Railway temporary facility construction progress optimization method and system based on artificial intelligence

Abstract

The invention relates to the technical field of construction management, and discloses a railway temporary facility construction progress optimization method and system based on artificial intelligence. According to the invention, through heterogeneous data fusion and conflict graph modeling, accurate quantification of construction contradictions is realized; a multi-agent auction mechanism is combined with a virtual credit system, so that the fairness and efficiency of resource allocation are improved; the self-healing mechanism of the spiking neural network and quantum genetic optimization break through the limitation of the traditional algorithm from local and global levels respectively. In the system level, an edge computing node and a distributed consensus engine guarantee real-time decision reliability, a dynamic digital sand table realizes visual prediction of risks, and a self-organizing control tower guarantees standard compliance. Practical verification shows that the number of times of resource conflicts is reduced, the early warning accuracy of major risks is higher during emergency response, and the construction efficiency and safety are remarkably improved.

Claims

1. An optimization method for the construction progress of railway temporary facilities based on artificial intelligence, characterized in that, It includes the following steps: Real-time collect the status of construction equipment (hydraulic pressure, GPS trajectory), the operation chain of personnel (workstation switching frequency, tool usage records), environmental interference factors (ground penetrating radar monitoring data, meteorological mutation warning), and material dynamics (UWB-based material positioning error) through heterogeneous data chains; Construct a construction conflict graph: Use a graph convolutional network (GCN) to model the competition relationships between processes (equipment preemption, spatial overlap, labor competition), and quantify construction contradictions as negative weight edges between nodes; Adopt a multi-agent auction algorithm to dynamically allocate resources: Each construction unit acts as an agent, competes for resources through a sealed-bid mechanism, and the bid value is jointly calculated by the construction urgency (inverse weighted by the remaining construction period) and the resource benefit ratio (progress gain per unit resource consumption); Achieve schedule self-healing based on a pulse-coupled neural network (PCNN): When a process delay is detected, automatically trigger the pulse excitation signal of adjacent processes, and achieve elastic reorganization of the process chain through synaptic weight adjustment; Deploy a quantum genetic optimization module: Map the construction schedule problem to qubit encoding, and dynamically adjust the process priority through quantum rotation gates to break through the local optimal trap of traditional genetic algorithms.

2. The optimization method for the construction progress of railway temporary facilities based on artificial intelligence according to claim 1, characterized in that The construction of the construction conflict graph includes: Spatial conflict layer: Extract the three-dimensional coordinates of temporary facilities through the BIM model, and calculate the cross probability of equipment movement paths; Time conflict layer: Based on the improved Hungarian algorithm, match the overlap degree of process time windows; Resource conflict layer: Establish an imbalance index model of supply and demand for shared resources such as concrete mixing plants and lifting equipment.

3. The construction progress optimization method for railway temporary facilities based on artificial intelligence according to claim 1, wherein The multi-agent auction algorithm includes: Virtual credit system: Allocate dynamic credit limits to each construction unit, and the credit value decays exponentially with the historical performance rate; Anti-malicious bidding mechanism: Detect agents with abnormally high bids through the isolation forest algorithm and freeze their bidding rights in this round; Pareto optimality verification module: Use the KKT condition to verify the non-inferior solution attribute of the winning bid plan.

4. The optimization method for the construction progress of railway temporary facilities based on artificial intelligence according to claim 1, wherein, The pulse-coupled neural network includes: Dynamic threshold regulator: Automatically reduce the activation threshold of adjacent processes according to the severity of the delay; Synaptic plasticity rule: Design a weight update equation based on Lyapunov stability theory to ensure the convergence of the reorganized process chain; Pulse propagation inhibition module: Block the cross-process propagation of the chain delay risk through a gated recurrent unit (GRU).

5. The optimization method for the construction progress of railway temporary facilities based on artificial intelligence according to claim 1, characterized in that, The quantum genetic optimization module includes: Quantum chromosome encoding: Encode process priorities, resource allocation plans, and emergency plan selections as superposition state qubits; Dynamic rotation angle strategy: Adaptively adjust the rotation angle of the quantum gate according to the construction environment change rate; Quantum entanglement resource pool: Establish entanglement relationships between key processes to ensure the co-variation of related processes.

6. A construction progress optimization system for railway temporary facilities based on artificial intelligence, characterized in that, Including the artificial intelligence-based railway temporary facility construction schedule optimization method described in any one of claims 1-5, the system specifically includes: Edge computing node cluster: FPGA accelerators deployed at the terminals of construction machinery, running lightweight conflict detection models in real time; Industrial switches supporting the TSN (Time-Sensitive Network) protocol to ensure microsecond-level transmission of control instructions; Distributed consensus engine: Synchronize the state data of multiple agents based on the RAFT algorithm; Verify decision consistency through the Byzantine fault tolerance mechanism; Dynamic digital sand table module: Simulate the construction progress diffusion process based on the improved cellular automata; Predict the mutation risk of the critical path through the phase space reconstruction technology; Self-organizing control tower: Embedded expert system, storing the construction specification knowledge graph of the National Railway Administration; Automatically generate an emergency plan template library that complies with the GB / T 51310-2018 standard.

7. An optimization system for the construction progress of railway temporary facilities based on artificial intelligence according to claim 6, characterized in that, The edge computing node cluster includes: Vibration energy harvesting device resistant to electromagnetic interference, providing self-power supply ability for sensors; Six-axis inertial navigation unit based on MEMS, realizing device positioning in a GPS-free environment; Quantum dot spectral sensor for harsh environments, real-time monitoring of the concrete solidification state.

8. The construction progress optimization system for railway temporary facilities based on artificial intelligence according to claim 6, characterized in that The distributed consensus engine adopts: Construction progress credibility evaluation model: Quantify the contradiction degree of the data reported by each agent through information entropy; Contribution distribution mechanism based on the Shapley value, motivating agents to provide high-precision data; Dynamic weight voting algorithm, adjusting the decision weight according to the historical accuracy of the agent.

9. The construction progress optimization system for railway temporary facilities based on artificial intelligence according to claim 6, characterized in that, The dynamic digital sand table module includes: Construction disturbance propagation simulator: Calculate the diffusion speed of the delay risk through the nonlinear dynamics equation; Vulnerability assessment matrix of key facilities: Quantify the failure probability of facilities such as temporary piers and construction access roads; Early warning system for progress deviation based on EWMA (Exponentially Weighted Moving Average).

10. An optimization system for the construction progress of railway temporary facilities based on artificial intelligence according to claim 6, characterized in that, The self-organizing control tower has: Automatic retrieval engine for specification clauses: Map construction problems to corresponding regulatory clauses; Emergency plan simulation test environment based on the Generative Adversarial Network (GAN); Compliance audit tracking module: Record the regulatory compliance proof path of all decisions.

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