An artificial intelligence-based high-rise building construction path dynamic planning method

Abstract

The application discloses a high-rise building construction path dynamic planning method based on artificial intelligence, and comprises the following steps: S1, data acquisition, construction task sequence and initial resource supply relationship graph generation; S2, current resource supply state, construction rhythm change state and work interference factor generation, current actual construction state formation; S3, construction-resource confrontation graph model construction; S4, path search tree structure initialization; S5, path sampling and evaluation are carried out through an improved heuristic Monte Carlo search algorithm, and a plurality of feasible path branches and evaluation scores thereof are output; S6, path branch comparison and dynamic optimization are executed, and a current optimal construction path is obtained; S7, construction step adjustment suggestion and resource allocation adjustment instruction generation; S8, optimal construction path and evaluation score recording, construction path optimization result formation. The application improves the intelligentization, self-adaptation and real-time optimization capability of the high-rise building construction path planning.

Description

Technical Field

[0001] This invention relates to the fields of building construction management and artificial intelligence optimization technology, and in particular to a dynamic planning method for construction paths of high-rise buildings based on artificial intelligence. Background Technology

[0002] With the acceleration of urbanization and the continuous improvement of building industrialization, high-rise building construction faces multiple challenges, including complex construction tasks, dynamic resource allocation, and frequent environmental disturbances. How to achieve efficient and reliable dynamic planning of construction paths has become a key issue in construction organization design. Existing construction path planning methods are mostly based on static task scheduling, critical path analysis, or linear resource scheduling models, primarily relying on fixed process logic and empirical parameters for scheduling optimization. However, in practical applications, they generally suffer from the following problems:

[0003] Current methods typically fail to effectively integrate the complex dependencies between construction tasks and the dynamic matching of resource supply and demand, resulting in a lack of adaptability in path decision-making. Limited environmental awareness at construction sites hinders the identification and modeling of potential operational interference factors in sensor data and log information, making it difficult to dynamically respond to unexpected events and schedule deviations during construction. Traditional optimization methods often employ single heuristics or static scheduling algorithms, failing to fully simulate the evolution of construction states and resource interaction processes, leading to resource conflicts, sequence deviations, and inefficiencies in the generated path solutions during actual execution. Furthermore, insufficient utilization of construction state feedback and a lack of a continuous path solution update mechanism result in unstable construction path optimization effects, making it difficult to achieve intelligent scheduling and control throughout the entire process.

[0004] Therefore, how to provide a dynamic planning method for construction paths of high-rise buildings based on artificial intelligence is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose a dynamic planning method for construction paths of high-rise buildings based on artificial intelligence. It adopts an improved heuristic Monte Carlo search algorithm, integrates a multi-strategy hybrid simulation mechanism, a construction rhythm adaptive path adjustment mechanism, and a construction environment risk dynamic modeling mechanism, and combines construction task sequences, resource supply status, and operational interference factors to simulate and optimize paths. It has the advantages of more adaptable path planning results, strong responsiveness to dynamic construction environments, and more reasonable allocation of construction resources.

[0006] A dynamic planning method for construction paths of high-rise buildings based on artificial intelligence, according to an embodiment of the present invention, includes the following steps:

[0007] S1. Obtain the construction task order and component installation sequence diagram, and generate a diagram showing the relationship between the construction task sequence and the initial resource supply.

[0008] S2. Obtain the current construction progress status data, combine it with the initial resource supply relationship diagram to form the current resource supply status, generate the construction rhythm change status according to the task completion rate of each construction node, and extract the operation interference factors by combining the on-site sensor data and construction logs to generate the current actual construction status.

[0009] S3. Based on the construction task sequence and the current resource supply status, construct a construction-resource confrontation graph model;

[0010] S4. Initialize the path search tree structure based on the construction-resource adversarial graph model and input it into the improved heuristic Monte Carlo search algorithm;

[0011] S5. Using an improved heuristic Monte Carlo search algorithm, a multi-strategy hybrid simulation mechanism is introduced in the simulation phase to perform construction state evolution and resource response simulation. In the selection and backtracking phase, the construction rhythm adaptive path adjustment mechanism and the construction environment risk dynamic modeling mechanism are integrated to dynamically correct the path evaluation value, complete path sampling and evaluation, and output multiple feasible path branches and their corresponding evaluation scores.

[0012] S6. Based on multiple feasible path branches and evaluation scores, combined with the construction-resource confrontation diagram model, construction rhythm change status and operation interference factors, perform path branch comparison and dynamic optimization to obtain the optimal construction path under the current state.

[0013] S7. Compare the optimal construction path with the current actual construction status in terms of node sequence, resource allocation and time window, and output resource allocation adjustment instructions.

[0014] S8. Record the optimal construction path and evaluation score to form the construction path optimization result.

[0015] Optionally, S1 specifically includes:

[0016] S11. Obtain the construction task sheet. Based on the task number, job type, duration and resource requirement type recorded in the construction task sheet, arrange and construct the construction task item table in sequence, and form the initial task arrangement set according to the task number order.

