction matching degree. The higher the value, the more stable the network connection and the more reliable the signal coverage. P (service priority satisfaction) is calculated based on whether the network meets the QoS requirements of each task in the service queue after handover. The higher the value, the more guaranteed the high-priority tasks can be. D (handover delay) includes the interruption time of hard handover, data retransmission overhead, and computing resource consumption. The higher the value, the higher the network handover cost. L (network load) includes the current network load and resource occupancy after access. The higher the value, the more limited the network resources.

9. The method according to claim 8, characterized in that, The timing of the handover includes initiating the handover 30 seconds before the equipment is about to enter the network area to be switched.

10. The method according to claim 1, characterized in that, The incremental update in step S7 specifically includes: the equipment reporting the grid signal data corresponding to its current location in real time and comparing it with the original data in the fingerprint map. When the signal strength deviation exceeds 10%, the delay deviation exceeds 20%, or the packet loss rate deviation exceeds 5%, an incremental update is triggered; only the difference data packet of the changed grid is generated and broadcast. The difference data packet includes the grid ID, the type of the changed parameter, and the difference between the old and new values. The receiving equipment only updates the corresponding part of its local fingerprint map. This reduces communication overhead and update latency.

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