[0017] S12. Obtain the component installation sequence diagram. Read the component number, floor elevation and structural connection direction information of each component from the component installation sequence diagram. Map the component number to the corresponding construction task number and construct the directed connection relationship between tasks according to the connection direction to obtain the construction priority relationship diagram.

[0018] S13. Based on the direction of the directed edges in the construction priority relationship diagram, the initial set of tasks is sorted using the topological sorting algorithm to generate a construction task sequence that conforms to the structural connection logic.

[0019] S14. Statistically analyze the resource requirement types of each task in the construction task item table, arrange them in the order of the construction task sequence to generate a resource requirement sequence, and combine them with the resource numbers in the standard construction resource allocation table provided by the construction unit to establish a supply and demand mapping matrix between task numbers and resource numbers, and construct an initial resource supply relationship diagram.

[0020] Optionally, S2 specifically includes:

[0021] S21. Collect construction logs and on-site sensor data, obtain the current task completion status, resource call records and construction environment monitoring data of each component in the construction task sequence, and generate raw data of construction progress status.

[0022] S22. Based on the component resource allocation relationship recorded in the initial resource supply relationship diagram, and combined with the current resource call value of each component in the original data of construction progress status, calculate the supply ratio of the component-resource correspondence, and construct the current resource supply status matrix. The matrix records the ratio of actual supply to allocation using component number and resource number as indexes.

[0023] S23. Based on the time sequence of the task completion rate of each component in the original data of construction progress status, calculate the task completion rate according to the component number, and use the sliding time window to calculate its first derivative to form a task completion rate change curve. Based on the curve, group and cluster the components to generate the construction rhythm change status.

[0024] S24. Analyze the abrupt change signals in the construction environment monitoring data, and combine the keywords and time records of event interference in the construction log to construct event interference tuples, including interference location, time window and impact type;

[0025] S25. The event interference tuples are associated and labeled according to the component number, and the interference intensity score is obtained by weighted superposition. A set of operation interference factors is generated, and each factor in the set contains the component number, interference type, interference score and corresponding time window information.

[0026] S26. Unify the current resource supply status matrix, construction rhythm change status diagram and operation interference factor set to form the current actual construction status.

[0027] Optionally, S3 specifically includes:

[0028] S31. Obtain the work content, dependent task number, construction priority and required resource type corresponding to each task from the construction task sequence, and number each task as a task node to form a task node set.

[0029] S32. Extract the resource type, number and available quantity of various construction resources from the current resource supply status, establish each type of resource as a resource node, and form a set of resource nodes;

[0030] S33. Based on the type and quantity of resources required by each task node, establish resource connection edges between the task node and the corresponding resource node, and attach the resource requirement quantity as an edge attribute to the connection edge.

[0031] S34. Based on the dependent task number marked for each task in the construction task sequence, establish task connection edges between task nodes to represent the sequential relationship of construction tasks.

[0032] S35. Combine the task node set, resource node set, resource connection edge and task connection edge into a heterogeneous graph structure, and add numerical information of the current resource supply status and construction rhythm change status to the attributes of each node to generate a construction-resource confrontation graph model.

[0033] Optionally, S4 specifically includes:

[0034] S41. Based on the construction-resource adversarial graph model, extract the connection relationship between task nodes in the construction task sequence and available resource nodes in the resource supply status, and initialize the path search tree structure with the current actual construction status as the initial root node.

[0035] S42. Based on the construction priority of the task nodes in the construction task sequence, expand the task status corresponding to the current root node, construct an expandable task node set, and generate corresponding child nodes in the path search tree.

[0036] S43. During the generation of child nodes, the resource matching status of each task node is determined by combining the supply of each resource number in the current resource supply status, and the resource constraint status label is marked for the task nodes that cannot be satisfied.

[0037] S44. Input the generated path search tree structure and its task nodes, resource status labels and time step index information into the improved heuristic Monte Carlo search algorithm.

[0038] Optionally, S5 specifically includes:

[0039] S51. In the improved heuristic Monte Carlo search algorithm, a state-space simulation model based on the current path search tree structure is constructed, and path branch simulation is performed; in each round of simulation, the construction sequence of components, resource consumption behavior and on-site state response process are simulated to generate a path execution sequence.

[0040] S52. In the simulation process, a multi-strategy hybrid simulation mechanism is introduced. The heuristic scheduling strategy based on empirical rules, the local optimization strategy based on reinforcement learning, and the resource intervention strategy based on structural weights are used to dynamically guide the execution sequence of the construction path. The construction feasibility and resource coordination degree are evaluated in parallel among the simulation nodes.

[0041] S53. In the path tree selection and backtracking stage, an adaptive path adjustment mechanism for construction rhythm is integrated. Based on the local rate changes of each construction node in the state of construction rhythm change, the path continuation direction is dynamically adjusted to improve the priority of high-frequency operation areas.

[0042] S54. Synchronous integration of construction environment risk dynamic modeling mechanism: Based on the frequency and duration of each event interference tuple in the set of operation interference factors in the path branch, generate path interference score and superimpose it on the path backtracking expectation value.

[0043] S55. Based on the execution sequence and simulation results of each path branch, calculate its task delay loss, resource conflict penalty and environmental risk score, assign preset weights to each, and then sum them up to obtain the evaluation score.

[0044] S56. Record all path branches and their corresponding evaluation scores.

[0045] Optionally, S6 specifically includes:

[0046] S61. Sort the multiple feasible path branches and their corresponding evaluation scores, and select the top m paths with the highest scores as a candidate path set.

[0047] S62. For each path in the candidate path set, perform path traversal operation in sequence, and map its nodes sequentially to the corresponding components and resource units in the construction-resource confrontation graph model to obtain the mapping result;

[0048] S63. Based on the mapping results, calculate the resource contention conflict rate, construction sequence feasibility and interference event hit probability among components in each path, and combine the construction rhythm change status and operation interference factors to comprehensively score the overall execution stability and execution efficiency of the path.

[0049] S64. Based on the comprehensive scoring results, select the path with the highest score as the optimal construction path in the current state, and output the node sequence, resource allocation sequence and key control period corresponding to the path.

[0050] Optionally, S7 specifically includes:

[0051] S71. Compare the order of construction nodes in the current actual construction state with the order of nodes in the optimal construction path one by one, and identify the position and degree of offset of nodes with inconsistent order.

[0052] S72. Compare the current resource supply status with the resource allocation required for nodes in the optimal construction path, and calculate the resource differences for each node.

[0053] S73. Compare the current construction rhythm changes with the construction time window in the optimal path, and identify the rhythm conflict intervals.

[0054] S74. Based on three indicators—degree of deviation, resource differences, and rhythm conflicts—generate suggestions for adjusting the current construction steps, including the reordering of nodes and the pre-operation instructions for key tasks.

[0055] S75. Based on resource differences and rhythm conflicts, generate resource allocation adjustment instructions, including resource reallocation suggestions and current construction step adjustment suggestions.

[0056] Optionally, S8 specifically includes:

[0057] S81. Encode the optimal construction path together with the construction-resource confrontation graph model state when the path was generated, the construction rhythm change state and the operation interference factor to form a path state snapshot data record.

[0058] S82. Assign a unique index number to the path status snapshot data record, establish a status snapshot index set, and associate and store the optimal construction path, the corresponding evaluation score and the status snapshot index to form a searchable construction path optimization result database.

[0059] S83. When subsequent construction status updates or path replanning requirements occur, call the status snapshot index set to perform a fast search and reuse operation for paths similar to the current status.

[0060] The beneficial effects of this invention are:

[0061] (1) Enhance the dynamic adaptability of path planning: By introducing a construction rhythm adaptive path adjustment mechanism, the speed difference and bottleneck position in the construction progress can be identified in real time, and the path planning results can be dynamically corrected. This effectively avoids congestion and task accumulation in high-frequency construction areas, improves the continuity and stability of path execution, and significantly enhances the adaptability of planning results to actual construction conditions.

[0062] (2) Enhance risk perception capability under complex working conditions: Integrate the dynamic modeling mechanism of construction environment risk, fully consider the spatiotemporal distribution and impact intensity of operation interference factors during the path search process, quantify the interference risk of path branches and incorporate it into the evaluation system, effectively improve the robustness and fault tolerance of path planning, and ensure that a stable and feasible construction path can still be output in complex environments.

[0063] (3) Achieve precise coordination and efficient allocation of construction resources: By constructing a construction-resource conflict graph model, the system integrates the logic of construction tasks and the supply and demand relationship of resources. During the Monte Carlo path search process, the resource allocation scheme is simulated and evaluated. The optimal path is selected through comprehensive score, thereby achieving coordinated optimization of task sequence, resource allocation and time window, and significantly reducing the resource conflict rate and the risk of project delay. Attached Figure Description

[0064] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0065] Figure 1 This is a flowchart of a dynamic planning method for construction paths of high-rise buildings based on artificial intelligence, as proposed in this invention. Detailed Implementation

[0066] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0067] refer to Figure 1 A dynamic planning method for construction paths of high-rise buildings based on artificial intelligence includes the following steps:

[0068] S1. Obtain the construction task order and component installation sequence diagram, and generate a diagram showing the relationship between the construction task sequence and the initial resource supply.

[0069] S2. Obtain the current construction progress status data, combine it with the initial resource supply relationship diagram to form the current resource supply status, generate the construction rhythm change status according to the task completion rate of each construction node, and extract the operation interference factors by combining the on-site sensor data and construction logs to generate the current actual construction status.

[0070] S3. Based on the construction task sequence and the current resource supply status, construct a construction-resource confrontation graph model;

[0071] S4. Initialize the path search tree structure based on the construction-resource adversarial graph model and input it into the improved heuristic Monte Carlo search algorithm;

[0072] S5. Using an improved heuristic Monte Carlo search algorithm, a multi-strategy hybrid simulation mechanism is introduced in the simulation phase to perform construction state evolution and resource response simulation. In the selection and backtracking phase, the construction rhythm adaptive path adjustment mechanism and the construction environment risk dynamic modeling mechanism are integrated to dynamically correct the path evaluation value, complete path sampling and evaluation, and output multiple feasible path branches and their corresponding evaluation scores.

[0073] S6. Based on multiple feasible path branches and evaluation scores, combined with the construction-resource confrontation diagram model, construction rhythm change status and operation interference factors, perform path branch comparison and dynamic optimization to obtain the optimal construction path under the current state.

[0074] S7. Compare the optimal construction path with the current actual construction status in terms of node sequence, resource allocation and time window, and output resource allocation adjustment instructions.

[0075] S8. Record the optimal construction path and evaluation score to form the construction path optimization result.

[0076] In this embodiment, S1 specifically includes:

[0077] S11. Obtain the construction task sheet. Based on the task number, job type, duration and resource requirement type recorded in the construction task sheet, arrange and construct the construction task item table in sequence, and form the initial task arrangement set according to the task number order.

[0078] S12. Obtain the component installation sequence diagram. Read the component number, floor elevation and structural connection direction information of each component from the component installation sequence diagram. Map the component number to the corresponding construction task number and construct the directed connection relationship between tasks according to the connection direction to obtain the construction priority relationship diagram.

[0079] S13. Based on the direction of the directed edges in the construction priority relationship diagram, the initial set of tasks is sorted using the topological sorting algorithm to generate a construction task sequence that conforms to the structural connection logic.

[0080] S14. Statistically analyze the resource requirement types of each task in the construction task item table, arrange them in the order of the construction task sequence to generate a resource requirement sequence, and combine them with the resource numbers in the standard construction resource allocation table provided by the construction unit to establish a supply and demand mapping matrix between task numbers and resource numbers, and construct an initial resource supply relationship diagram.

[0081] This implementation constructs a construction task sequence and an initial resource supply relationship diagram. During the generation process, topological sorting is used to ensure the sequential logic of construction tasks, and a supply-demand mapping is established between tasks and standard resource configurations. This approach improves the structural integrity of construction task modeling, enabling precise expression of resource flow relationships between different trades, floors, and components. This provides accurate input for subsequent construction path simulation and resource scheduling, effectively improving the executability of overall path planning, the matching degree of resource allocation, and the coordination of construction rhythm.

[0082] In this embodiment, S2 specifically includes:

[0083] S21. Collect construction logs and on-site sensor data, obtain the current task completion status, resource call records and construction environment monitoring data of each component in the construction task sequence, and generate raw data of construction progress status.

[0084] S22. Based on the component resource allocation relationship recorded in the initial resource supply relationship diagram, and combined with the current resource call value of each component in the original data of construction progress status, calculate the supply ratio of the component-resource correspondence, and construct the current resource supply status matrix. The matrix records the ratio of actual supply to allocation using component number and resource number as indexes.

[0085] S23. Based on the time sequence of the task completion rate of each component in the original data of construction progress status, calculate the task completion rate according to the component number, and use the sliding time window to calculate its first derivative to form a task completion rate change curve. Based on the curve, group and cluster the components to generate the construction rhythm change status.

[0086] S24. Analyze the abrupt change signals in the construction environment monitoring data, and combine the keywords and time records of event interference in the construction log to construct event interference tuples, including interference location, time window and impact type;

[0087] S25. The event interference tuples are associated and labeled according to the component number, and the interference intensity score is obtained by weighted superposition. A set of operation interference factors is generated, and each factor in the set contains the component number, interference type, interference score and corresponding time window information.

[0088] S26. Unify the current resource supply status matrix, construction rhythm change status diagram and operation interference factor set to form the current actual construction status.

[0089] This implementation comprehensively depicts the dynamic evolution of the construction site by constructing a set of current resource supply status, construction rhythm changes, and operational interference factors. Regarding resource supply, it accurately reflects the actual deviations in resource allocation during construction. In interference modeling, it constructs event interference tuples and generates interference intensity scores, effectively capturing the impact of construction anomalies on path planning. This method significantly improves the precision and real-time performance of construction status modeling, providing a highly robust environmental awareness foundation for subsequent path optimization.

[0090] In this embodiment, S3 specifically includes:

[0091] S31. Obtain the work content, dependent task number, construction priority and required resource type corresponding to each task from the construction task sequence, and number each task as a task node to form a task node set.

[0092] S32. Extract the resource type, number and available quantity of various construction resources from the current resource supply status, establish each type of resource as a resource node, and form a set of resource nodes;

[0093] S33. Based on the type and quantity of resources required by each task node, establish resource connection edges between the task node and the corresponding resource node, and attach the resource requirement quantity as an edge attribute to the connection edge.

[0094] S34. Based on the dependent task number marked for each task in the construction task sequence, establish task connection edges between task nodes to represent the sequential relationship of construction tasks.

[0095] S35. Combine the task node set, resource node set, resource connection edge and task connection edge into a heterogeneous graph structure, and add numerical information of the current resource supply status and construction rhythm change status to the attributes of each node to generate a construction-resource confrontation graph model.

[0096] This implementation generates a construction-resource conflict graph model by structurally integrating the construction task sequence with the current resource supply status. This model embeds information on resource supply status and construction rhythm changes into node attributes, enabling it to dynamically express the construction process and resource allocation. Compared to traditional construction flowcharts or resource layout diagrams, this heterogeneous graph model can simultaneously describe task dependencies and resource conflict structures, significantly improving the path planning algorithm's ability to identify complex resource constraints and construction priority relationships.

[0097] In this embodiment, S4 specifically includes:

[0098] S41. Based on the construction-resource adversarial graph model, extract the connection relationship between task nodes in the construction task sequence and available resource nodes in the resource supply status, and initialize the path search tree structure with the current actual construction status as the initial root node.

[0099] S42. Based on the construction priority of the task nodes in the construction task sequence, expand the task status corresponding to the current root node, construct an expandable task node set, and generate corresponding child nodes in the path search tree.

[0100] S43. During the generation of child nodes, the resource matching status of each task node is determined by combining the supply of each resource number in the current resource supply status, and the resource constraint status label is marked for the task nodes that cannot be satisfied.

[0101] S44. Input the generated path search tree structure and its task nodes, resource status labels and time step index information into the improved heuristic Monte Carlo search algorithm.

[0102] This implementation introduces resource constraint status labels during the generation of a multi-level path search tree structure, enabling dynamic annotation of resource matching. The constructed path search tree structure, along with task nodes, resource status, and time step indexes, is input into the improved heuristic Monte Carlo search algorithm. This significantly enhances the algorithm's ability to express the rationality of task scheduling and resource coordination during the initialization phase, laying a high-quality data structure foundation for subsequent construction path simulation and evaluation, and improving the accuracy and feasibility of path optimization results.

[0103] In this embodiment, S5 specifically includes:

[0104] S51. In the improved heuristic Monte Carlo search algorithm, a state-space simulation model based on the current path search tree structure is constructed, and path branch simulation is performed; in each round of simulation, the construction sequence of components, resource consumption behavior and on-site state response process are simulated to generate a path execution sequence.

[0105] S52. In the simulation process, a multi-strategy hybrid simulation mechanism is introduced. The heuristic scheduling strategy based on empirical rules, the local optimization strategy based on reinforcement learning, and the resource intervention strategy based on structural weights are used to dynamically guide the execution sequence of the construction path. The construction feasibility and resource coordination degree are evaluated in parallel among the simulation nodes.

[0106] S53. In the path tree selection and backtracking stage, an adaptive path adjustment mechanism for construction rhythm is integrated. Based on the local rate changes of each construction node in the state of construction rhythm change, the path continuation direction is dynamically adjusted to improve the priority of high-frequency operation areas.

[0107] S54. Synchronous integration of construction environment risk dynamic modeling mechanism: Based on the frequency and duration of each event interference tuple in the set of operation interference factors in the path branch, generate path interference score and superimpose it on the path backtracking expectation value.

[0108] S55. Based on the execution sequence and simulation results of each path branch, calculate its task delay loss, resource conflict penalty and environmental risk score, assign preset weights to each, and then sum them up to obtain the evaluation score.

[0109] S56. Record all path branches and their corresponding evaluation scores.

[0110] This implementation integrates a multi-strategy hybrid simulation mechanism with two types of adaptive mechanisms to dynamically correct path evaluation values ​​during path search, optimizing the path selection process. The multi-strategy simulation mechanism can simultaneously execute heuristic scheduling, reinforcement learning guidance, and structural weight intervention, improving the comprehensiveness of construction path simulation. The construction rhythm adaptive mechanism adjusts node priorities based on local task rate changes, ensuring path continuity. The environmental risk modeling mechanism generates path interference scores through operational interference factors, making path evaluation more adaptable to the site. This approach significantly improves the accuracy and robustness of construction path planning, providing a more reliable decision-making basis for dynamic construction scheduling.

[0111] In this embodiment, S6 specifically includes:

[0112] S61. Sort the multiple feasible path branches and their corresponding evaluation scores, and select the top m paths with the highest scores as a candidate path set.

[0113] S62. For each path in the candidate path set, perform path traversal operation in sequence, and map its nodes sequentially to the corresponding components and resource units in the construction-resource confrontation graph model to obtain the mapping result;

[0114] S63. Based on the mapping results, calculate the resource contention conflict rate, construction sequence feasibility and interference event hit probability among components in each path, and combine the construction rhythm change status and operation interference factors to comprehensively score the overall execution stability and execution efficiency of the path.

[0115] S64. Based on the comprehensive scoring results, select the path with the highest score as the optimal construction path in the current state, and output the node sequence, resource allocation sequence and key control period corresponding to the path.

[0116] This implementation method dynamically selects the optimal construction path under the current condition by combining a construction-resource conflict diagram model, the changing state of construction rhythm, and operational interference factors. This approach comprehensively considers resource conflict rate, construction sequence rationality, and environmental interference probability to achieve a comprehensive evaluation of path stability and execution efficiency. It effectively avoids resource waste or process conflicts caused by improper path selection, thereby improving the overall efficiency and reliability of high-rise building construction.

[0117] In this embodiment, S7 specifically includes:

[0118] S71. Compare the order of construction nodes in the current actual construction state with the order of nodes in the optimal construction path one by one, and identify the position and degree of offset of nodes with inconsistent order.

[0119] S72. Compare the current resource supply status with the resource allocation required for nodes in the optimal construction path, and calculate the resource differences for each node.

[0120] S73. Compare the current construction rhythm changes with the construction time window in the optimal path, and identify the rhythm conflict intervals.

[0121] S74. Based on three indicators—degree of deviation, resource differences, and rhythm conflicts—generate suggestions for adjusting the current construction steps, including the reordering of nodes and the pre-operation instructions for key tasks.

[0122] S75. Based on resource differences and rhythm conflicts, generate resource allocation adjustment instructions, including resource reallocation suggestions and current construction step adjustment suggestions.

[0123] This implementation method compares the optimal construction path with the current actual construction status item by item in terms of node sequence, resource allocation, and construction time window. It identifies offset nodes, resource discrepancies, and rhythm conflict sections, and generates resource allocation adjustment instructions accordingly. This approach can calibrate deviations in the construction execution process in real time, promptly adjust the sequence of key tasks and resource allocation plans, ensure a high degree of consistency between the construction path and the plan, effectively improve the stability, continuity, and execution efficiency of the construction process, and reduce the risk of resource waste and delays.

[0124] In this embodiment, S8 specifically includes:

[0125] S81. Encode the optimal construction path together with the construction-resource confrontation graph model state when the path was generated, the construction rhythm change state and the operation interference factor to form a path state snapshot data record.

[0126] S82. Assign a unique index number to the path status snapshot data record, establish a status snapshot index set, and associate and store the optimal construction path, the corresponding evaluation score and the status snapshot index to form a searchable construction path optimization result database.

[0127] S83. When subsequent construction status updates or path replanning requirements occur, call the status snapshot index set to perform a fast search and reuse operation for paths similar to the current status.

[0128] This implementation method generates path status snapshots and establishes an index set to achieve structured storage and efficient management of the optimal construction path and its evaluation status. This approach supports rapid searching and reuse of similar paths during subsequent construction, avoiding redundant calculations and shortening planning response time. Compared to traditional re-evaluation strategies, it significantly improves the system's intelligent scheduling efficiency and resource adaptability in complex construction environments, enhancing the real-time performance and stability of the path planning process.

[0129] Example 1:

[0130] To verify the feasibility of this invention in practice, it was applied to a large-scale urban complex project. The construction unit planned to complete the main structure construction of a 45-story high-rise office building within 20 months. The building structure is complex, with numerous components and high resource allocation requirements. Traditional construction path planning methods mainly rely on the experience of construction personnel to formulate scheduling plans, which is difficult to maintain consistent construction rhythm and resource utilization efficiency in a dynamic construction environment. Therefore, the construction unit decided to introduce the artificial intelligence-based dynamic planning method for high-rise building construction paths proposed in this invention to comprehensively improve construction efficiency and intelligence.

[0131] This project first inputs the construction task sheet and component installation sequence diagram into the system. By parsing the task number, work type, resource type, and connection logic, a construction task sequence and an initial resource supply relationship diagram are generated. Based on this, the topological dependencies between construction procedures are constructed, and a resource supply and demand mapping matrix is ​​formed by combining it with a standard construction resource allocation table. During construction, displacement monitoring instruments, vibration sensors, temperature and humidity sensors, and an RFID material identification system deployed on-site collect real-time construction progress status data and resource call records. Simultaneously, the project management system accesses the construction log to record information such as interference events, schedule delays, and material anomalies.

[0132] During the daily scheduling cycle, the system automatically generates the current resource supply status using the aforementioned data, and generates the construction rhythm change status based on changes in the component task completion rate. Simultaneously, it extracts a set of operational interference factors through an event interference identification algorithm to form the current actual construction status. Subsequently, the system constructs a construction-resource adversarial graph model based on the construction task sequence and the current supply status, integrating task logical relationships, resource distribution, and interference weight information to provide basic graph data support for the initialization of the path search tree structure.

[0133] The path search tree structure built based on the construction-resource adversarial graph model is input into an improved heuristic Monte Carlo search algorithm. In the path simulation phase, a heuristic scheduling strategy based on empirical rules, a reinforcement learning-guided strategy, and a resource intervention priority strategy are introduced to form a multi-strategy hybrid simulation mechanism. In the selection and backtracking phase, an adaptive path adjustment mechanism based on construction rhythm and a dynamic modeling mechanism for construction environment risks are integrated to achieve refined correction of path evaluation and dynamic decision-making for the optimal branch. Finally, multiple feasible path branches and their corresponding comprehensive evaluation scores are output.

[0134] The system selects the top 10 routes by score as a candidate path set, performs mapping analysis and stability assessment, and finally outputs the optimal construction path. This path is then compared with the current actual construction status to generate suggestions for adjusting construction steps and resource allocation instructions, forming a structured path optimization result. The following are statistical comparisons of traditional methods and the method of this invention under several key indicators during actual construction:

[0135] Table 1 Comparison of Construction Path Planning Performance

[0136]

[0137] As can be seen from the comparison table above, the method of the present invention has achieved significant improvements in several core construction scheduling indicators. It particularly demonstrates a high degree of advantage in resource conflict handling, interference event avoidance, and path execution efficiency. Taking the core floors (floors 18 to 21) of a certain construction unit as an example, in the traditional path scheme, the average waiting time for node resource allocation is 41 minutes, with a cumulative number of conflicts reaching 8, and the actual construction cycle exceeds expectations by 4 days; while the path recommended by the system of the present invention, under the same conditions, reduces the waiting time to 19 minutes, reduces the number of conflicts to 1, and completes the construction cycle 2 days ahead of schedule.

[0138] More importantly, the construction path optimization result database constructed by the method of this invention can be quickly reused in subsequent construction stages, reducing the calculation time for path replanning and improving the response speed of path adjustment. When material supply anomalies occur in subsequent stages of the project, the system calls historical state snapshot data, realizing rapid matching and scheduling updates of paths in similar states, saving approximately 58% of path generation time.

[0139] In summary, the application of this invention in practical engineering demonstrates that it can effectively support intelligent path planning under complex construction processes and dynamic construction conditions in high-rise buildings; by introducing multi-strategy simulation, rhythm adjustment, and risk modeling mechanisms, it significantly improves the accuracy and execution stability of path planning; the system possesses good real-time performance, scalability, and reusability, providing a feasible technical path for building information technology and intelligent construction, and has broad prospects for promotion and application.

[0140] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An artificial intelligence-based high-rise building construction path dynamic planning method, characterized by, Includes the following steps: S1. Obtain the construction task order and component installation sequence diagram, and generate a diagram showing the relationship between the construction task sequence and the initial resource supply. S2. Obtain the current construction progress status data, combine it with the initial resource supply relationship diagram to form the current resource supply status, generate the construction rhythm change status according to the task completion rate of each construction node, and extract the operation interference factors by combining the on-site sensor data and construction logs to generate the current actual construction status. S3. Based on the construction task sequence and the current resource supply status, construct a construction-resource confrontation graph model; S4. Initialize the path search tree structure based on the construction-resource adversarial graph model and input it into the improved heuristic Monte Carlo search algorithm; S5. Using an improved heuristic Monte Carlo search algorithm, a multi-strategy hybrid simulation mechanism is introduced in the simulation phase to perform construction state evolution and resource response simulation. In the selection and backtracking phase, the construction rhythm adaptive path adjustment mechanism and the construction environment risk dynamic modeling mechanism are integrated to dynamically correct the path evaluation value, complete path sampling and evaluation, and output multiple feasible path branches and their corresponding evaluation scores. S6. Based on multiple feasible path branches and evaluation scores, combined with the construction-resource confrontation diagram model, construction rhythm change status and operation interference factors, perform path branch comparison and dynamic optimization to obtain the optimal construction path under the current state. S7. Compare the optimal construction path with the current actual construction status in terms of node sequence, resource allocation and time window, and output resource allocation adjustment instructions. S8. Record the optimal construction path and evaluation score to form the construction path optimization result.

2. The high-rise building construction path dynamic planning method based on artificial intelligence according to claim 1, characterized in that, S1 specifically includes: S11. Obtain the construction task sheet. Based on the task number, job type, duration and resource requirement type recorded in the construction task sheet, arrange and construct the construction task item table in sequence, and form the initial task arrangement set according to the task number order. S12. Obtain the component installation sequence diagram. Read the component number, floor elevation and structural connection direction information of each component from the component installation sequence diagram. Map the component number to the corresponding construction task number and construct the directed connection relationship between tasks according to the connection direction to obtain the construction priority relationship diagram. S13. Based on the direction of the directed edges in the construction priority relationship diagram, the initial set of tasks is sorted using the topological sorting algorithm to generate a construction task sequence that conforms to the structural connection logic. S14. Statistically analyze the resource requirement types of each task in the construction task item table, arrange them in the order of the construction task sequence to generate a resource requirement sequence, and combine them with the resource numbers in the standard construction resource allocation table provided by the construction unit to establish a supply and demand mapping matrix between task numbers and resource numbers, and construct an initial resource supply relationship diagram.

3. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, S2 specifically includes: S21. Collect construction logs and on-site sensor data, obtain the current task completion status, resource call records and construction environment monitoring data of each component in the construction task sequence, and generate raw data of construction progress status. S22. Based on the component resource allocation relationship recorded in the initial resource supply relationship diagram, and combined with the current resource call value of each component in the original data of construction progress status, calculate the supply ratio of the component-resource correspondence, and construct the current resource supply status matrix. The matrix records the ratio of actual supply to allocation using component number and resource number as indexes. S23. Based on the time sequence of the task completion rate of each component in the original data of construction progress status, calculate the task completion rate according to the component number, and use the sliding time window to calculate its first derivative to form a task completion rate change curve. Based on the curve, group and cluster the components to generate the construction rhythm change status. S24. Analyze the abrupt change signals in the construction environment monitoring data, and combine the keywords and time records of event interference in the construction log to construct event interference tuples, including interference location, time window and impact type; S25. The event interference tuples are associated and labeled according to the component number, and the interference intensity score is obtained by weighted superposition. A set of operation interference factors is generated, and each factor in the set contains the component number, interference type, interference score and corresponding time window information. S26. Unify the current resource supply status matrix, construction rhythm change status diagram and operation interference factor set to form the current actual construction status.

4. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, S3 specifically includes: S31. Obtain the work content, dependent task number, construction priority and required resource type corresponding to each task from the construction task sequence, and number each task as a task node to form a task node set. S32. Extract the resource type, number and available quantity of various construction resources from the current resource supply status, establish each type of resource as a resource node, and form a set of resource nodes; S33. Based on the type and quantity of resources required by each task node, establish resource connection edges between the task node and the corresponding resource node, and attach the resource requirement quantity as an edge attribute to the connection edge. S34. Based on the dependent task number marked for each task in the construction task sequence, establish task connection edges between task nodes to represent the sequential relationship of construction tasks. S35. Combine the task node set, resource node set, resource connection edge and task connection edge into a heterogeneous graph structure, and add numerical information of the current resource supply status and construction rhythm change status to the attributes of each node to generate a construction-resource confrontation graph model.

5. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, S4 specifically includes: S41. Based on the construction-resource adversarial graph model, extract the connection relationship between task nodes in the construction task sequence and available resource nodes in the resource supply status, and initialize the path search tree structure with the current actual construction status as the initial root node. S42. Based on the construction priority of the task nodes in the construction task sequence, expand the task status corresponding to the current root node, construct an expandable task node set, and generate corresponding child nodes in the path search tree. S43. During the generation of child nodes, the resource matching status of each task node is determined by combining the supply of each resource number in the current resource supply status, and the resource constraint status label is marked for the task nodes that cannot be satisfied. S44. Input the generated path search tree structure and its task nodes, resource status labels and time step index information into the improved heuristic Monte Carlo search algorithm.

6. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, S5 specifically includes: S51. In the improved heuristic Monte Carlo search algorithm, a state-space simulation model based on the current path search tree structure is constructed, and path branch simulation is performed; in each round of simulation, the construction sequence of components, resource consumption behavior and on-site state response process are simulated to generate a path execution sequence. S52. In the simulation process, a multi-strategy hybrid simulation mechanism is introduced. The heuristic scheduling strategy based on empirical rules, the local optimization strategy based on reinforcement learning, and the resource intervention strategy based on structural weights are used to dynamically guide the execution sequence of the construction path. The construction feasibility and resource coordination degree are evaluated in parallel among the simulation nodes. S53. In the path tree selection and backtracking stage, an adaptive path adjustment mechanism for construction rhythm is integrated. Based on the local rate changes of each construction node in the state of construction rhythm change, the path continuation direction is dynamically adjusted to improve the priority of high-frequency operation areas. S54. Synchronous integration of construction environment risk dynamic modeling mechanism: Based on the frequency and duration of each event interference tuple in the set of operation interference factors in the path branch, generate path interference score and superimpose it on the path backtracking expectation value. S55. Based on the execution sequence and simulation results of each path branch, calculate its task delay loss, resource conflict penalty and environmental risk score, assign preset weights to each, and then sum them up to obtain the evaluation score. S56. Record all path branches and their corresponding evaluation scores.

7. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, S6 specifically includes: S61. Sort the multiple feasible path branches and their corresponding evaluation scores, and select the top m paths with the highest scores as a candidate path set. S62. For each path in the candidate path set, perform path traversal operation in sequence, and map its nodes sequentially to the corresponding components and resource units in the construction-resource confrontation graph model to obtain the mapping result; S63. Based on the mapping results, calculate the resource contention conflict rate, construction sequence feasibility and interference event hit probability among components in each path, and combine the construction rhythm change status and operation interference factors to comprehensively score the overall execution stability and execution efficiency of the path. S64. Based on the comprehensive scoring results, select the path with the highest score as the optimal construction path in the current state, and output the node sequence, resource allocation sequence and key control period corresponding to the path.

8. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, Specifically, S7 includes: S71. Compare the order of construction nodes in the current actual construction state with the order of nodes in the optimal construction path one by one, and identify the position and degree of offset of nodes with inconsistent order. S72. Compare the current resource supply status with the resource allocation required for nodes in the optimal construction path, and calculate the resource differences for each node. S73. Compare the current construction rhythm changes with the construction time window in the optimal path, and identify the rhythm conflict intervals. S74. Based on three indicators—degree of deviation, resource differences, and rhythm conflicts—generate suggestions for adjusting the current construction steps, including the reordering of nodes and the pre-operation instructions for key tasks. S75. Based on resource differences and rhythm conflicts, generate resource allocation adjustment instructions, including resource reallocation suggestions and current construction step adjustment suggestions.

9. The method for dynamic planning of construction paths for high-rise buildings based on artificial intelligence according to claim 1, characterized in that, S8 specifically includes: S81. Encode the optimal construction path together with the construction-resource confrontation graph model state when the path was generated, the construction rhythm change state and the operation interference factor to form a path state snapshot data record. S82. Assign a unique index number to the path status snapshot data record, establish a status snapshot index set, and associate and store the optimal construction path, the corresponding evaluation score and the status snapshot index to form a searchable construction path optimization result database. S83. When subsequent construction status updates or path replanning requirements occur, call the status snapshot index set to perform a fast search and reuse operation for paths similar to the current status.

Images